, 张淼中国科学院遥感与数字地球研究所 中国科学院数字地球重点实验室 遥感科学国家重点实验室,北京 100101
Remote sensing: Observations to data products
WUBingfang, ZHANGMiao收稿日期:2017-01-10
修回日期:2017-08-23
网络出版日期:2017-11-20
版权声明:2017《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
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1 引言
遥感通过探测电磁波谱、重力或电磁场扰动,在不直接接触物体的条件下对物体进行观测[1]。遥感作为一种观测手段,与其他观测方法的相同点是为了获取有价值的数据产品,均需要对观测获取的信号/样品进行处理;不同点在于遥感不接触物体便能够实现对物体的观测,观测方法更加灵活;以像元为观测单元的信息获取方式是遥感观测与传统观测方法最大的不同点[2]。长期以来,地学、生态学等学科通过在有限的、分散的点上要素观测推算宏观的、区域的要素变化状况[3-8],无形中增加了客观认识的不确定性。遥感观测通过全覆盖观测,获取细至厘米级、粗至千米级的长期、持续性的观测数据,极大地克服了以点带面的观测弊端,为全球变化研究、地球系统科学研究等提供了独特的观测手段[9]。任何观测方法,最初获得的信号、数据或样品都需通过有效地分析处理后才能得到有意义的数据产品。基于站点的水文气象数据需要经过空间插值转换得到流域降水量等信息[3, 5],通过水量平衡方程推算流域出口断面的径流量[10];实地采集的水、大气、土壤、农产品等样品,需要通过实验仪器分析解析采样点水质、大气质量、土壤属性、农作物品质等信息[7-8];森林蓄积量、树龄测定则需通过量测胸径、树高、钻取树芯后获得[11]。
遥感观测方法也不例外,最初获得的电磁波信号,需要经过处理和分析后才能转换为可直接应用的数据产品,这一过程中辐射定标、大气纠正、几何纠正只是得到遥感观测数据的预处理过程,是生成遥感数据产品的基础,而更重要的是从遥感观测数据生成有价值的数据产品的方法;如植被结构参数[12-13]、生理生化参数[14-22]、河流湖泊水质[23-24]、水循环与水资源管理[25-28]、地物类型[29-31]、植被生长状况[32-35]、生物多样性[36-38]等,与之相关的数据产品生成方法,部分简单,部分又极其复杂,相同的处理方法常因原始遥感观测数据的不同、研究区域的转变、专家知识的差异而产生截然不同的数据产品,以作物种植面积遥感监测为例,不同分辨率遥感产品,即使采用相同方法估算的作物种植面积受尺度影响也可能存在较大差异;植被叶面积遥感估算的方法多种多样,但都只能适用于特定区域与特定环境。遥感从观测数据到数据产品的不确定性极大地妨碍了遥感走出象牙塔。
本文通过系统回顾现有的从遥感观测数据中获得有价值的数据产品的方法,总结分析现有的目标识别和参数提取两大类处理方法及各自的优势,阐述各类方法的局限性及其原因,重点指出遥感数据产品生成方法未来的发展方向,希望促进遥感从观测数据到数据产品的结构化与科学性。
2 目标识别
对遥感影像进行解译和判读进而识别地物类型,提取土地覆盖和土地利用类型、识别地物目标并开展动态变化监测,以及云识别、灾害损毁监测、海表异常探测等均属于目标识别范畴[39-41]。最初的基于遥感观测数据的目标识别是通过目视判读的方式实现的,其首先应用在军事要塞、电厂、机场等敏感目标的识别。目视判读因其直观易懂一直沿用至今,受限于遥感数据质量、监测系统的自动化水平等诸多因素,国家级农情遥感监测系统在开展农作物种植区提取时,仍部分依赖目视判读的方法;中国土地资源数据[42]、第2次全国土地调查工作同样采用人机交互目视判读的方法[43]。但目视解译需要大量的人工投入,并受解译人员知识经验等主观因素的影响,存在效率低、精度与质量难以控制、解译经验要求高等缺点。随着遥感技术的迅速发展,全球卫星遥感数据总量已达艾字节（EB） 级[44],目视判读早已无法充分发挥海量遥感数据在目标识别中的作用。
利用计算机技术实现目标的自动判别与分类,已成为遥感技术与应用研究的重 点[45-47],包括参数化分类器、非参数化分类器在内的多样化分类器被广泛地用于土地利用分类[29, 48-52]、不同植被类型识别[30, 53-60]、关键目标识别[61-63]等多个应用领域。遥感数据源的丰富也为目标识别能力的提高提供了保障。高光谱遥感观测获取的光谱信息在反映地物波谱特征方面能力突出,能够甄别地物特殊的光谱特征,使得高光谱图像中相似目标的探测成为可能[64]。微波遥感因其全天候的观测能力,在多云雨区开展目标识别的能力突出,广泛地应用于水稻种植区提取、洪泛区识别[56, 65-67]等。多时相、光学与SAR数据等多源遥感协同观测因充分结合不同时间获取的遥感数据以及不同数据源自身的优势,目标识别的精度有所提升[45, 56, 65]。
然而,遥感目标识别的精度并没有质的飞跃。2000年全球有代表性的1 km分辨率土地覆盖数据集中的IGBP DISCover、GLC 2000、MODIS土地覆被数据产品的总体精度分别为66.9%、68.6%和78.3%[68],ESA利用2003-2012年间的中分辨率光谱成像仪（MERIS）数据生成的2000年、2005年、2010年3个时段的全球土地覆盖数据集,空间分辨率提高到300 m,水体要素产品的空间分辨率提高到150 m,总体分类精度为73%[69-71];而利用2006年之后的Landsat系列卫星生成的全球30 m分辨率土地覆盖数据集（FROM-GLC）的总体精度为71.5%[49];利用“像素分类—对象提取—知识检核”方案生成的2000年、2010年全球30 m分辨率土地覆盖数据集（GlobalLand 30）[50-51, 72],总体精度达到80%[50];采用面向对象分类技术以及变化检测方法生成的中国1990年、2000年、2005年、2010年4个年度的30 m分辨率土地覆盖数据集（ChinaCover）[48],通过分类后人工修正将总体精度提高到86%[29],勉强达到实际应用要求的85%的分类识别精度目标[73]。
表面上,目标识别的对象因同物异谱及同谱异物导致识别的难度大、精度低。如针叶林与针阔混交林、小麦与大麦、黄豆和绿豆等,同时识别精度又受到如训练样本差异、复杂地形状况、非均一性地表信息、数据预处理质量、分类方法差异、人为主观干预等诸多因素影响[68],且不同目标的识别精度因区域与监测时段的差异而截然不同[29, 48, 50-51, 68-71],目标识别在小区域能够取得较高的精度[59-60],但在其他区域的适应性和拓展性往往不足[60, 73]。数据源对目标识别的影响同样引起了广泛关注;基于光学遥感数据的目标识别精度受到天气条件的显著影响,高光谱数据波段数量大,数据冗余,维数灾难、Hughes现象等问题对目标识别与精细分类精度的影响不容忽视[65, 74-75],对监督分类的样本量要求更高;微波数据分类中斑点噪声现象为准确识别目标、精细分类带来困难[56, 64]。但导致目标识别精度受限的根源在于当前的目标识别方法没有扬长避短,海量多时相、多源遥感数据蕴含的具有生态学、地理学、农学意义的丰富信息未得到有效挖掘,多数目标识别方法达不到输入输出数据标准化的要求。
3 参数提取
参数提取是利用遥感提取地球表面目标的物理、地学、化学、生态学等状态参数的过程。参数提取方法可划分为经验/半经验模型和物理模型两种;遥感指数最初提出是作为一种突出不同地物间差异性的中间产品,但经过长时间发展,因其简单易用,一些遥感指数已经作为表征地表参数的数据产品被直接使用。本节分别对遥感指数、经验/半经验模型和物理模型三种方法进行论述。3.1 遥感指数
地物波谱特征的独特性为遥感指数的构建提供了物理基础。利用不同地物的波谱特征建立特定的遥感指数能够定量探测特殊地物的特征,目前已发展出包括植被指数、水体指数、土壤水分指数、云检测指数、大气污染指数等一系列不同类型的遥感指数。当前,仅植被指数已多达40余种[76-80],这些指数多利用红光波段、近红外波段、红边波段为主的不同波段组合,或者复合指数组合计算获得,早期发展出的植被指数没有考虑大气影响、土壤亮度和土壤颜色的影响以及土壤、植被间的相互作用,导致诸如NDVI等植被指数受植被冠层背景影响较为显著,调整土壤亮度的植被指数、红边植被指数综合了相关波段的光谱信号,在一定程度上增强了植被信号,弱化了非植被信号[77, 80-81]。
面向特定应用提出的遥感指数很多。利用可见光和热红外遥感观测资料建立的遥感指数在干旱监测中发挥了重要作用[81-82];基于微波遥感的干旱指数[83-84]克服了光学遥感观测受云雨影响的缺陷。利用高光谱遥感数据建立的全谱段植被指数（VIUPD）模型与传感器无关,能够更准确的反映植被的细微变化[65, 85];针对不同病虫害胁迫的作物光谱特征,建立的区分特定病虫害的新型光谱指数,包括健康指数（Health Index, HI）、白粉病指数（Powdery Mildew Index, PMI）、黄锈病指数（Yellow Rust Index, YRI）和蚜虫指数（Aphids Index, AI）等[86]。利用植被覆盖度修正植被指数获得的植被茂盛程度的纯化植被指数,能够更准确的表征草地地上生物量的高低[87];类似的植被指数修改方法也被用于修正年度间耕地种植状况、灌溉强度差异导致的作物长势异常信号,实现耕地种植状况归一化的作物长势遥感定量监测,其结果能够更准确的反映作物长势真实状况[35, 88]。植被生产力指数（VPI）基于NDVI数据在历史同期NDVI直方分布中所处位置构建,用以评价植被生产力水平[89]。利用磁力指数可以实现高精度的磁场强度和方向的直接测量[90]。此外,基于高光谱遥感的岩矿指数[91]、水体组分指数[65]也得到广泛应用。
受益于遥感指数计算方式简单和标准,遥感指数产品的生成以及应用较其他遥感数据产品有先天优势,已经成为运行化系统中应用最为广泛的数据产品。全球农情遥感速报系统（CropWatch）提出了多个具有特定指示意义的农情指标,如利用生长季内的植被指数峰值与历史同期植被指数峰值的最大值,发展出生长季最佳植被状况指数（VCIx）,用于评价作物生长季总体长势,能够有效去除物候偏移对长势监测的影响[33],该方法适用于不同传感器数据,不因数据源的不同而产生认知上的差异。农业胁迫指数（Agriculture Stress Index, ASI）用于全球不同生长季异常植被生长情况以及可能发生的旱情监测与预警[92]。基于遥感指数的遥感数据产品为天气预报的业务化运行提供了丰富的数据产品信息,能够准确指示云微物理特性、扬尘、热带气旋等[83, 93]。
不同形式的遥感指数虽已得到广泛应用,但仍面临一定的问题,如大部分遥感指数在构建时缺乏合理的物理解释,导致其指示的生理意义解释不清。NDVI作为最常用的植被指数之一,常被用于作物长势监测[32-35],但相同的NDVI值的指示意义并不唯一,既可以代表作物生长茂盛程度或植被健康未受病虫害侵扰,也可以代表植被的强壮程度。在植被覆盖度高的状况下,NDVI等植被指数出现的饱和现象进一步增加了用户使用植被指数时的困惑。基于不同时空分辨率卫星数据获取的植被指数间存在着非线性关系,导致多源数据的协同使用面临困难。受大气条件、观测条件、地形、定标水平等以及遥感传感器信号获取的不确定性因素的影响,导致不同遥感观测源获取的遥感指数缺乏一致性和可对比性[94],无法形成一致性较高的长时间序列遥感指数[95],进而影响到遥感指数在长时序分析中（如全球变化领域）的可信度[9]。
3.2 经验/半经验模型
基于地面观测/实验数据,应用统计方法分析遥感参量与地面观测数据（物体状态的物理、生理、生化参数）的统计关系,通过建立经验/半经验模型来获得地物参数也是常见的参数提取方法之一。自遥感诞生之日起,经验/半经验模型就被广泛的用于植被覆盖度、植被叶面积指数（LAI）[96-98]、植被光合有效辐射吸收比率（FAPAR）[99]、植被地上生物量[21-22, 79, 100-104]、森林高度[105-107]、植被凋落物生物量/覆盖度[20, 79]、冠层叶绿素浓度[108]、冠层/土壤水分含量[109-113]、氮磷钾等养分浓度[114-115]、土壤水分[116-117]等陆表物理化学参量以及海洋表层参数（海洋表层叶绿素含量、近海泥沙含量和黄色物质浓度等）的估算;最新推动的生物量卫星（BIOMASS）、冰云和地面高度二号卫星（ICESAT-2）等仍建议采用经验/半经验模型实现森林生物量、森林树高等参量的提取[103-104, 107]。经验/半经验模型还被广泛的用于农作物单产遥感估算[32-33, 118]、地表实际蒸散发遥感估算[119]、降水量估算、森林蓄积量清查、病虫害遥感监测[85]、湖泊水量变化监测[120]等领域,为农业、生态、水利、环境科学等传统学科提供了更为便捷的数据获取途径。
经验/半经验模型简单、参数少、运算效率高等优点促成了其在参数提取中得到广泛应用,但同时也存在诸多不确定性。如受地表状况、大气环境、植被冠层结构、遥感数据质量、传感器光谱响应函数等因素的影响[121-123],遥感指数与地表生理生化参量间的关系存在线性关系、指数关系、对数关系、幂函数关系或不同关系的阶段性变化[97-98, 121-122, 124-126],地表双向反射因子（受太阳高度角、观测方位角影响）以及叶倾角分布等冠层结构信息对经验/半经验模型的影响无法有效规避[127]。不同参量的提取可能会基于同种遥感信息或指数,如基于NDVI建立的经验模型提取的植被覆盖度（fCover）、LAI、FAPAR、生物量、作物产量等数据产品相互之间具有高度的相关性和信息冗余性,而终端用户在使用多种数据产品时往往不得而知。
经验/半经验模型的精度不高,外推性差。如基于中低分辨率遥感数据估算海洋泥沙含量、叶绿素含量等参量的精度不足60%,而基于GF-1、Landsat等高分辨率遥感观测数据的估算精度也仅达到87%,基于中低分辨率植被指数的LAI估算模型精度也在60%~80%的范围内波动,而基于高分辨率数据的LAI估算精度接近81%。总体上,经验/半经验模型参数提取的精度多接近80%,说明遥感光谱信息能够解释物理、生理变量80%的变异度,但很难实现精度的进一步突破。特别是一些极端现象很难采用经验/半经验模型实现参量的估算,如作物生长高峰期,灌溉作物的植被指数常处于饱和状态,不同年份间的变化较小,无法用于农作物长势与单产的准确监测[35, 88]。同时,经验/半经验模型对问题的内涵及物理机制解释不清,适用性常受实验条件限制,随着时间和地表状况等的变化,在一个地区建立的统计模型很难用于其他区域。
3.3 物理模型
与经验/半经验模型相对应的参数提取方法是物理模型法,该方法首先要建立描述地表遥感像元物理过程的数学模型,将参数提取转换为数学模型/方程的求解问题。本节中模型反演关注的是由遥感观测数据生成数据产品的反演过程,因此未考虑同化方法。为完成模型方程的数学求解,就需要获取足够的已知信息量,以满足求解方程时独立方程个数等于或大于未知参数的必要条件。然而遥感无论是开展陆地观测、海洋观测还是大气探测,对象都是复杂开放的巨系统,构建的参数方程中,未知量几乎是无穷的,遥感观测的有限性、自相关性以及地表状况的复杂多变导致遥感获取的信息量无法满足求解方程的数据需求,因此方程求解的问题本质上是“病态”的,是“无定解”的数学模型,无法采用“最小二乘法”进行迭代求解[128],只能被迫采用特定的方法和策略以解决“病态”方程问题（反演）,大体可概括成两类：一是通过引入新的知识源-先验知识,增加求解方程所要求的信息量,保证方程求解结果的稳定和可靠[128],一定程度上解决了观测信息量不足的问题[2, 126, 129],提高参数提取的效果[128];二是利用迭代等算法优化方程求解方法和策略[126],对物理模型进行简化[130-131],并对数据空间和参数空间进行分割,分阶段开展参数反演[132-133]。高光谱数据因光谱分辨率显著提高,也是增加反演可用数据的途径,多时相、多角度、多源高分辨率数据的引入同样能够提升模型方程求解的成功率[91, 98, 134-136]。
引入先验知识的物理模型已经广泛的应用于植被冠层结构、LAI、FAPAR、叶绿素含量等参数的反演[2, 128, 137-138],丰富而有效的先验知识有助于识别出模型中的有限个关键参数,并基于先验知识设定其他非关键参数为某一常量或阈值范围。但先验知识的引入必然导致方程求解结果受先验知识的影响,研究表明不同的先验知识（模型方程初始条件）会造成方程求解结果的显著差异,以LAI为例,1%的观测数据变化导致LAI反演结果变化幅度高达75%[137]。同时大范围获取准确的先验知识又面临巨大挑战。目前,SMOS卫星计划利用微波辐射传输模型求解亮度温度及土壤湿度[139],并通过引入分辨率更高的土地覆盖类型作为先验知识,解决了先验知识无法大面积获取的问题,但仍受到土地覆盖数据精度的影响。
反演模型优化策略包括数值迭代优化法、查找表法、神经网络法、支持向量机、遗传算法等,广泛的用于植被冠层结构、LAI、FAPAR、叶绿素含量等植被生理参数的反演[96, 140-144]。MODIS陆地和海洋上空大气气溶胶光学厚度、水汽含量等大气参量反演则在辐射传输模型的基础上,借助查找表法,提升大气参数的反演效率[145]。地球探索计划EarthCARE卫星搭载的大气探测激光雷达、云廓线雷达、多光谱成像仪和宽波段辐射计等四颗不同类型的传感器,通过主被动传感器的组合,利用查找表法、神经网络模型等方法实现云、大气气溶胶的垂直和水平分布以及大气层顶短波和长波通量数据的同步反演[146-148]。SMOS卫星计划最新的第六版本海洋盐度反演算法则是在微波辐射传输模型的基础上[149],考虑能量由大气进入泡沫层并被大小尺寸不均匀分布的泡沫层吸收和耗散的过程,进一步优化提出了基于L波段的泡沫发射率模型,在反演海洋盐度的同时,同步获得了亮度温度、海表粗糙度等数据[149]。利用数值优化方法求解重力梯度仪解析模型、重力场模型、微波随机体散射—地面散射组合模型、多普勒频移量模型等,实现了重力异常、高度、风速等参数的提取[150]。
然而,当前模型反演仍以MODIS、SPOT VGT等中低分辨率遥感观测数据为主,其原因是现有的反演模型多假设等距邻近像元对目标像元产生相同的影响,无法有效地描述异质性像元的邻近效应,从而使得模型对高分辨率遥感数据不适应。但中低分辨率遥感观测的像元均是由多种不同地物组成的混合像元,基于单一对象刻画的模型对混合像元反演时,往往增加了模型反演结果的不确定性[129],如对MODIS LAI和FAPAR产品的验证结果显示非洲部分地区的反演误差达15%和20%[151]。遥感观测的瞬时性与观测对象变化过程的动态性之间也存在不可调和的矛盾[129, 152-157]。另一方面,模型反演的时间代价不容忽视。基于中低分辨率卫星观测数据的反演算法在并行优化之后仍需要数天时间完成,但当反演所用观测数据的分辨率提高至30 m（Landsat、HJ-1等卫星）或10 m（GF、Sentinel-2）时,反演算法耗时将呈现指数级增加。总体上,现有反演模型无法有效平衡高分辨率数据与低分辨率遥感数据在反演过程中的弊端。
4 数据产品
本节以美国MODIS数据产品、欧空局哥白尼计划陆地数据产品、中国全球陆表特征参数（GLASS）产品以及集成多源卫星数据生成的CYCLOPES、降水和蒸散数据产品为例,分析各遥感数据产品采用的方法及策略。MODIS数据产品（表1）有7个采用目标识别方法,1个产品所用的是遥感指数方法,1个采用经验/半经验模型,8个采用物理模型,部分数据产品生成过程中,采用了多种数据产品生成方法,如MODIS云综合产品中的云掩膜产品采用的是基于决策树的目标识别方法,而云相、云光学厚度、有效云粒径、云顶温度、云高度等参量则基于物理模型提取。MODIS数据产品完全依赖MODIS单一传感器获得的遥感观测数据,生成的数据产品虽已得到广泛应用,但无论采用的方法如何优越,生成的数据产品均无法规避MODIS观测数据质量、传感器衰退等因素的影响。
Tab. 1
表1
表1美国、欧洲和中国代表性遥感产品所用方法分类
Tab. 1Methodology used for producing remote sensing data products in the US, Europe and China
| 共性方法 | MODIS数据产品* | 欧空局哥白尼计划陆地数据产品 | GLASS数据产品 | |
|---|---|---|---|---|
| 目标识别 | 云掩膜;雪覆盖;土地覆盖;热异常—火点;海冰覆盖;土地覆盖动态变化;火烧迹地 | 水体1;火烧迹地1; | ||
| 参数提取 | 遥感指数 | 植被指数(NDVI/EVI) | NDVI1;植被状况指数(VCI)1 | |
| 经验/半经验模型 | 总初级生产力(GPP)/净初级生产力(NPP); | VPI1;DMP(干物质生产力)1;地表温度(LST)n;Albedo1; | Albedon;裸土宽波段发射率n;长波净辐射n;净辐射n;潜热通量n;GPP n | |
| 物理模型 | 气溶胶;可降水量;云产品(云相、云光学厚度、有效云粒径、云顶温度、高度等);地表温度和比辐射率;大气剖面;LAI/FAPAR;蒸散发;Albedo/BRDF | LAI1;FAPAR1;fCover1;土壤水分1;反射率1 | LAIn;FAPARn;fCovern;植被区宽波段发射率n;下行短波辐射n;光合有效辐射n | |
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欧空局哥白尼计划陆地监测项目将利用30颗包括RADARSAT2、ENVISAT ASAR等SAR数据以及SPOT VGT、Proba-V、ENVISAT MERIS和6颗哨兵（Sentinel）系列卫星等多源卫星数据,提供陆表植被监测、能量平衡和水分监测的多种数据产品。截至2017年仅提供12种数据产品（表1）,其中,NDVI及其衍生出的VCI产品的生成属于遥感指数方法;DMP、VPI和地表温度则利用FAPAR、NDVI或热红外波段亮度温度建立的经验/半经验统计模型生成;LAI、FAPAR、fCover、反射率和土壤水分等产品的生成则采用神经网络模型对反演模型进行优化;Albedo产品通过对各波段反照率经验组合计算获得;火烧迹地和水体产品采用目标识别方法获得。现阶段,哥白尼计划提供的数据产品仍主要依赖SPOT VGT及其后续星Proba-V等遥感传感器,遥感观测数据的质量直接影响数据产品的质量好坏,仅LST产品综合利用了多颗静止气象卫星数据。
集成多种卫星平台的观测数据、多种数据产品生成更高精度、质量更可靠的遥感数据产品是常见的策略。全球陆表特征参数（GLASS）产品基于AVHRR、MODIS和多种地球同步卫星观测数据生成1982-2014年长时间序列的8种数据产品。GLASS的LAI、fCover产品基于神经网络模型对现有数据进行融合和时空序列数据插补而成;FAPAR则基于GLASS LAI产品采用孔隙率模型反演生成;Albedo、长波净辐射、净辐射产品采用经验统计法获得;GPP产品则采用经验/半经验的光能利用率模型实现产品生产;发射率产品在裸土区采用经验统计法,在植被区则采用查找表优化4SAIL辐射传输模型实现参数提取;下行短波辐射、光合有效辐射则采用查找表法实现辐射量的反演[142]。CYCLOPES项目集成AVHRR、VEGETATION、POLDER、MERIS和MSG数据,利用模型反演（基于神经网络算法的辐射传输模型）方法生成了LAI、fCover、FAPAR、反照率（Albedo）等生物物理参数产品[137, 143]。以LAI数据产品为例,对比MODIS、VGT、CYCLOPES和GLASS产品的精度（表2）,MODIS上午星和下午星双星联合反演的精度较单一卫星数据产品高,而结合多颗卫星搭载的多颗传感器联合反演获得的CYCLOPES和GLASS数据产品精度较双星反演精度更高。
Tab. 2
表2
表2全球不同LAI产品精度验证对比结果
Tab. 2Validation of different LAI products based on ground measurement
| 数据产品 | 植被类型 | 相对误差 | 均方根误差 | 文献 |
|---|---|---|---|---|
| MODIS Terra LAI | 混合类型1* | - | 1.07~2.08 (与有效LAI对比) 1.42 (与真实LAI对比) | [158-160] |
| MODIS Aqua LAI | 混合类型1* | - | 1.74 (与有效LAI对比) 1.53 (与真实LAI对比) | [158] |
| MODIS Terra & Aqua LAI | 农田 | 88% | 0.5~1.05 | [161] |
| 森林 | 35%~65% | - | [161] | |
| 草地 | 47% | - | [161] | |
| 混合类型2# | - | 1.29 (与有效LAI对比) 1.14 (与真实LAI对比) | [154] | |
| 混合类型1* | - | 1.63 (与有效LAI对比) 1.09 (与真实LAI对比) | [158] | |
| VGT LAI | 农田 | 44% | 0.5~1.05 | [161] |
| 森林 | 25%~37% | - | [161] | |
| 草地 | 76% | - | [161] | |
| CYCLOPES LAI | 混合类型2* | - | 0.73 (与有效LAI对比) 0.84 (与真实LAI对比) | [143] |
| 混合类型1* | - | 0.50~1.34 (与有效LAI对比) 0.97 (与真实LAI对比) | [158-159] | |
| GLASS LAI | 混合类型1* | - | 0.78~0.87 | [159-160] |
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多传感器联合反演降水（Multi-sensor Precipitation Estimation, MPE）的方法同样是为了克服从单一来源的遥感观测数据到降水数据产品反演过程的缺点[162]。热带降雨测量计划（Tropical Rainfall Measurement Mission, TRMM）多卫星降水分析数据产品以TRMM微波成像仪、先进微波扫描辐射仪（AMSR-E）、专用微波成像仪（SSMI）、专用微波成像声呐（SSMIS）、高级微波温度计（AMSU）、大气湿度廓线探测仪（MHS）和微波调节融合红外仪（IR）7种遥感观测数据为基础,形成空间分辨率为0.25°、时间分辨率3 h的降水产品。全球降水观测计划（Global Precipitation Measurement, GPM）携带的双频降雨雷达和GPM微波成像仪,同现有的MPE方法所用观测数据结合,加上欧洲第三代气象卫星、美国的GOES-R卫星和已经发射升空的中国风云四号（FY-4）卫星,为全球多传感器联合反演降水提供新的观测数据源,提升降水数据产品的精度[162]。
基于遥感的区域蒸散量监测方法（ETWatch）[25-26, 153, 163]充分利用不同卫星数据产品在相关地表参数或地表通量模型构建时能够提供的优势信息,发展了基于风云静止气象卫星云产品数据的净辐射模型[164]、基于MODIS/AIRS大气廓线产品的大气边界层高度模 型[165]、基于光学—红外—微波数据的空气动力学粗糙度模型[166]以及基于不同下垫面、不同气候环境参量的感热通量模型[167]等,最终通过模型集成,构建了多尺度—多源数据协同的陆表蒸散遥感模型参数化方法[163]。
5 讨论
对遥感的理解有不同的角度,本文仅将遥感作为一种观测手段,剖析从遥感观测数据到数据产品的数据处理方法。遥感发展了50余年,大量的研究解决了特定时间、特定区域的应用问题,推动了利用遥感观测数据生成数据产品方法的进步,形成的数据产品及信息服务能力也不断提升,但均在一定程度上受时间、区域、使用者经验的影响,导致在解决相同问题时,存在结果不一、结论矛盾的现象。遥感从观测数据到生成数据产品的过程至今仍未形成系统性、科学性的方法论,还有很长的路要走。准确性更高、更可靠的数据产品是用户使用遥感数据产品时最关注的问题,产品生成过程的复杂性用户并不在意。目标识别方法重点解决识别地物类型的问题,但精度不高且受专家经验知识的影响;前文提到的ChinaCover土地覆盖数据产品采用面向对象的变化检测和决策树方法[29, 48, 168],2000-2014年全球森林年度动态变化数据产品采用决策树分类方法[169],在建立分类体系和不同类别区分标准的前提下,具有显著的结构化特征而得到广泛应用。参数提取解决的是从遥感观测数据中提取具有物理、化学、地学、生态学、生物学等实际意义的数据产品,其中遥感指数产品的物理意义不明确或不唯一;基于经验/半经验模型的参数提取方法对问题的内涵及物理机制解释不清,能够有效的解决一时一地的实际应用问题,但模型适用性常受实验条件限制,外推性较差,只能是解决遥感从观测数据生成数据产品的权宜之计;基于物理模型的参数提取方法为地表参量数据产品的生成提供了具有物理内涵的解决途径,但方法本身的“病态”性决定了方程“无定解”的现状,模型对地表状态和物理过程刻画的精确度即模型本身的质量直接导致模型反演精度受限[129];通过数值迭代优化、查找表法、遗传算法等模型反演优化策略,多通过数值逼近、相似性对比等方法,实质上是从预先“模拟”出的结果库中筛选出模型反演结果。以上各类方法均存在不足,没有一种方法能够让终端用户信心十足。即便是现有的遥感主流产品,如LAI遥感产品,采用的方法、形成的产品间仍存在差异[97, 128, 158-161]。未来需要对数据分析处理策略进行仔细的分析和梳理、科学论证和验证,去伪存真,明确哪些方法是结构化的,哪些方法能够改造成结构化方法,哪些方法只是权宜之计,哪些方法能获得定量数据,哪些方法只能获得定性的数据,哪些方法实质上是在“伪造数据”,以及这些方法的精度水平及改进空间,从而指明现有方法如何向结构化方法转变,逐步构建以结构化为特征的、科学的从遥感观测数据生成数据产品的遥感数据产品方法论。
（1）发展新型遥感指数产品。遥感指数作为一种凸显不同地物差异的参数产品,其构建方法符合结构化方法的特征,但现有的遥感指数的物理意义欠缺,在应用时往往存在适应性限制。需结合遥感信息自身的优势,从问题出发,发展出一些易于处理且能够反映生态学、地学、气象学意义的特征指标,充分挖掘遥感观测数据隐含的深层指示性特征,构建具有指示性意义的新型遥感指数数据产品。例如对地表水下渗、自然地表蒸散、城市热岛效应、城市内涝等具有重要影响的不透水面要素[71-72],能够利用不透水面指数实现快速提取,识别方法相对简单[170-171];用于粮食安全早期预警的作物生长早期的耕地种植比例指数,较传统的作物类型的识别精度大幅度提高[172],如2015年9月之后南非出现严重旱情,耕地种植比例较2014年同期偏低达34%,全球农情遥感速报系统（CropWatch）基于该信息对南非玉米生产形势做出了早期预警。
（2）以数据产品为导向。现有的数据产品多以卫星为导向,每种卫星观测数据都有一套各自的数据产品,数据产品各成体系。同时不同卫星获得数据产品受限于遥感传感器的不一致性,相互间的时空连续性和一致性较差,为数据产品的广泛应用造成障碍[95]。遥感数据产品生成方法应该以形成高质量的数据产品为目标,如前文提到的CYCLOPES项目[138, 144]、多传感器联合反演降水数据产品[162]、基于遥感的区域蒸散量监测方法（ETWatch）及其产出的多尺度-多源数据协同的陆表蒸散发数据产品[25-26, 153, 163],充分利用所有可用的遥感观测数据,发挥不同遥感观测数据的优势,已经成为反演高精度、高分辨率遥感数据产品的主流途径[162]。未来应利用多源协同遥感观测与分析处理方法,充分结合多种遥感观测数据的优势,形成合力,提高数据产品的精度。数据产品为导向的遥感处理方法需进一步拓展至卫星传感器设计、卫星发射计划等方面,围绕现有数据产品分析处理过程中的缺陷和需求,有针对性的发展新型传感器和卫星计划,以实现数据产品质量的提高。
（3）与前沿计算机技术充分结合。当前全球覆盖的遥感数据产品多为中低分辨率的产品,空间分辨率多低于250 m,高分辨率遥感数据产品十分匮乏,特别是时间连续、空间无缝的数据产品缺失,使得精细尺度遥感应用的需求无法满足[173]。另一方面,大数据时代,遥感传感器的发展使得遥感观测数据的时空分辨率逐步提高,产生的遥感观测数据的数据量呈几何级数增长,对海量遥感观测数据的快速自动化处理依赖于计算机技术的创新[174]。近年来,深度学习方法逐渐被引入到图像分割、目标识别和分类中[175-176],利用机器学习的过程对图像所包含的具有生态学、地理学、农学意义的深层特征信息进行挖掘,开展高精度的建筑、水体、裸地等不同地物类型的分类以及飞机、舰船等目标识别。ImageNet大规模视觉识别挑战赛举办以来,图像识别的错误率从2012年的29.6%降到了2015年的3.6%,充分显示了深度学习在目标识别中的作用[177-179]。Google针对地球观测大数据,开发了全球尺度PB级数据处理能力的Google Earth Engine云平台[180],极大提升了地球观测大数据的处理与信息挖掘能力。Google Earth Engine内置全球经预处理的长时间序列的Landsat/MODIS等系列数据,能够快速实现长时间序列大范围农作物种植区的提取与分析、全球尺度森林动态变化监测等[169, 180],为遥感与先进计算机技术结合提供应用典范。利用非遥感大数据充分挖掘待分类识别目标的深层隐含特征[181],将为基于遥感的目标分类识别方法提供新的解决途径。未来结合深度学习、大数据处理等技术,有望解决传统处理方法无法有效解决的复杂难题,依托集群、云技术的数据密集型计算方法,突破高分辨率遥感数据分析处理的时间瓶颈,实现高分辨率时空连续的遥感数据产品的快速生成与动态追加更新[182]。国家重点研发计划“全球变化及应对”重点专项“全球变化大数据的科学认知与云共享平台”项目拟结合大数据与深度学习方法,基于Landsat系列卫星数据,结合其他光学、SAR等多源遥感数据,实现20世纪70年代以来多年度30 m分辨率森林覆盖、火烧迹地、陆面水体、不透水面、耕地和极地冰盖冻融6种关键地表覆盖类型的快速生成[183],将为全球变化研究提供更加全面细致的数据支撑。
（4）建立遥感处理方法标准体系。标准是产品是否达标、是否合规的标志,其能减少人为主观因素影响,避免相同的观测数据获得的数据质量因方法、因地域、因人而异。纵观农业、生态、气象、水文、国土资源、测绘等遥感应用常见领域,均有各自的成体系的国家标准,例如土地利用现状分类国家标准明确规定了土地利用的类型、含义,为土地调查观测提供标准章程。与遥感高度相关的测绘学科,早在1984年便由国家测绘地理信息局设立了测绘标准化研究所,专门从事测绘标准化研究,先后制定了大地、航测、制图等多个领域的系列国家标准以及测绘地理信息行业标准制修订[184]。遥感领域也有少量的国家标准与行业标准,如卫星遥感影像植被指数产品规范,但针对从遥感观测数据到生成数据产品这一分析方法的标准相对缺失,需大力推进遥感从观测数据到数据产品的分析处理方法的标准规范制定。为建立遥感处理方法标准体系,需要对现有的遥感数据产品生成方法进行全面收集整理,分析不同类型的数据产品所用的方法特点,以及相同遥感数据处理方法用于生成不同数据产品时的差异性,综合分析归纳,并将从遥感观测数据到生成数据产品的全过程进行步骤细分,逐渐形成各个步骤的标准输入、输出流程,制定出输入输出的标准规范,形成从遥感观测数据到生成数据产品的全流程标准体系。
6 结语
现代科学以观测为基础。从遥感观测数据到获取具有实际物理意义的数据产品的过程所采用的各类方法均存在一定问题,根本原因是遥感从观测数据到生成数据产品过程的系统、科学方法的缺失。未来需要建立遥感从观测数据到数据产品的系统性方法论和处理方法标准体系,包括建立结构化方法、发展新型遥感指标、以数据产品为导向、与前沿技术结合等方面,实现遥感从观测数据到数据产品的方法向科学化与结构化转变,推动遥感从观测数据到生成数据产品的标准化、规范化。系统性方法论的形成有望提升遥感在各行各业的应用前景,改变遥感在传统行业饱受质疑的现状。
致谢：特别感谢中国科学院遥感与数字地球研究所曾红伟副研究员、赵旦博士、邢强博士和朱伟伟博士以及联合国粮农组织前副总干事何昌垂先生在文章成稿和修改过程中提供的宝贵意见和建议。
The authors have declared that no competing interests exist.
参考文献 原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子
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| [3] | 在区域农田生态系统生产力模拟模型研究中，空间插值可以提供每个计算栅格的气象要素资料。然而，在众多的气象要素空间插值方法中，并没有一种适合每一个气象要素的普适的最佳插值方法。本文以全国７２５站１９５１～１９９０年整编资料中的旬平均温度和计算得来的６７５站的月乎均光合有效辐射日总量（ＰＡＲ）为数据源，选用了距离平方反比法（ＩＤＳ）、梯度距离平方反比法（ＧＩＤＳ）和普通克立格法（ＯＫ）等３种插值方法，进行了方法选取的探讨。交叉验证结果表明：３种方法中，温度插值的平均绝对误差（ＭＡＥ）的排序为ＩＤＳ＞ＯＫ＞ＧＩＤＳ，其值分别为２．１５℃、１.９０℃和 ｌ．３２℃；在作物生长季节（４－１０月），ＭＡＥ分别 ２０℃、１．９℃和 １.２℃ ，表明ＧＩＤＳ在温度插值方面更具实用价值；对于ＰＡＲ，ＭＡＥ的排序为ＯＫ＞ＧＩＤＳ＞ＩＤＳ，其值分别为 ０．８３ＭＪ／ｍ２、０７１ＭＪ／ ｍ２和 ０．４６ＭＪ／ｍ２，说明复杂的方法并不必然具有更好的效果。对这２个气象要素的空间分布特征分析表明：温度和ＰＡＲ的经、纬向梯度和高度梯度均具有明显的季节性变化特征；温度的纬向梯度有近似正弦曲线的较强的季节变化，表现为夏季高，而冬、春季低；温度的高度梯度年 . 在区域农田生态系统生产力模拟模型研究中，空间插值可以提供每个计算栅格的气象要素资料。然而，在众多的气象要素空间插值方法中，并没有一种适合每一个气象要素的普适的最佳插值方法。本文以全国７２５站１９５１～１９９０年整编资料中的旬平均温度和计算得来的６７５站的月乎均光合有效辐射日总量（ＰＡＲ）为数据源，选用了距离平方反比法（ＩＤＳ）、梯度距离平方反比法（ＧＩＤＳ）和普通克立格法（ＯＫ）等３种插值方法，进行了方法选取的探讨。交叉验证结果表明：３种方法中，温度插值的平均绝对误差（ＭＡＥ）的排序为ＩＤＳ＞ＯＫ＞ＧＩＤＳ，其值分别为２．１５℃、１.９０℃和 ｌ．３２℃；在作物生长季节（４－１０月），ＭＡＥ分别 ２０℃、１．９℃和 １.２℃ ，表明ＧＩＤＳ在温度插值方面更具实用价值；对于ＰＡＲ，ＭＡＥ的排序为ＯＫ＞ＧＩＤＳ＞ＩＤＳ，其值分别为 ０．８３ＭＪ／ｍ２、０７１ＭＪ／ ｍ２和 ０．４６ＭＪ／ｍ２，说明复杂的方法并不必然具有更好的效果。对这２个气象要素的空间分布特征分析表明：温度和ＰＡＲ的经、纬向梯度和高度梯度均具有明显的季节性变化特征；温度的纬向梯度有近似正弦曲线的较强的季节变化，表现为夏季高，而冬、春季低；温度的高度梯度年 |
| [4] | . |
| [5] | 根据长江中上游697个气象观测站1971—2000年30年降水资料，利用逐步回归方法和地理信息技术(GIS)，建立了平均季降水和年降水与4 km分辨率的DEM、坡向、坡度等地形数据的回归方程，并通过了信度为0.05的F检验，将降水量中地形影响部分分离出来。在此基础上，发展了逐步插值方法（SIA），并与GIS技术和多元逐步回归方法结合，显著提高了年、季降水空间分布的计算精度。结果表明：SIA季节降水空间分布的相对误差为6.86%，绝对误差为13.07 mm，平均变差系数为0.070，平均相关系数为0.9675；年降水量的绝对误差为72.1 mm，相对误差为7.34%，平均变差系数为0.092，相关系数达到了0.9605。对SIA年平均降水量的分析表明，采用3—5步的SIA计算，就可以显著提高计算精度，绝对误差由211.0 mm下降到62.4 mm，相对误差由20.74%下降到5.97%，变差系数从0.2312下降到0.0761，相关系数由0.5467提高到0.9619，SIA方法50步的计算表明，SIA计算的结果一致收敛于观测值。 . 根据长江中上游697个气象观测站1971—2000年30年降水资料，利用逐步回归方法和地理信息技术(GIS)，建立了平均季降水和年降水与4 km分辨率的DEM、坡向、坡度等地形数据的回归方程，并通过了信度为0.05的F检验，将降水量中地形影响部分分离出来。在此基础上，发展了逐步插值方法（SIA），并与GIS技术和多元逐步回归方法结合，显著提高了年、季降水空间分布的计算精度。结果表明：SIA季节降水空间分布的相对误差为6.86%，绝对误差为13.07 mm，平均变差系数为0.070，平均相关系数为0.9675；年降水量的绝对误差为72.1 mm，相对误差为7.34%，平均变差系数为0.092，相关系数达到了0.9605。对SIA年平均降水量的分析表明，采用3—5步的SIA计算，就可以显著提高计算精度，绝对误差由211.0 mm下降到62.4 mm，相对误差由20.74%下降到5.97%，变差系数从0.2312下降到0.0761，相关系数由0.5467提高到0.9619，SIA方法50步的计算表明，SIA计算的结果一致收敛于观测值。 |
| [6] | 论文以MTCLIM模型为基础,根据中国的实际情况,对模型的参数进行了优化与调整,建立了适合我国的基于温度、降水和相对湿度等因子的太阳总辐射计算模式。提出了太阳总辐射空间化的模式,即先参数化,然后采用合适的空间插值方法(论文中采用地统计学方法)实现空间化。同时鉴于逐日太阳总辐射空间化工作量庞大,提出了在计算月均日结果的基础之上,采用线性插值技术快速得到逐日太阳辐射值。从而得到了中国太阳总辐射的空间分布结果。 . 论文以MTCLIM模型为基础,根据中国的实际情况,对模型的参数进行了优化与调整,建立了适合我国的基于温度、降水和相对湿度等因子的太阳总辐射计算模式。提出了太阳总辐射空间化的模式,即先参数化,然后采用合适的空间插值方法(论文中采用地统计学方法)实现空间化。同时鉴于逐日太阳总辐射空间化工作量庞大,提出了在计算月均日结果的基础之上,采用线性插值技术快速得到逐日太阳辐射值。从而得到了中国太阳总辐射的空间分布结果。 |
| [7] | 采用土壤空间变异及其插值方法，对上海五四农场现代化农业园区水稻田60m×60m 间隔采样，得到280个土壤有机质含量、速效磷、速效钾、全N、全P等采样数据，用逆距离加权、球面多项式、局部多项式、辐射基础函数、简单克立格、通用克立格、平常克立格(指数模型、球形模型、高斯模型、静态模型)等插值方法，对该区土壤速效磷含量的140采样点进行插值，得到各种插值的速效磷连续空1'4分布，将插值所得拟合值与同期测得的另140个采样点数据进行比较，则局部多项式插值、球面多项式和3种克立格插值方法效果较好，其中局部多项式插值方法效果最佳，平常克立格的静态模型、高斯模型和球形模型插值效果较佳。 . 采用土壤空间变异及其插值方法，对上海五四农场现代化农业园区水稻田60m×60m 间隔采样，得到280个土壤有机质含量、速效磷、速效钾、全N、全P等采样数据，用逆距离加权、球面多项式、局部多项式、辐射基础函数、简单克立格、通用克立格、平常克立格(指数模型、球形模型、高斯模型、静态模型)等插值方法，对该区土壤速效磷含量的140采样点进行插值，得到各种插值的速效磷连续空1'4分布，将插值所得拟合值与同期测得的另140个采样点数据进行比较，则局部多项式插值、球面多项式和3种克立格插值方法效果较好，其中局部多项式插值方法效果最佳，平常克立格的静态模型、高斯模型和球形模型插值效果较佳。 |
| [8] | |
| [9] | <p>在调研国内外遥感案例的基础上,论述了遥感在推动地球系统科学发展方面的作用,及在我国的重点应用领域。遥感催生了全球变化研究,使得人类得以从新的视角来探索地球上的生命未来;遥感推动了地球科学从定性到定量、从描述到分析、从单站点到多时空尺度的变革,诸多新兴交叉学科应运而生。遥感是应用驱动的, 一致性及可对比性是定量遥感的核心,也是遥感深化应用的基础。遥感应用于众多领域,但不同的国家基于各自的国情有不同的侧重点,其中,维护国家全球利益、灾害快速响应与灾后评估、第三方独立监督、保障国防安全是我国的应用重点。</p> . <p>在调研国内外遥感案例的基础上,论述了遥感在推动地球系统科学发展方面的作用,及在我国的重点应用领域。遥感催生了全球变化研究,使得人类得以从新的视角来探索地球上的生命未来;遥感推动了地球科学从定性到定量、从描述到分析、从单站点到多时空尺度的变革,诸多新兴交叉学科应运而生。遥感是应用驱动的, 一致性及可对比性是定量遥感的核心,也是遥感深化应用的基础。遥感应用于众多领域,但不同的国家基于各自的国情有不同的侧重点,其中,维护国家全球利益、灾害快速响应与灾后评估、第三方独立监督、保障国防安全是我国的应用重点。</p> |
| [10] | . |
| [11] | Decadal-scale oscillatory modes of atmosphere-ocean variability have recently been identified in instrumental studies of the Pacific sector. The regime shift around 1976 is one example of such a fluctuation, which has been shown to have significantly impacted climate and the environment along the coastline of the western N and S Americas. The length of meteorological data for the Pacific and western Americas critically limits analyses of such decadal-scale climate variability. Here we present reconstructions of the annual Pacific Decadal Oscillation (PDO) index based on western North American tree-ring records which account for up to 53% of the instrumental variance and extend as far back as AD 1700. The PDO reconstructions indicate that decadal-scale climatic shifts have occurred prior to the period of instrumental record. Evaluation of temperature and precipitation-sensitive tree-ring series from the northeast Pacific as well as these reconstructions reveals evidence for a shift towards less pronounced interdecadal variability after about the middle 1800s. Our analyses also suggest that sites from both the northeast Pacific coast as well as the subtropical Americas need to be included in proxy data sets used to reconstruct the PDO. |
| [12] | 该文在对像元二分模型的两个重要参数推导的基础上，对已有模型的参数估算方法进行改进，建立了用NDVI归一化植被指数定量估算植被覆盖度的模型，并根据实际运用时的二种情况，提出了估算植被覆盖度的方案。然后根据研究区密云水库上游的具体特点并结合实际情况设计了模型应用的技术路线和实施方法，对研究区植被覆盖度进行了估算。通过密云流域的实地考察，利用照相法对植被覆盖度的估算结果进行了验证，估算精度达85%，表明使用此改进模型进行植被覆盖度遥感监测是可行的。 . 该文在对像元二分模型的两个重要参数推导的基础上，对已有模型的参数估算方法进行改进，建立了用NDVI归一化植被指数定量估算植被覆盖度的模型，并根据实际运用时的二种情况，提出了估算植被覆盖度的方案。然后根据研究区密云水库上游的具体特点并结合实际情况设计了模型应用的技术路线和实施方法，对研究区植被覆盖度进行了估算。通过密云流域的实地考察，利用照相法对植被覆盖度的估算结果进行了验证，估算精度达85%，表明使用此改进模型进行植被覆盖度遥感监测是可行的。 |
| [13] | We use a simple radiative transfer model with vegetation, soil, and atmospheric components to illustrate how the normalized difference vegetation index (NDVI), leaf area index (LAI), and fractional vegetation cover are dependent. In particular, we suggest that LAI and fractional vegetation cover may not be independent quantitites, at least when the former is defined without regard to the presence of bare patches between plants, and that the customary variation of LAI with NDVI can be explained as resulting from a variation in fractional vegetation cover. The following points are made: i) Fractional vegetation cover and LAI are not entirely independent quantities, depending on how LAI is defined. Care must be taken in using LAI and fractional vegetation cover independently in a model because the former may partially take account of the latter; ii) A scaled NDVI taken between the limits of minimum (bare soil) and miximum fractional vegetation cover is insenstive to atmospheric correction for both clear and hazy conditions, at least for viewing angles less than about 20 degrees from nadir; iii) A simple relation between scaled NDVI and fractional vegetation cover, previously described in the literature, is further confirmed by the .simulations; iv) The sensitive dependence of LAI on NDVI when the former is below a value of about 2 4 may be viewed as being due to the variation in the bare soil component. |
| [14] | Vegetation indices, including the simple ratio (SR) and the normalized difference vegetation index (NDVI), from Landsat TM data were correlated to ground-based measurements of LAI, effective LAI, and the crown closure in boreal conifer forests located near Candle Lake and Prince Albert, Saskatchewan and near Thompson, Manitoba, as part of the Boreal Ecosystem-Atmosphere Study (BOREAS). The measurements were made using two optical instruments: the Plant Canopy Analyzer (LAI-2000, LI-COR) and the TRAC (Tracing Radiation and Architecture of Canopies). The TRAC was recently developed to quantify the effect of canopy architecture on optical measurements of leaf area index. The stands were located on georeferenced Landsat TM images using global positioning system (GPS) measurements. It is found that late spring Landsat images are superior to summer images for determining overstory LAI in boreal conifer stands because the effect of the understory is minimized in the spring before the full growth of the understory and moss cover. The effective LAI, obtained from gap fraction measurements assuming a random distribution of foliage spatial positions, was found to be better correlated to SR and NDVI than LAI. The effective LAI is less variable and easier to measure than LAI, and is also an intrinsic attribute of plant canopies. It is therefore suggested to use effective LAI as the most important parameter for radiation interception considerations. |
| [15] | An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities. |
| [16] | 以山东禹城为研究区，利用我国自主研发运行的HJ-1卫星数据，计算了4种植被指数（NDVI，RVI，SAVI，EVI），结合同步观测数据，对植被指数与实测FPAR进行回归分析，比较4种植被指数模型对夏玉米FPAR的估测精度，结果表明各植被指数与FPAR均具有较高的相关性，整个研究区NDVI具有最高的反演精度，对估算夏玉米FPAR的最优模型进行验证，得出模型的平均误差仅为3.8%，模型达到了较高的精度。利用HJ-1 CCD反演了山东禹城9月的FPAR。 . 以山东禹城为研究区，利用我国自主研发运行的HJ-1卫星数据，计算了4种植被指数（NDVI，RVI，SAVI，EVI），结合同步观测数据，对植被指数与实测FPAR进行回归分析，比较4种植被指数模型对夏玉米FPAR的估测精度，结果表明各植被指数与FPAR均具有较高的相关性，整个研究区NDVI具有最高的反演精度，对估算夏玉米FPAR的最优模型进行验证，得出模型的平均误差仅为3.8%，模型达到了较高的精度。利用HJ-1 CCD反演了山东禹城9月的FPAR。 |
| [17] | The fraction of absorbed photosynthetically active radiation (FPAR) is an important biophysical parameter of vegetation. It is often estimated using vegetation indices (VIs) derived from remote-sensing data, such as the normalized difference VI (NDVI). Ideally a linear relationship is used for the estimation; however, most conventional VIs are affected by canopy background reflectance and their sensitivity to FPAR declines at high biomass. In this study, a multiplier, the ratio of the green to the red reflectance, was introduced to improve the linear relationship between VIs and crop FPAR. Three widely used VIs – NDVI, the green normalized difference VI (GNDVI), and the renormalized difference VI (RDVI) – were modified this way and were called modified NDVI (MNDVI), modified GNDVI (MGNDVI), and modified RDVI (MRDVI), respectively. A sensitivity study was applied to analyse the correlation between the three modified indices and the leaf area index (LAI) using the reflectance data simulated by the combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model (PROSAIL model). The results revealed that these new indices reduced the saturation trend at high LAI and achieved better linearity with crop LAI at low-to-medium biomass when compared with their corresponding original versions. This has also been validated using in situ FPAR measurements over wheat and maize crops. In particular, estimation using MNDVI achieved a coefficient of determination (R2) of 0.97 for wheat and 0.86 for maize compared to 0.90 and 0.82 for NDVI, respectively, while MGNDVI achieved 0.97 for wheat and 0.88 for maize, compared to 0.90 and 0.81 for GNDVI, respectively. Algorithms based on the VIs when applied to both wheat and maize showed that MNDVI and MGNDVI achieved a better linearity relationship with FPAR (R202=020.92), in comparison with NDVI (R202=020.85) and GNDVI (R202=020.82). The study demonstrated that applying the green to red reflectance ratio can improve the accuracy of FPAR estimation. |
| [18] | Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate iosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success. |
| [19] | In recent years, the use of high temporal resolution satellite data has been emerging as an important tool to study crop phenology. Most methods to detect phenological events based on satellite data use thresholds to identify key events in the lifecycle of the crop. In this study, a new method was used to define such thresholds for identifying the start and end of the growing season (SOS/EOS) for 43 different agricultural zones in China. The method used 2000-2003 NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data with a spatial resolution of eight kilometers and a temporal resolution of 15 days. Following data pre-processing, time series for the normalized difference vegetation index (NDVI or N), slope of the NDVI curve (S), and difference (D) between the NDVI value and a base NDVI value for bare land without snow were constructed. For each zone, an optimal set of threshold values for N, D, and S was determined, based on the remote sensing data and observed SOS/EOS data for 2003 at 261 agro-meteorological stations. Results were verified by comparing the accuracy of the new proposed NDS threshold method with the results of three other methods for SOS/EOS detection with remote sensing data. The findings of all four methods were compared to in situ SOS/EOS data from 2000 to 2002 for 110 agro-meteorological stations. Results show that the developed NDS threshold method had a significantly higher accuracy compared with other methods. The method is mainly limited by the observed data and the necessity of reestablishing the thresholds periodically. |
| [20] | Quantification of crop residue biomass on cultivated lands is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous studies focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIR IRS vertex by the three vertices at R , NIR IRS , and SWIR ( ANIR IRS ) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of determination ( R 2 ) of 0.811 was obtained between measured winter wheat residue biomass and ANIR IRS derived from simulated HJ-1B reflectance data. The ability of ANIR IRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIR IRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error ( RMSE ) between measured and estimated residue biomass was 0.038 kg/m 2 . ANIR IRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350 km) and four-day revisit rate of the HJ-1 satellite. While ANIR IRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIR IRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored. |
| [21] | While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial emporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail. |
| [22] | Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determination R2 of 0.507, 0.556 and 0.499; n65=6548). In addition, R2 values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (965×659 HJ CCD pixels to approximate that MODIS pixel), R2 values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring and agricultural ecosystem carbon cycle studies. |
| [23] | This study presents an approach for the determination of the trophic parameters Secchi disk transparency and chlorophyll- a from hyperspectral airborne casi and HyMap data with multitemporal validity. Based on in situ water sampling and reflectance measurements, algorithms have been developed. For Secchi disk transparency, the area between a base line and the spectrum from 400 to 750 nm was calculated and correlated to the Secchi disk transparency measured in situ; chlorophyll- a concentration was quantified using the existing reflectance ratio at 705 and 678 nm, which showed a linear relationship to chlorophyll- a concentration from laboratory spectrophotometric measurements. The algorithms have been adapted to the spectral characteristics of the airborne sensors and applied to these data recorded in September 1997 and 1998 and May and June 1999. The validation using independent in situ reference data showed mean standard errors of 1.2–1.3 m for Secchi disk transparency and of 10.2–10.9 μg/l chlorophyll- a . |
| [24] | Due to the impacts of rural and urban development on southwestern Australian estuaries, and the general isolation of these water bodies, there is a need to develop water quality monitoring systems that are both repetitive and cost-effective. The literature suggests that Landsat Thematic Mapper (TM) has spectral and spatial characteristics that are suited to monitoring small coastal water bodies. This study examined the potential for the satellite-based TM sensor to serve as a regulator monitoring tool. Atmospherically corrected TM digital data acquired on four dates over summer 1990/91 and concurrent field measurements collected at the time of the satellite over-pass over the Peel-Harvey Estuarine System were used to obtain multitemporal, empirical algorithms for predicting pigment concentration, Secchi disk depth (SDD), and salinity. Highly significant, predictive algorithms were developed for these parameters. It is concluded that Landsat TM has the resolution and accuracy to be a potentially very useful monitoring tool. However, cloud cover and delays in data acquisition seriously diminish its usefulness for monitoring on anything less than a seasonal basis. Laboratory-based radiometric studies also indicated that Landsat TM was unlikely to be useful in determining the taxonomic composition of phytoplankton blooms in coastal waters. |
| [25] | As the demand for water resources continues to grow, the current emand management approach often fails to deliver the expected results in terms of reduced water consumption, release of water to other uses, or improved environmental conditions. Recognizing that evapotranspiration (ET) represents the dominant consumptive use of water in the hydrologic cycle, this paper describes an approach to basin-scale water resources management based on ET. The ET management approach comprises four stages: (i) a basin-scale water consumption balance; (ii) determination of a target ET consistent with sustainable water consumption; (iii) identification of water consumption tradeoffs, competition and feedback among different water sectors (agricultural, industrial, domestic, and socio-environmental); and (iv) basin-wide monitoring of sustainable water consumption. Continuous, basin-wide ET data obtained from the ETWatch models are combined with estimates of water consumption as a result of mechanical, chemical, and biological energy to assess the water consumption balance, and set targets. On this basis, water resource managers can identify opportunities to achieve sustainable, productive use of water resources by (i) reducing non-beneficial ET; (ii) converting non-beneficial ET to beneficial ET; and (iii) increasing the productivity of beneficial ET. Irrigated agriculture is usually the largest controllable contribution to ET in a basin, so meeting the target ET for agriculture is key. A water balance analysis for Hai Basin and the implementation of ET management in the Basin are presented to illustrate the ET management approach. |
| [26] | The latent heat of evapotranspiration (ET) plays an important role for water resource management in water scarcity areas. Compared to the water balance method or to in situ measurements, an operational integrated monitoring method of regional surface ET from remote sensing data is a potentially useful approach to achieve water saving. This study presents new algorithms for the aerodynamic roughness length for complex landscape, for gap filling for cloud days, and for data fusion at different resolutions, based on the Penman onteith equation. It also presents an improved algorithm for ET calculation with remotely sensed data for clear days. Algorithms were integrated into the ETWatch. The research objective was to present the enhanced features of the ETWatch algorithm and its validation in the 320,000km2 Hai Basin in Northern China. This area faces serious over-exploitation of groundwater. ET was modeled and extensive field campaigns were done to collect data on soil moisture depletion, lysimeter measurements, eddy covariance measurements, and water balance calculations at diverse landscapes. The overall deviation for individual fields on a seasonal basis was 12% and decreased to 6% for an annual cycle. For larger areas, the deviation was 3% for an annual cycle. These levels of deviation are within the error bands for in situ measurements. The study concludes that data sets from ETWatch are able to aid consumptive water use reduction management in the study area. |
| [27] | The potential of high-resolution IKONOS and QuickBird satellite imagery for mapping and analysis of land and water resources at local scales in Minnesota is assessed in a series of three applications. The applications and accuracies evaluated include: (1) classification of lake water clarity ( r 2=0.89), (2) mapping of urban impervious surface area ( r 2=0.98), and (3) aquatic vegetation surveys of emergent and submergent plant groups (80% accuracy). There were several notable findings from these applications. For example, modeling and estimation approaches developed for Landsat TM data for continuous variables such as lake water clarity and impervious surface area can be applied to high-resolution satellite data. The rapid delivery of spatial data can be coupled with current GPS and field computer technologies to bring the imagery into the field for cover type validation. We also found several limitations in working with this data type. For example, shadows can influence feature classification and their effects need to be evaluated. Nevertheless, high-resolution satellite data has excellent potential to extend satellite remote sensing beyond what has been possible with aerial photography and Landsat data, and should be of interest to resource managers as a way to create timely and reliable assessments of land and water resources at a local scale. |
| [28] | No abstract is available for this item. |
| [29] | To meet the carbon storage estimate in ecosystems for a national carbon strategy, we introduce a consistent database of China land cover. The Chinese Huan Jing (HJ) satellite is proven efficient in the cloud-free acquisition of seasonal image series in a monsoon region and in vegetation identification for mesoscale land cover mapping. Thirty-eight classes of level II land cover are generated based on the Land Cover Classification System of the United Nations Food and Agriculture Organization that follows a standard and quantitative definition. Twenty-four layers of derivative spectral, environmental, and spatial features compose the classification database. Object-based approach characterizing additional nonspectral features is conducted through mapping, and multiscale segmentations are applied on object boundary match to target real-world conditions. This method sufficiently employs spatial information, in addition to spectral characteristics, to improve classification accuracy. The algorithm of hierarchical classification is employed to follow step-by-step procedures that effectively control classification quality. This algorithm divides the dual structures of universal and local trees. Consistent universal trees suitable to most regions are performed first, followed by local trees that depend on specific features of nine climate stratifications. The independent validation indicates the overall accuracy reaches 86%. |
| [30] | |
| [31] | Airborne hyperspectral data were analyzed for the classification of 11 forest cover types, including pure and mixed stands of deciduous and conifer species. Selected bands from first difference reflectance spectra were used to determine cover type at the Harvard Forest using a maximum likelihood algorithm assigning all pixels in the image into one of the 11 categories. This approach combines species specific chemical characteristics and previously derived relationships between hyperspectral data and foliar chemistry. Field data utilized for validation of the classification included both a stand-level survey of stem diameter, and field measurements of plot level foliar biomass. A random selection of validation pixels yielded an overall classification accuracy of 75%. |
| [32] | Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices. China's global crop-monitoring system (CropWatch) uses remote sensing data combined with selected field data to determine key crop production indicators: crop acreage, yield and production, crop condition, cropping intensity, crop-planting proportion, total food availability, and the status and severity of droughts. Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages. CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments. This paper presents a comprehensive overview of CropWatch as a remote sensing-based system, describing its structure, components, and monitoring approaches. The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach, as well as a comparison with other global crop-monitoring systems. |
| [33] | Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China) and “sub-countries” (for the nine largest countries). The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Cropped Arable Land Fraction (CALF) as well as Cropping Intensity (CI). Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI), cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion). Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly “CropWatch bulletin” which provides accurate and timely information essential to food producers, traders and consumers. |
| [34] | Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6 1.6-km 2 grid scale were comparable to county yields reported by the USDA ational Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields. |
| [35] | Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method application in other regions and agricultural systems. |
| [36] | Remote-sensing systems typically produce imagery that averages information over tens or even hundreds of square meters – far too coarse to detect most organisms – so the remote sensing of biodiversity would appear to be a fool's errand. However, advances in the spatial and spectral resolutions of sensors now available to ecologists are making the direct remote sensing of certain aspects of biodiversity increasingly feasible; for example, distinguishing species assemblages or even identifying species of individual trees. In cases where direct detection of individual organisms or assemblages is still beyond our grasp, indirect approaches offer valuable information about diversity patterns. Such approaches derive meaningful environmental parameters from biophysical characteristics that are revealed by remote sensing. |
| [37] | Abstract Aim Habitat heterogeneity has long been recognized as a key landscape characteristic determining biodiversity patterns. However, a lack of standardized, large-scale, high-resolution and temporally updatable heterogeneity information based on direct observations has limited our understanding of this connection and its effective use for biodiversity conservation. To address this, we develop here remote sensing-based metrics to characterize global habitat heterogeneity at 1-km resolution and assess their value for biodiversity modelling. Location Global. Methods <p>We develop 14 heterogeneity metrics (available at |
| [38] | |
| [39] | An important consideration for monitoring land-cover (LC) change is the nominal temporal frequency of remote sensor data acquisitions required to adequately characterize change events. Ecosystem-specific regeneration rates are an important consideration for determining the required frequency of data collections to minimize change omission errors. Clear-cut forested areas in north central North Carolina undergo rapid colonization from pioneer (replacement) vegetation that is often difficult to differentiate spectrally from that previously present. This study compared change detection results for temporal frequencies corresponding to 3-, 7-, and 10-year time intervals for near-anniversary date Landsat 5 Thematic Mapper (TM) data acquisitions corresponding to a single path/row. Change detection was performed using an identical change vector analysis (CVA) technique for all imagery dates. Although the accuracy of the 3-year analysis was acceptable (86.3%, κ=0.55), a significant level of change omission errors resulted (51.7%). Accuracies associated with both the 7-year (43.6%, κ=0.10) and 10-year (37.2%, κ=0.05) temporal frequency analyses performed poorly, with excessive change omission errors of 84.8% and 86.3%, respectively. The average rate of LC change observed over the study area for the 13-year index period (1987–2000) was approximately 1.0% per annum. Overall results indicated that a minimum of 3–4-year temporal data acquisition frequency is required to monitor LC change events in north central North Carolina. Reductions in change omission errors could probably best be achieved by further increasing temporal data acquisition frequencies to a 1–2-year time interval. |
| [40] | . The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. It will be produced globally at single-pixel resolution; the algorithm uses as many as 14 of the MODIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MODIS cloud mask is ancillary input to MODIS land, ocean, and atmosphere science algorithms to suggest processing options. The MODIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy-to-follow algorithm paths. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear-sky conditions. This paper outlines the MODIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MODIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets. |
| [41] | 中国十分重视海洋遥感及其监测技术的发展,初步形成了具有优势互补的海洋遥感观测体系,并发挥了显著的经济和社会效益。其中,海洋一号(HY-1A/B)卫星已经广泛应用于中国海温预报业务系统、冬季海冰业务监测、夏季赤潮和绿潮监测、海岸带动态变化监测、近岸海水水质监测和渔业遥感监测等方面。海洋二号(HY-2A)卫星不仅填补了中国海洋动力环境卫星遥感的空白,也是目前国际上唯一在轨运行的集主被动微波遥感器于一身的综合型海洋动力环境卫星,具备同时获取风场、有效波高、海面高度和海面温度的能力。通过卫星获得的数据提高了中国海洋环境监测与灾害性海况预报的水平,为国民经济建设和国防建设、海洋科学研究、全球变化研究等提供了可靠的遥感数据,同时还在国际对地观测体系中发挥了重要作用,受到国内外用户的高度认可。海洋一号和海洋二号卫星系列为中国建立完善的海洋环境立体监测体系奠定了坚实基础。根据国家发展和"一带一路"建设的实施,在加快建设海洋强国、维护海洋权益和加快发展海洋经济的进程中对海洋遥感的发展也进一步提出了更高的要求和更紧迫的需求。为此,紧紧围绕国家海洋强国战略需求,在《国家民用空间基础设施中长期发展规划(2015年—2025年)》中专门规划了海洋观测卫星系列,服务于中国的海洋资源开发、环境保护、防灾减灾、权益维护、海域使用管理、海岛海岸带调查和极地大洋考察等方面,同时兼顾陆地和大气观测领域的需求。在充分继承已有HY-1A/B、HY-2A、高分三号(GF-3)和中法海洋卫星(CFOSAT)成功研制经验和应用成果的基础上,发展多种光学和微波遥感技术,建设新一代的海洋水色卫星和海洋动力环境卫星,具备卫星组网观测能力;发展海洋监视监测卫星,构建优势互补的海洋卫星综合观测体系。通过空间基础设施的建设,海洋遥感卫星必将在建设海洋强国的进程中发挥出重要作用。 . 中国十分重视海洋遥感及其监测技术的发展,初步形成了具有优势互补的海洋遥感观测体系,并发挥了显著的经济和社会效益。其中,海洋一号(HY-1A/B)卫星已经广泛应用于中国海温预报业务系统、冬季海冰业务监测、夏季赤潮和绿潮监测、海岸带动态变化监测、近岸海水水质监测和渔业遥感监测等方面。海洋二号(HY-2A)卫星不仅填补了中国海洋动力环境卫星遥感的空白,也是目前国际上唯一在轨运行的集主被动微波遥感器于一身的综合型海洋动力环境卫星,具备同时获取风场、有效波高、海面高度和海面温度的能力。通过卫星获得的数据提高了中国海洋环境监测与灾害性海况预报的水平,为国民经济建设和国防建设、海洋科学研究、全球变化研究等提供了可靠的遥感数据,同时还在国际对地观测体系中发挥了重要作用,受到国内外用户的高度认可。海洋一号和海洋二号卫星系列为中国建立完善的海洋环境立体监测体系奠定了坚实基础。根据国家发展和"一带一路"建设的实施,在加快建设海洋强国、维护海洋权益和加快发展海洋经济的进程中对海洋遥感的发展也进一步提出了更高的要求和更紧迫的需求。为此,紧紧围绕国家海洋强国战略需求,在《国家民用空间基础设施中长期发展规划(2015年—2025年)》中专门规划了海洋观测卫星系列,服务于中国的海洋资源开发、环境保护、防灾减灾、权益维护、海域使用管理、海岛海岸带调查和极地大洋考察等方面,同时兼顾陆地和大气观测领域的需求。在充分继承已有HY-1A/B、HY-2A、高分三号(GF-3)和中法海洋卫星(CFOSAT)成功研制经验和应用成果的基础上,发展多种光学和微波遥感技术,建设新一代的海洋水色卫星和海洋动力环境卫星,具备卫星组网观测能力;发展海洋监视监测卫星,构建优势互补的海洋卫星综合观测体系。通过空间基础设施的建设,海洋遥感卫星必将在建设海洋强国的进程中发挥出重要作用。 |
| [42] | . |
| [43] | 遥感数据与土地资源在时空特性方面具有高度的一致性,土地资源研究长期是遥感应用的主要领域之一.过去数十年来,国内外开展了大量的土地资源与环境遥感应用研究,遥感技术为土地资源研究提供了丰富的信息源和实现手段,拓展了土地资源的研究内容,强化了土地资源的研究程度.随着遥感技术的发展和应用研究的深入,针对日益多样化的实际需求,创新研究方法,加强与传统学科的有机结合,在提取系列化专题信息基础上,开展不同时空尺度的综合性分析与评估,切实满足全球变化研究和实现区域可持续发展的需要,仍然是土地资源遥感应用研究应该关注的主要发展方向. . 遥感数据与土地资源在时空特性方面具有高度的一致性,土地资源研究长期是遥感应用的主要领域之一.过去数十年来,国内外开展了大量的土地资源与环境遥感应用研究,遥感技术为土地资源研究提供了丰富的信息源和实现手段,拓展了土地资源的研究内容,强化了土地资源的研究程度.随着遥感技术的发展和应用研究的深入,针对日益多样化的实际需求,创新研究方法,加强与传统学科的有机结合,在提取系列化专题信息基础上,开展不同时空尺度的综合性分析与评估,切实满足全球变化研究和实现区域可持续发展的需要,仍然是土地资源遥感应用研究应该关注的主要发展方向. |
| [44] | . |
| [45] | Aims: Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. |
| [46] | A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement. |
| [47] | Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of rey literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes. |
| [48] | 土地覆盖变化是陆地生态系统变化的重要组成部分与驱动因素。在全球变化、生态环境建设、经济高速发展等因素的影响下，21世纪前十年中国土地覆盖发生了显著变化，对此变化的监测和分析不但能支持中国碳源/汇的评估和碳收支估算，还可为生态环境变化评估提供基础数据。本研究在面向对象（object-based）的分类技术支持下，利用LandsatTM/ETM数据和HJ-1卫星数据，结合大量外业调查数据生产了30m分辨率的2000年、2010年中国土地覆盖数据（ChinaCover）；采用像元二分法生产了植被覆盖度数据。利用这两个数据集对中国土地覆盖10年的变化特点进行了分析。结果表明，人工表面和林地呈增加趋势，而耕地、湿地和草地面积呈减少的趋势；人工表面的快速增加和耕地面积的大规模减少是这一时期中国土地覆盖变化的最主要特点；土地覆盖类型转换中，耕地转换为人工表面的区域主要集中在我国中东部地区，耕地转换为林地和草地的区域主要分布在退耕还林还草的重点区域，耕地的扩张主要来自三江平原和新疆绿洲的农业开发。以植被覆盖度为评估指标显示森林、灌丛和草地质量总体呈上升趋势，但在汶川地震重灾区、横断山以及武夷山等局部地区的森林质量呈退化趋势；塔里木盆地周围、青藏高原东部、太行山、吕梁山等地区的灌丛植被覆盖度有所下降；内蒙古中部、青藏高原西南部、新疆天山南部、呼伦贝尔等地区的草地出现退化现象。 . 土地覆盖变化是陆地生态系统变化的重要组成部分与驱动因素。在全球变化、生态环境建设、经济高速发展等因素的影响下，21世纪前十年中国土地覆盖发生了显著变化，对此变化的监测和分析不但能支持中国碳源/汇的评估和碳收支估算，还可为生态环境变化评估提供基础数据。本研究在面向对象（object-based）的分类技术支持下，利用LandsatTM/ETM数据和HJ-1卫星数据，结合大量外业调查数据生产了30m分辨率的2000年、2010年中国土地覆盖数据（ChinaCover）；采用像元二分法生产了植被覆盖度数据。利用这两个数据集对中国土地覆盖10年的变化特点进行了分析。结果表明，人工表面和林地呈增加趋势，而耕地、湿地和草地面积呈减少的趋势；人工表面的快速增加和耕地面积的大规模减少是这一时期中国土地覆盖变化的最主要特点；土地覆盖类型转换中，耕地转换为人工表面的区域主要集中在我国中东部地区，耕地转换为林地和草地的区域主要分布在退耕还林还草的重点区域，耕地的扩张主要来自三江平原和新疆绿洲的农业开发。以植被覆盖度为评估指标显示森林、灌丛和草地质量总体呈上升趋势，但在汶川地震重灾区、横断山以及武夷山等局部地区的森林质量呈退化趋势；塔里木盆地周围、青藏高原东部、太行山、吕梁山等地区的灌丛植被覆盖度有所下降；内蒙古中部、青藏高原西南部、新疆天山南部、呼伦贝尔等地区的草地出现退化现象。 |
| [49] | |
| [50] | Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible for operational GLC mapping at 30 m resolution. |
| [51] | China : Open access to Earth land-cover map |
| [52] | Information about land cover and land use is a very important component of the planning process as it can contribute to the debate on the current arrangements and patterns and the need to modify land use as part of a regional plan, a resource development or management project, an environmental planning exercise, or as a baseline study of a region. Planners may seek to suggest modifications to land-use patterns to achieve some social or economic outcomes, or as part of an environmental conservation or sustainability project, or to avoid some predicted future unwanted consequences. Access to accurate land-use maps can assist planners and the enterprise of planning. It is in this context that remote sensing is able to contribute. The purpose of this monograph is to present an overview and critique of the growing field of remote sensing as it applies to the mapping and monitoring of land-cover and land-use at a range of spatial and temporal scales. The ability of remote sensing to contribute to the mandate of planners and managers has changed significantly over the last decade. Satellite data are now available that can be used to map and monitor change from continental to local scales and over daily to weekly temporal scales. With the recent launch of satellites capable of collecting data that is comparable to aerial platforms, there is an enhanced capability of identifying change at small spatial scales. Similarly, advances in image processing, database management and spatial analysis tools have enhanced our ability to analyse these data for depicting land-cover and land-use change. Here, remote sensing technologies are described along with methods of analysing remote sensing data for detecting change at local, regional and continental scales. It is this diverse range of scales of observation and analysis that are now key to mapping and monitoring both anthropogenic and natural, and dramatic and incremental change. These aspects are demonstrated using case studies with different objectives and applied at different scales. |
| [53] | The series of MODIS NDVI-derived crop maps generally had classification accuracies greater than 80%. Overall accuracies ranged from 94% for the general crop map to 84% for the summer crop map. The state-level crop patterns classified in the maps were consistent with the general cropping patterns across Kansas. The classified crop areas were usually within 1 5% of the USDA reported crop area for most classes. Sub-state comparisons found the areal discrepancies for most classes to be relatively minor throughout the state. In eastern Kansas, some small cropland areas could not be resolved at MODIS' 250m resolution and led to an underclassification of cropland in the crop/non-crop map, which was propagated to the subsequent crop classifications. Notable regional areal differences in crop area were also found for a few selected crop classes and locations that were related to climate factors (i.e., omission of marginal, dryland cropped areas and the underclassification of irrigated crops in western Kansas), localized precipitation patterns (overclassification of irrigated crops in northeast Kansas), and specific cropping practices (double cropping in southeast Kansas). |
| [54] | Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results. |
| [55] | In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have 30% of the world population and 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries. |
| [56] | Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data. |
| [57] | In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a non-parametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixel-based maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The object-based classification approach overcame the problem of salt-and-pepper effects found in classification results from traditional pixel-based approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computer-assisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present ground-based inventory of National Park lands. |
| [58] | This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested. |
| [59] | The area occupied by plastic-covered greenhouses has undergone rapid growth in recent years, currently exceeding 500,000 ha worldwide. Due to the vast amount of input (water, fertilisers, fuel, etc.) required, and output of different agricultural wastes (vegetable, plastic, chemical, etc.), the environmental impact of this type of production system can be serious if not accompanied by sound and sustainable territorial planning. For this, the new generation of satellites which provide very high resolution imagery, such as QuickBird and IKONOS can be useful. In this study, one QuickBird and one IKONOS satellite image have been used to cover the same area under similar circumstances. The aim of this work was an exhaustive comparison of QuickBird vs. IKONOS images in land-cover detection. In terms of plastic greenhouse mapping, comparative tests were designed and implemented, each with separate objectives. Firstly, the Maximum Likelihood Classification (MLC) was applied using five different approaches combining R, G, B, NIR, and panchromatic bands. The combinations of the bands used, significantly influenced some of the indexes used to classify quality in this work. Furthermore, the quality classification of the QuickBird image was higher in all cases than that of the IKONOS image. Secondly, texture features derived from the panchromatic images at different window sizes and with different grey levels were added as a fifth band to the R, G, B, NIR images to carry out the MLC. The inclusion of texture information in the classification did not improve the classification quality. For classifications with texture information, the best accuracies were found in both images for mean and angular second moment texture parameters. The optimum window size in these texture parameters was 3 3 for IK images, while for QB images it depended on the quality index studied, but the optimum window size was around 15 15. With regard to the grey level, the optimum was 128. Thus, the optimum texture parameter depended on the main objective of the image classification. If the main classification goal is to minimize the number of pixels wrongly classified, the mean texture parameter should be used, whereas if the main classification goal is to minimize the unclassified pixels the angular second moment texture parameter should be used. On the whole, both QuickBird and IKONOS images offered promising results in classifying plastic greenhouses. |
| [60] | 78 Decision tree modeling is suitable to identify crops at different field conditions. 78 Consideration of intra-class variations is required to improve classifications. 78 Textural features improve discrimination among heterogeneous permanent crops. 78 Information from NIR and SWIR bands is needed for detailed crop identification. 78 Crop identification requires the study of field status in distinct growing seasons. |
| [61] | Ship detection is an important application of global monitoring of environment and security. In order to overcome the limitations by other systems, surveillance with satellite synthetic aperture radar (SAR) is used because of its possibility to provide ship detection at high resolution over wide swaths and in all weather conditions. A new X-band radar onboard the TerraSAR-X (TS-X) satellite gives access to spatial resolution as fine as 1 m. In this paper, first results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (SatAIS) is presented. The AIS system is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS, as a space-based system, allows almost global coverage for monitoring of ships since not all ships operate their AIS and smaller ships are not equipped with AIS. The system is considered to be of cooperative nature. In this paper, the quality of TS-X images with respect to ship detection is evaluated, and a first assessment of its performance for ship detection is given. The velocity of a moving ship is estimated using complex TS-X data. As test cases, images were acquired over the North Sea, Baltic Sea, Atlantic Ocean, and Pacific Ocean in Stripmap mode with a resolution of 3 m at a coverage of 30 km 100 km. Simultaneous information on ship positions was available from TS-X and terrestrial as well as SatAIS. First results on the simultaneous superposition of SatAIS and high-resolution radar images are presented. |
| [62] | NOAA/NESDIS a initié le programme “Alaska SAR Demonstration” dont l'objectif est de faire la démonstration du potentiel des images RSO en bande C de RADARSAT-1 à fournir une information utile et en temps opportun sur l'environnement et pour la gestion des ressources pour des utilisateurs en Alaska. Un des produits développés dans le cadre du programme est une liste de localisations des navires. Cet article décrit l'algorithme développé pour générer ce produit par le biais de la détection automatique des navires basée sur des changements dans les statistiques locales. 08 l'aide d'images à basse résolution (100 mètres d'espacement), on démontre que l'on peut détecter des navires de dimension supérieure à 35 mètres (représentant 105 navires sur un total de 272 dans la zone test) avec un taux de fausse alerte de 0,01% pour une seule détection. Avec des images à haute résolution (50 mètres d'espacement), on peut détecter des navires d'une dimension supérieure à 32 mètres (représentant 124 navires sur 272) avec un taux de fausse alerte de 0,002% pour une seule détection. L'algorithme est entièrement automatisé et prend environ 10 minutes de temps-machine pour traiter une image ScanSAR en mode B large. |
| [63] | In this study, we have looked into the problem of vehicle detection in high-resolution satellite images. Based on the input from the local road authorities, we have focused not only on highways, but also on inner city roads, where more clutter is expected. The study site is the city of Oslo, Norway. To do vehicle detection in these areas, we propose an automatic approach, consisting of a segmentation step, followed by two stages of object classification. In the process, we utilize multispectral images, panchromatic images and a road network. The approach has been tested on Quickbird images, and the results that are obtained have been compared with manual counts and classifications. |
| [64] | 高光谱成像技术具有光谱分辨率高、图谱合一的独特优势,是遥感技术发展以来最重大的科技突破之一。中国的高光谱遥感发展与国际基本同步,在国家和省部级科研项目的支持下,解决了高光谱遥感信息机理、图像处理和多学科应用等方面多项世界性难题,有效解决了高光谱遥感理论研究与多领域应用中的关键技术瓶颈,实现了在农业、地矿、环境、文物保护等多领域的成功应用,产生了显著的社会经济效益。本文回顾了中国高光谱遥感技术的前沿研究进展,总结分析了取得的主要创新性成果。 . 高光谱成像技术具有光谱分辨率高、图谱合一的独特优势,是遥感技术发展以来最重大的科技突破之一。中国的高光谱遥感发展与国际基本同步,在国家和省部级科研项目的支持下,解决了高光谱遥感信息机理、图像处理和多学科应用等方面多项世界性难题,有效解决了高光谱遥感理论研究与多领域应用中的关键技术瓶颈,实现了在农业、地矿、环境、文物保护等多领域的成功应用,产生了显著的社会经济效益。本文回顾了中国高光谱遥感技术的前沿研究进展,总结分析了取得的主要创新性成果。 |
| [65] | Agriculture plays a critical role within Canada’s economy and, as such, sustainability of this sector is of high importance. Targeting and monitoring programs designed to promote economic and environmental sustainability are a vital component within Canada’s agricultural policy. A hierarchy of land information, including up to date information on cropping practices, is needed to measure the impacts of programs on land use decision-making and to gauge the environmental and economic benefits of these investments. A multi-year, multi-site research activity was completed to develop a robust methodology to inventory crops across Canada’s large and diverse agricultural landscapes. To move towards operational implementation the methodology must deliver accurate crop inventories, with consistency and reliability. In order to meet these operational requirements and to mitigate risk associated with reliance on a single data source, the methodology integrated both optical and Synthetic Aperture Radar (SAR) imagery. The results clearly demonstrated that multi-temporal satellite data can successfully classify crops for a variety of cropping systems present across Canada. Overall accuracies of at least 85% were achieved, and most major crops were also classified to this level of accuracy. Although multi-temporal optical data would be the preferred data source for crop classification, a SAR-optical dataset (two Envisat ASAR images and one optical image) provided acceptable accuracies and will mitigate risk associated with operational implementation. The preferred dual-polarization mode would be VV–VH. Not only were these promising classification results repeated year after year, but the target accuracies were met consistently for multiple sites across Canada, all with varying cropping systems. |
| [66] | This paper presents the use of time series of SAR images to map the flood temporal dynamics and the spatial distribution of vegetation over a large Amazonian floodplain. The region under study (3500 km 2) presents a diversity of landscape units with open lakes, bogs, large meadows, savannahs, alluvial forests and terra firma forest, covered by 21 images acquired by J-ERS between 1993 and 1997. Ground data include in situ observations of vegetation structure and flood extent as well as water level records. Image analysis demonstrates that temporal variations of the radar backscatter can be used to monitor efficiently the flood extent regardless of the landscape units. Also, analysis of the backscatter temporal variation greatly reduces the confusion between smooth surfaces (e.g. open water bodies, bare soils) inherent to L-band backscatter. The mapping method is based on decision rules over two decision variables: 1) the mean backscatter coefficient computed over the whole time series; 2) the total change computed using an bsolute Change estimator. The first variable provides classification into rough vegetation types while the second variable yields a direct estimate of the intensity of change that is related to flood dynamics. The classifier is first applied to the whole time series to map the maximum and minimum flood extent by defining 3 flood conditions: never flooded (NF); occasionally flooded (OF); permanently flooded (PF). It also furnishes the broad land cover type: open water/bare soils/low vegetation/forest. The accuracy of the flood extent mapping shows a kappa value of 0.82. Then, the classifier is run iteratively on the OF pixels to monitor flood stages during which the occasionally flooded areas get submerged. The mapping accuracy is assessed on one intermediate flood stage, showing a precision in excess of 90%. The importance of the time sampling for flood mapping is discussed along with the influence of SAR backscatter accuracy and the number of images. Then general guidelines for floodplain mapping are presented. By combining water level reports with maps of different flood stages the flooding pattern can be retrieved along with the vegetation succession processes. It is shown that the spatial distribution of vegetation communities is governed by flood stress and can be modelled as a function of the mean annual exposure to floods. |
| [67] | Rice monitoring and production estimation has special significance to China, as rice is the staple grain and accounts for 42% of the crop production in this country. Radar remote sensing is appropriate for monitoring rice because the areas where this crop is cultivated are often cloudy and rainy. Synthetic Aperture Radar (SAR) is thus anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions. It also provides revisit schedules suitable for agricultural monitoring. This paper presents the results of a study examining the backscatter behavior of rice as a function of time using multitemporal RADARSAT data acquired in 1996 and 1997. A rice-type distribution map was produced, showing four types of rice with different life spans ranging from 80 days to 120 125 days. The life span of a rice crop has significant impact on the yield, as well as on the taste and quality of the rice, with the longer growing varieties having the best taste and the highest productivity. The rice production of three counties and two administrative regions, totaling 5000 km 2, was estimated in this study. The accuracy of the rice classification was found to be 91% (97% after postclassification filtering) providing confidence that multitemporal RADARSAT data is capable of rice mapping. An empirical growth model was then applied to the results of the rice classification, which related radar backscatter values to rice life spans. These life spans could then be used to sum up the production estimates, which were obtained from agronomic models already in use for rice by local agronomists. These models related the yield of rice to their life span based on empirical observations for each type of rice. The resulting productivity estimate could not be compared to any other existing data on yield production for the study-area, but was well received by the local authorities. Based on the studies carried out in the Zhaoqing test site since 1993, it is suggested that rice production estimates require three radar data acquisitions taken at three different stages of crop growth and development. These three growth stages are: at the end of the transplanting and seedling development period, during the ear differentiation period, and at the beginning of the harvest period. Alternatively, if multiparameter radar data is available, only two data acquisitions may be needed. These would be at the end of the transplanting and seedling development period, and at the beginning of the harvest period. This paper also proposes an operational scenario for rice monitoring and production estimation. |
| [68] | Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends. As a result, comparison of the different land cover maps is difficult and information about their relative utility is limited. In an attempt to compare the datasets and assess their strengths and weaknesses we harmonized the thematic legends of four available coarse-resolution global land cover maps (IGBP DISCover, UMD, MODIS 1-km, and GLC2000) using the LCCS-based land cover legend translation protocols. Analysis of the agreement among the global land cover maps and existing validation information highlights general patterns of agreement, inconsistencies and uncertainties. The thematic classes of Evergreen broadleaf trees, Snow and Ice, and Barren show high producer and user accuracy and good agreement among the datasets, while classes of mixed tree types show high commission errors. Overall, the results show a limited ability of the four global products to discriminate mixed classes characterized by a mosaic of trees, shrubs, and herbaceous vegetation. There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes. Suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes. |
| [69] | |
| [70] | One of the relevant processes driven by political discussion under the United Framework Convention on Climate Change is the monitoring of Essential Climate Variables. Land Cover is one of those variables and efforts are therefore to be made to develop land cover observation approaches which meet the climate modelling community expectations. This paper aims at contributing to this necessity. First, consultation mechanisms were established with the climate modelling community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization and products accuracy, stability and consistency that are currently not met. Based on this outcome, the paper calls into question the current land cover representation and for revisiting global land cover mapping approaches. Increasing the flexibility of current classification systems and making the mapping techniques less sensitive to the period of observation are proposed as two key aspects to enhance the usability of global land cover dataset. |
| [71] | |
| [72] | The National Geomatics Center of China (NGCC) produced Global Land Cover (GlobalLand30) maps with 30 m spatial resolution for the years 2000 and 2009 2010, responding to the need for harmonized, accurate, and high-resolution global land cover data. This study aims to assess the mapping accuracy of the land surface water layer of GlobalLand30 for 2009 2010. A representative Mediterranean region, situated in Greece, is considered as the case study area, with 2009 as the reference year. The assessment is realized through an object-based comparison of the GlobalLand30 water layer with the ground truth and visually interpreted data from the Hellenic Cadastre fine spatial resolution (0.5 m) orthophoto map layer. GlobCover 2009, GlobCorine 2009, and GLCNMO 2008 corresponding thematic layers are utilized to show and quantify the progress brought along with the increment of the spatial resolution, from 500 m to 300 m and finally to 30 m with the newly produced GlobalLand30 maps. GlobalLand30 detected land surface water areas show a 91.9% overlap with the reference data, while the coarser resolution products are restricted to lower accuracies. Validation is extended to the drainage network elements, i.e., rivers and streams, where GlobalLand30 outperforms the other global map products, as well. |
| [73] | Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing. |
| [74] | |
| [75] | . J.-Y. Girard presents the evolution of Proof Theory during the second half of the 20th century. His introduction replaces in their historical context the works of Godel, Herbrand and Gentzen as well as the constructivist tradition (Brouwer's intuitions), the introduction by |
| [76] | The vegetation index, transformed vegetation index, and square root of the IR/red ratio were the most significant, followed closely by the IR/red ratio. Less than a 6% difference separated the highest and lowest of these four ER and red linear combinations. The use of these linear combinations was shown to be sensitive primarily to the green leaf area or green leaf biomass. As such, these linear combinations of the red and photographic IR radiances can be employed to monitor the photosynthetically active biomass of plant canopies. |
| [77] | A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton ( Gossypium hirsutum L. var DPI-70) and range grass ( Eragrostics lehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple lobal that can describe dynamic soil-vegetation systems from remotely sensed data. |
| [78] | Abstract In aircraft and satellite multispectral scanner data, soil background signals are superimposed on or intermingled with information about vegetation. A procedure which accounts for soil background would, therefore, make a considerable contribution to an operational use of Landsat and other spectral data for monitoring the productivity of range, forest, and crop lands. A description is presented of an investigation which was conducted to obtain information for the development of such a procedure. The investigation included a study of the soil reflectance that supplies the background signal of vegetated surfaces. Landsat data as recorded on computer compatible tapes were used in the study. The results of the investigation are discussed, taking into account a study reported by Kauth and Thomas (1976). Attention is given to the determination of Kauth's plane of soils, sun angle effects, vegetation index modeling, and the evaluation of vegetation indexes. Graphs are presented which show the results obtained with a gray mapping technique. The technique makes it possible to display plant, soil, water, and cloud conditions for any Landsat overpass. |
| [79] | 作物残茬作为农田生态系统的重要组成部分， 影响着农田生态系统中的营养物质、 碳、 水和能量的流动与循环。 作物残茬覆盖度作为描述作物残茬数量和分布的重要指标， 对于农田生态系统C循环和全球气候变化均有实际意义， 具备重要的定量监测价值。 遥感技术具有准确、 经济、 快速大面积监测的能力， 因此利用遥感监测区域尺度的作物残茬覆盖度， 受到国内外****的关注。 工作回顾总结了目前利用遥感监测作物残茬覆盖度的主要方法和最新研究进展， 并根据基本方法的差异以及数据源的不同， 从五个类别分别介绍了遥感监测原理与技术革新， 对每一类方法的优点和缺陷进行分析， 并提出了相应的改进措施。 最后对作物残茬覆盖度遥感监测方法的发展趋势进行了展望。 . 作物残茬作为农田生态系统的重要组成部分， 影响着农田生态系统中的营养物质、 碳、 水和能量的流动与循环。 作物残茬覆盖度作为描述作物残茬数量和分布的重要指标， 对于农田生态系统C循环和全球气候变化均有实际意义， 具备重要的定量监测价值。 遥感技术具有准确、 经济、 快速大面积监测的能力， 因此利用遥感监测区域尺度的作物残茬覆盖度， 受到国内外****的关注。 工作回顾总结了目前利用遥感监测作物残茬覆盖度的主要方法和最新研究进展， 并根据基本方法的差异以及数据源的不同， 从五个类别分别介绍了遥感监测原理与技术革新， 对每一类方法的优点和缺陷进行分析， 并提出了相应的改进措施。 最后对作物残茬覆盖度遥感监测方法的发展趋势进行了展望。 |
| [80] | // ABSTRACT Most remote sensing estimations of vegetation variables such as Leaf Area Index (LAI), Absorbed Photosynthetically Active Radiation (APAR), and phytomass are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. A study directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of APAR in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter is presented. A high spectral resolution method defined as the Chlorophyll Absorption Ratio Index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of APAR. CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of LAI were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI obtained from the simulated canopy reflectance was compared with the broad band vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR)). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy APAR more effectively than broad band vegetation indices. |
| [81] | Successful application of the normalized difference vegetation index (NDVI) for estimating weather impacts on vegetation is currently hindered in non-homogeneous areas. The problem is that the differences between the level of vegetation in these areas can be related, in addition to weather impacts, to the differences in geographic resources (climate, soil, vegetation type and topography). These... |
| [82] | Droughts occur frequently in China and their real-time monitoring and timely reporting are required for prevention and mitigation. This paper presents a method for developing an operational drought monitoring system for China. The method is based on various components such as Moderate Resolution Imaging Spectroradiometer data access, data processing, indices calculations, drought monitoring and analysis, and information dissemination. The system was tested by monitoring drought conditions in the early spring of 2009 in the Hai Basin of China. Results were compared with thein situdata-based indices. It was found that the system was capable of monitoring spatial variation in vegetation conditions attributed to droughts. The traditional meteorological drought index and yield data were collected to evaluate the system performance. A stronger relationship was found between the vegetation health index and the three-month standard precipitation index for the rainfed cropped areas. The relationship between the drought-area percentage and the winter wheat yield reduction percentage for 16 counties was stronger for the April ay period than for the February arch period. The drought monitoring system could explain about 60% of the variance in the winter wheat yields. |
| [83] | 目前用于中国干旱监测的遥感方法大多是可见光和热红外指数法，受云雨、植被和地形的影响较大，不能满足中国南方地区干旱监测的需求。该研究基于被动微波辐射传输方程，首先构建了基于AMSR-E（advanced microwave scanning radiometer-EOS）数据的地表温度反演模型，R2=0.79，RMSE（root mean square error）为2.54℃，实现了中国地表温度的被动微波遥感监测。然后，拟合了不同下垫面归一化植被指数（normalized difference vegetation index，NDVI）与微波极化差异指数（microwave polarization difference index，MPDI）的关系。在此基础上改进了植被供水指数（vegetation supply water index，VSWI），构建了基于AMSR-E数据的被动微波遥感气象干旱指数，并以中国2009年的旱情为例进行实例验证。研究表明，该干旱指数与AMSR-E L3土壤湿度数据有着显著的负相关关系（R2=0.75），且能基本表征2009年中国实际的气象干旱状况。 . 目前用于中国干旱监测的遥感方法大多是可见光和热红外指数法，受云雨、植被和地形的影响较大，不能满足中国南方地区干旱监测的需求。该研究基于被动微波辐射传输方程，首先构建了基于AMSR-E（advanced microwave scanning radiometer-EOS）数据的地表温度反演模型，R2=0.79，RMSE（root mean square error）为2.54℃，实现了中国地表温度的被动微波遥感监测。然后，拟合了不同下垫面归一化植被指数（normalized difference vegetation index，NDVI）与微波极化差异指数（microwave polarization difference index，MPDI）的关系。在此基础上改进了植被供水指数（vegetation supply water index，VSWI），构建了基于AMSR-E数据的被动微波遥感气象干旱指数，并以中国2009年的旱情为例进行实例验证。研究表明，该干旱指数与AMSR-E L3土壤湿度数据有着显著的负相关关系（R2=0.75），且能基本表征2009年中国实际的气象干旱状况。 |
| [84] | 61We developed a new drought index MIDI based on satellite multi-sensor microwave data.61Used in-situ drought index SPI to assess MIDI in semi-arid northern China.61MIDI was reliable in monitoring short-term drought, especially meteorological drought.61Droughts detected by MIDI and SPI had similar spatial pattern and temporal variation. |
| [85] | In previous studies of the universal pattern decomposition method (UPDM), the band width has been used to calculate standard spectral pattern vectors, without consideration of the effect of spectral response functions (SRFs). This study revised the UPDM to further reduce sensor dependence, by taking into account the effect of SRFs. Both the UPDM and the revised UPDM (RUPDM) were applied to MODIS and ETM+ ata acquired over the Three Gorges region of China. The reconstruction accuracy was significantly greater when the RUPDM was used, with a relative decrease in the mean 2 of more than 14%. Using the new method, the dependence of the decomposition coefficients and vegetation index (VIUPD) on the sensor also decreased, with their linear regression factors approximately equal to one. These increases in accuracy indicate that the RUPDM further reduces sensor dependence and hence can outperform the UPDM in data retrieval. |
| [86] | The vegetation indices from hyperspectral data have been shown to be effective for indirect monitoring of plant diseases. However, a limitation of these indices is that they cannot distinguish different diseases on crops. We aimed to develop new spectral indices (NSIs) that would be useful for identifying different diseases on crops. Three different pests (powdery mildew, yellow rust, and aphids) in winter wheat were used in this study. The new optimized spectral indices were derived from a weighted combination of a single band and a normalized wavelength difference of two bands. The most and least relevant wavelengths for different diseases were first extracted from leaf spectral data using the RELIEF-F algorithm. Reflectance of a single band extracted from the most relevant wavelengths and the normalized wavelength difference from all possible combinations of the most and least relevant wavelengths were used to form the optimized spectral indices. The classification accuracies of these new indices for healthy leaves and leaves infected with powdery mildew, yellow rust, and aphids were 86.5%, 85.2%, 91.6%, and 93.5%, respectively. We also applied these NSIs for nonimaging canopy data of winter wheat, and the classification results of different diseases were promising. For the leaf scale, the powdery mildew-index (PMI) correlated well with the disease index (DI), supporting the use of the PMI to invert the severity of powdery mildew. For the canopy scale, the detection of the severity of yellow rust using the yellow rust-index (YRI) showed a high coefficient of determination ( \mbiR2 = 0.86) between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application. |
| [87] | Remote sensing can be the most effective means of scaling up grassland aboveground biomass (AGB) from the sample scale to the regional scale. Among the remote-sensing approaches, statistical models based on the vegetation index (VI) are frequently used to retrieve grassland AGB because of their simplicity and reliability. However, these types of models have never been comprehensively optimized to overcome VI insensitivity and soil effects. Because grassland AGB is related to grassland type, in our research the integrated orderly classification system for grassland (IOCSG) was used to differentiate grassland types. The study area, located in Inner Mongolia, China, included desert steppe, typical steppe and meadow steppe. A pure VI (PVI) was extracted from the normal VI using spectral mixture analysis (SMA). Using a proportional relationship, PVI models were then constructed based on grassland type. The results demonstrated that the PVI models can have clear advantages over the more commonly used VI models. They simplify the parameterization of VI models and thus enhance models constructed for different regions with different remote sensing data sources. Notably, detailed differentiation of grassland types can improve the accuracy of AGB estimates. The methodology proposed in this study is particularly beneficial for AGB estimates at a national scale, especially for countries such as China with many grassland types. |
| [88] | 以美国内布拉斯加为例,按照耕地灌溉比例0%—30%,30%—60%,60%—100%将农业区分为雨养农业区、混合农业区与灌溉农业区,同时筛选丰水年(2008年)、平水年(2005年)、枯水年(2012年),比较相同年份雨养农业区、混合农业区与灌溉农业区的作物长势的峰值特征差异,以及相同农业区在丰水年、平水年、枯水年的长势过程线的相似性,并定量分析作物长势随灌溉百分比的变化规律与趋势。研究表明:(1)相同年份,灌溉农业区作物长势好于混合农业区,混合农业区的作物长势好于雨养农业区,耕地灌溉比例越高,作物长势越好;(2)不同年份的灌溉农业区作物长势差异最小,混合农业区次之,雨养农业区长势差异最大,即耕地灌溉比例越高,作物长势越稳定;(3)枯水年雨养农业区的作物长势过程线与降水过程线同增同减,受灌溉与降水的双重影响,灌溉农业区的作物长势过程线的峰值滞后于降水峰值;丰水年,作物水分胁迫减弱,灌溉农业区、混合农业区与雨养农业区作物长势过程线与降水过程线变化趋势基本一致;(4)作物长势增幅与灌溉百分比之间呈现显著的分段二次函数变化关系,当灌溉百分比增幅小于60%时,作物长势增长幅度逐步加快,当灌溉百分比大于60%时,作物长势增速逐步放缓,在枯水年时,长势随灌溉百分比增加而增长的幅度高于丰水年与枯水年。鉴于不同农业区作物长势差异,作物长势的定量监测需要进一步区分灌溉与雨养农业。 . 以美国内布拉斯加为例,按照耕地灌溉比例0%—30%,30%—60%,60%—100%将农业区分为雨养农业区、混合农业区与灌溉农业区,同时筛选丰水年(2008年)、平水年(2005年)、枯水年(2012年),比较相同年份雨养农业区、混合农业区与灌溉农业区的作物长势的峰值特征差异,以及相同农业区在丰水年、平水年、枯水年的长势过程线的相似性,并定量分析作物长势随灌溉百分比的变化规律与趋势。研究表明:(1)相同年份,灌溉农业区作物长势好于混合农业区,混合农业区的作物长势好于雨养农业区,耕地灌溉比例越高,作物长势越好;(2)不同年份的灌溉农业区作物长势差异最小,混合农业区次之,雨养农业区长势差异最大,即耕地灌溉比例越高,作物长势越稳定;(3)枯水年雨养农业区的作物长势过程线与降水过程线同增同减,受灌溉与降水的双重影响,灌溉农业区的作物长势过程线的峰值滞后于降水峰值;丰水年,作物水分胁迫减弱,灌溉农业区、混合农业区与雨养农业区作物长势过程线与降水过程线变化趋势基本一致;(4)作物长势增幅与灌溉百分比之间呈现显著的分段二次函数变化关系,当灌溉百分比增幅小于60%时,作物长势增长幅度逐步加快,当灌溉百分比大于60%时,作物长势增速逐步放缓,在枯水年时,长势随灌溉百分比增加而增长的幅度高于丰水年与枯水年。鉴于不同农业区作物长势差异,作物长势的定量监测需要进一步区分灌溉与雨养农业。 |
| [89] | The monitoring of vegetation in Southern Africa with satellite data has become increasingly important over the past decade because it is linked to variation in agricultural production and climate change with implications for wildlife management and tourism. This study shows how maps of vegetation status were produced in near real time from NOAA images acquired from the local receiving stations in Etosha National Park, Namibia and in Zambia. Map products based on the NDVI were put into historical context and stratified to remove effects of the main vegetation types in order to assess vegetation status. The historical data were extracted from the FAO ARTEMIS NDVI archive and processed to obtain a statistical distribution of the NDVI for each 10-day period of the year and vegetation type by applying techniques commonly used in hydrology for the prediction of extreme events. The quintile probability ranges were used to define five classes of a Vegetation Productivity Indicator (VPI). LAC NDVI images obtained in real-time from the receiving station were processed to derive a VPI map for each 10-day period. In Etosha National Park and in Zambia, the VPI was strongly related to the rainfall and the VPI maps provided improved information on the spatial variations. The weighted average VPI for the main agricultural region of Zambia was significantly correlated with maize production. |
| [90] | 61Best scalar performance ever attained in space.61Burst mode: powerful tool to assess the instruments07 performance and good health.61Vector mode experiment successful.61Simultaneous delivery of absolute scalar and vector data, a world first.61Geomagnetic field models built successfully using ASM data only. |
| [91] | Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes. |
| [92] | Drought is one of the most frequent climate-related disasters occurring across large portions of the African continent, often with devastating consequences for the food security of agricultural households. This study proposes a novel method for calculating the empirical probability of having a significant proportion of the total agricultural area affected by drought at sub-national level. First, we used the per-pixel Vegetation Health Index (VHI) from the Advanced Very High Resolution Radiometer (AVHRR) averaged over the crop season as main drought indicator. A phenological model based on NDVI was employed for defining the start of season (SOS) and end of the grain filling stage (GFS) dates. Second, the per-pixel average VHI was aggregated for agricultural areas at sub-national level in order to obtain a drought intensity indicator. Seasonal VHI averaging according to the phenological model proved to be a valid drought indicator for the African continent, and is highly correlated with the drought events recorded during the period (1981 2009). The final results express the empirical probability of drought occurrence over both the temporal and the spatial domain, representing a promising tool for future drought monitoring. |
| [93] | . |
| [94] | AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 802km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 102km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000–2007) has been compared to Terra MODIS (102km and 802km resampled) and SPOT VGT NDVI data 102km (2000–2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002–2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel–Sudanian areas receiving < 100002mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian–Guinean zones should be done with certain reservations. |
| [95] | 78 Accuracy of GIMMS NDVI was assessed using Terra MODIS monthly NDVI 2000–2010. 78 Overall trends of GIMMS NDVI were in acceptable agreement with Terra MODIS NDVI. 78 Humid/arctic area discrepancies due to lack of high quality data from both sensors. 78 Arid area discrepancies due to lower GIMMS sensitivity for low chlorophyll areas. 78 GIMMS NDVI is found to include residual influence from land cover class data. |
| [96] | Leaf area index () is a crucial biophysical parameterthat is indispensable for many biophysical and climatic models.A neural network algorithm in conjunction with extensive canopyand atmospheric radiative transfer simulations is presented in thispaper to estimateLAIfromLandsat-7 Enhanced ThematicMapperPlus data. Two schemes were explored; the first was based on surfacereflectance, and the second on top-of-atmosphere (TOA) radiance.The implication of the second scheme is that atmosphericcorrections are not needed for estimating the surface LAI. A soilreflectance index (SRI) was proposed to account for variable soilbackground reflectances. Ground-measured LAI data acquired atBeltsville, MD were used to validate both schemes. The results indicatethat both methods can be used to estimate LAI accurately.The experiments also showed that the use of SRI is very critical. |
| [97] | The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels. |
| [98] | The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the “main” algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the “backup” algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004–2005 and 2005–2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0–25°; 25–45°; 45–60°) and development stages (90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models. |
| [99] | The fraction of intercepted photosynthetic active radiation (fPAR) is a key variable used by the Monteith model to estimate the net primary productivity (NPP). This variable can be assessed by vegetation indices (VIs) derived from spectral remote sensing data but several factors usually affect their relationship. The objectives of this work were to analyse the fPAR dynamics and to describe the relationships between fPAR and several indices (normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), Green NDVI (GNDVI), visible atmospherically resistant index (VARI) green, VIgreen and red edge position (REP)) under different water and nutrient treatments for two species with different canopy architectures. Two C3 grass species with differences in leaf orientation (planophile and erectophile) were cultivated from seeds in pots. Four treatments were applied combining water and nitrogen availability. Every week, canopy reflectance and fPAR were measured. Aerial biomass was clipped to estimate final above-ground production for each species and treatment. Starting from reflectance values, the indices were calculated. Planophile species have a steeper (but not significantly) slope in VIs PAR relationships than the erectophile species. Water and nutrient deficiencies treatment showed no relationship with fPAR in any spectral index in the erectophile species. In the other species, this treatment showed significant relationship according to the index used. Analysing each species individually, treatments did not modify slopes except in one case (planophile species between both treatments with high nitrogen but differing in water availability). Among indices, GNDVI was the best estimator of fPAR for both species, followed by NDVI and OSAVI. Inaccurate results may be obtained from commonly reported spectral relationships if plants' stress factors are not taken into account. |
| [100] | |
| [101] | <p>湿地植被生物量是衡量湿地生态系统健康状况的重要指标，其估算方法研究一直是湿地领域的研究热点。传统的植被地上生物量测算主要依靠样方调查，对于复杂湿地生态系统存在一定的局限，而随着遥感估算方法的发展，湿地植被生物量的研究实现了长期、动态且大尺度的监测。本文在查阅和分析国内外相关文献的基础上，以遥感数据为主要数据源，阐述了基于光学、合成孔径雷达（SAR）、激光雷达（Lidar）以及多源协同遥感数据反演湿地植物地上生物量的理论基础及计算原理，总结了其研究进展，分析了其适用性，继而从湿地植被生物量监测类型的拓展、多源遥感数据的融合、遥感数据的同化以及遥感机理模型发展等方面出发，对植物生物量研究的发展趋势进行了深入探讨。</p> . <p>湿地植被生物量是衡量湿地生态系统健康状况的重要指标，其估算方法研究一直是湿地领域的研究热点。传统的植被地上生物量测算主要依靠样方调查，对于复杂湿地生态系统存在一定的局限，而随着遥感估算方法的发展，湿地植被生物量的研究实现了长期、动态且大尺度的监测。本文在查阅和分析国内外相关文献的基础上，以遥感数据为主要数据源，阐述了基于光学、合成孔径雷达（SAR）、激光雷达（Lidar）以及多源协同遥感数据反演湿地植物地上生物量的理论基础及计算原理，总结了其研究进展，分析了其适用性，继而从湿地植被生物量监测类型的拓展、多源遥感数据的融合、遥感数据的同化以及遥感机理模型发展等方面出发，对植物生物量研究的发展趋势进行了深入探讨。</p> |
| [102] | 78 First meta-analysis of remote sensing of biomass 78 Lidar is shown to be more accurate than other sensors. 78 Accuracy is a function of lidar type, biome type and plot size. 78 Results are discussed in the context of MRV. |
| [103] | Abstract Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees02≥02502cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter–height relationship depended linearly on a bioclimatic stress variable E , which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter–height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development. |
| [104] | 78 The BIOMASS mission aims to provide global forest biomass maps using P-band SAR images. 78 Forest biomass is derived using radar intensity, PolInSAR and tomographic techniques. 78 The mission will help quantify the terrestrial contribution to the global carbon cycle. |
| [105] | . |
| [106] | 为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求. . 为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求. |
| [107] | We develop and validate an automated approach to determine canopy height, an important metric for global biomass assessments, from micro-pulse photon-counting lidar data collected over forested ecosystems. Such a lidar system is planned to be launched aboard the National Aeronautics and Space Administration follow-on Ice, Cloud and land Elevation Satellite mission (ICESat-2) in 2017. For algorithm development purposes in preparation for the mission, the ICESat-2 project team produced simulated ICESat-2 data sets from airborne observations of a commercial micro-pulse lidar instrument (developed by Sigma Space Corporation) over two forests in the eastern USA. The technique derived in this article is based on a multi-step mathematical and statistical signal extraction process which is applied to the simulated ICESat-2 data set. First, ground and canopy surfaces are approximately extracted using the statistical information derived from the histogram of elevations for accumulated photons in 100 footprints. Second, a signal probability metric is generated to help identify the location of ground, canopy-top, and volume-scattered photons. According to the signal probability metric, the ground surface is recovered by locating the lowermost high-photon density clusters in each simulated ICESat-2 footprint. Thereafter, canopy surface is retrieved by finding the elevation at which the 95th percentile of the above-ground photons exists. The remaining noise is reduced by cubic spline interpolation in an iterative manner. We validate the results of the analysis against the full-resolution airborne photon-counting lidar data, digital terrain models (DTMs), and canopy height models (CHMs) for the study areas. With ground surface residuals ranging from 0.2 to 0.5 m and canopy height residuals ranging from 1.6 to 2.2 m, our results indicate that the algorithm performs very well over forested ecosystems of canopy closure of as much as 80%. Given the method success in the challenging case of canopy height determination, it is readily applicable to retrieval of land ice and sea ice surfaces from micro-pulse lidar altimeter data. These results will advance data processing and analysis methods to help maximize the ability of the ICESat-2 mission to meet its science objectives. |
| [108] | Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes. |
| [109] | |
| [110] | |
| [111] | Remote sensing of plant water content is difficult because the absorption band sensitive to foliar liquid water is also sensitive to the atmospheric vapour. A method using non-water-absorption spectral parameters to evaluate plant water content (PWC) would be valuable. In our experiment, canopy spectra of 48 winter wheat treatments with different varieties, different fertilization and irrigation levels were measured by an ASD FieldSpec FR spectrometer in six different growth stages from erecting stage to milking stage, and the PWCs of the related wheat plant samples were also measured. Significant positive coefficients of correlation were observed between PWC and spectral reflectance in 740-930 nm region in all of the six different growth stages, which indicates that the NIR spectral |
| [112] | Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage. |
| [113] | <p>土壤水分是影响地表过程的核心变量之一。精准地测量土壤水分及其时空分布，长期以来是定量遥感研究领域的难点问题。简要回顾基于光学、被动微波、主动微波和多传感器联合反演等卫星遥感反演土壤水分的主要反演算法、存在的难点和前沿性研究问题，介绍了应用土壤水分反演算法所形成的3种主要全球土壤水分数据集，包括欧洲气象业务卫星(ERS/ MetOp)数据集、高级微波扫描辐射计（AMSR-E）数据集、土壤湿度与海洋盐分卫星（SMOS）数据集，并结合目前存在的问题探讨卫星遥感反演土壤水分研究的发展趋势。</p> . <p>土壤水分是影响地表过程的核心变量之一。精准地测量土壤水分及其时空分布，长期以来是定量遥感研究领域的难点问题。简要回顾基于光学、被动微波、主动微波和多传感器联合反演等卫星遥感反演土壤水分的主要反演算法、存在的难点和前沿性研究问题，介绍了应用土壤水分反演算法所形成的3种主要全球土壤水分数据集，包括欧洲气象业务卫星(ERS/ MetOp)数据集、高级微波扫描辐射计（AMSR-E）数据集、土壤湿度与海洋盐分卫星（SMOS）数据集，并结合目前存在的问题探讨卫星遥感反演土壤水分研究的发展趋势。</p> |
| [114] | |
| [115] | Hyperspectral reflectance (438 to 884 nm) data were recorded at five different growth stages of winter wheat in a field experiment including two cultivars, three plant densities, and four levels of N application. All two-band combinations in the normalized difference vegetation index ( λ161 λ2)/( λ1+ λ2) were subsequently used in a linear regression analysis against green biomass (GBM, g fresh weight m 612 soil), leaf area index (LAI, m 2 green leaf m 612 soil), leaf chlorophyll concentration (Chl conc, mg chlorophyll g 611 leaf fresh weight), leaf chlorophyll density (Chl density, mg chlorophyll m 612 soil), leaf nitrogen concentration (N conc, mg nitrogen g 611 leaf dry weight), and leaf nitrogen density (N density, g nitrogen m 612 soil). A number of grouped wavebands with high correlation ( R 2>95%) were revealed. For the crop variables based on quantity per unit surface area, i.e. GBM, LAI, Chl density, and N density, these wavebands had in the majority (87%) of the cases a center wavelength in the red edge spectral region from 680 to 750 nm and the band combinations were often paired so that both bands were closely spaced in the steep linear shift between R red and R nir. The red edge region was almost absent for bands related to Chl conc and N conc, where the visible spectral range, mainly in the blue region, proved to be better. The selected narrow-band indices improved the description of the influence of all six-crop variables compared to the traditional broad- and short-band indices normally applied on data from satellite, aerial photos, and field spectroradiometers. For variables expressed on the basis of soil or canopy surface area, the relationship was further improved when exponential curve fitting was used instead of linear regression. The best of the selected narrow-band indices was compared to the results of a partial least square regression (PLS). This comparison showed that the narrow-band indices related to LAI and Chl conc, and to some extent also Chl density and N density, were optimal and could not be significantly improved by PLS using the information from all wavelengths in the hyperspectral region. However, PLS improved the prediction of GBM and N conc by lowering the RMSE with 22% and 24%, respectively, compared to the best narrow-band indices. It is concluded that PLS regression analysis may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data. |
| [116] | An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively |
| [117] | . During the four years of the AgRISTARS Program, significant progress was made in quantifying the capabilities of microwave sensors for the remote sensing of soil moisture. In this paper we discuss the results of numerous field and aircraft experiments, analysis of spacecraft data, and modeling activities which examined the various noise factors such as roughness and vegetation that affect the interpretability of microwave emission measurements. While determining that a 21-cm wavelength radiometer was the best single sensor for soil moisture research, these studies demonstrated that a multisensor approach will provide more accurate soil moisture information for a wider range of naturally occurrring conditions. |
| [118] | Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available. |
| [119] | Satellite remote sensing is a promising technique for estimating global or regional evapotranspiration (ET). A simple and accurate method is essential when estimating ET using remote sensing data. Such a method is investigated by taking advantage of satellite measurements and the extensive ground-based measurements available at eight enhanced surface facility sites located throughout the Southern Great Plains (SGP) area of the United States from January 2002 to May 2005. Data analysis shows that correlation coefficients between ET and surface net radiation are the highest, followed by temperatures (air temperature or land surface temperature, T), and vegetation indices (enhanced vegetation index (EVI) or normalized difference vegetation index (NDVI)). A simple regression equation is proposed to estimate ET using surface net radiation, air or land surface temperatures and vegetation indices. ET can be estimated using daytime-averaged air temperature and EVI with a root mean square error (RMSE) of 藴30 W mand a correlation coefficient of 0.91 across all sites and years. ET can also be estimated with comparable accuracy using NDVI and T. More importantly, the daytime-averaged ET can also be estimated using only one measurement per day of temperatures (the daytime maximum air temperature or T) with comparable accuracy. A sensitivity analysis shows that the proposed method is only slightly sensitive to errors of temperatures, vegetation indices and net surface radiation. An independent validation was made using the measurements colleted by the eddy covariance method at six AmeriFlux sites throughout the United States from 2001 to 2006. The land cover associated with the AmeriFlux sites varies from grassland, to cropland and forest. The results show that ET can be reasonably predicted with a correlation coefficient that varies from 0.84 to 0.95 and a bias that varies from 3 W mto 15 W mand RMSE varying from 30 W mto 40 W m. The positive bias partly comes from the energy imbalance problem encountered in the eddy covariance method. The proposed method can predict ET under a wide range of soil moisture contents and land cover types. |
| [120] | <p>湖面高程与面积变化对区域气候变化响应敏感, 二者结合可用来估算湖泊水量平衡. 本研究利用ICESat 和Landsat 数据, 对中国最大的10 个湖泊2003~2009 年的高程、面积和体积变化进行了研究. 结果表明, 青藏高原地区色林错、纳木错、青海湖和中国东北的兴凯湖显示出湖面高程增加, 色林错为10 个湖泊中水面高程(0.69 m/a)、面积(32.59 km<sup>2</sup>/a)和体积(1.25 km<sup>3</sup>/a)升高最快的湖泊; 中国北部干旱和半干旱地区的博斯腾湖和呼伦湖表现了湖面高程与面积的下降, 博斯腾湖则显示了最快的湖面高程下降(-0.43 m/a), 呼伦湖面积收缩最大(-35.56 km<sup>2</sup>/a). 长江中下游地区的洞庭湖、鄱阳湖、太湖和洪泽湖湖面高程与面积则呈现出明显的季节变化, 但总的变化趋势不明显. 色林错、纳木错、青海湖、鄱阳湖、呼伦湖和博斯腾湖的湖面高程与面积表现出高的相关性(<em>R</em><sup>2</sup>>0.80), 太湖、洪泽湖和兴凯湖相关性中等(<em>R</em><sup>2</sup>≈0.70), 东洞庭湖相关性较小(<em>R</em><sup>2</sup>=0.37).所有湖泊的高程变化与体积变化表现出较高的相关性(<em>R</em><sup>2</sup>>0.98). 根据湖泊高程与面积变化, 对其水量平衡进行了估算, 色林错、纳木错、青海湖和兴凯湖表现出正平衡, 分别为9.08, 4.07, 2.88和1.09 km<sup>3</sup>; 博斯腾湖和呼伦湖则显示出负平衡, 分别为-3.01 和-4.73 km<sup>3</sup>. 另外, 根据湖面高程变化的特点, 选择湖面升高、下降和无明显趋势的代表性湖泊, 对其变化的原因进行了分析. 此研究表明, 可利用遥感卫星数据快速有效地估算湖泊的水量平衡.</p> . <p>湖面高程与面积变化对区域气候变化响应敏感, 二者结合可用来估算湖泊水量平衡. 本研究利用ICESat 和Landsat 数据, 对中国最大的10 个湖泊2003~2009 年的高程、面积和体积变化进行了研究. 结果表明, 青藏高原地区色林错、纳木错、青海湖和中国东北的兴凯湖显示出湖面高程增加, 色林错为10 个湖泊中水面高程(0.69 m/a)、面积(32.59 km<sup>2</sup>/a)和体积(1.25 km<sup>3</sup>/a)升高最快的湖泊; 中国北部干旱和半干旱地区的博斯腾湖和呼伦湖表现了湖面高程与面积的下降, 博斯腾湖则显示了最快的湖面高程下降(-0.43 m/a), 呼伦湖面积收缩最大(-35.56 km<sup>2</sup>/a). 长江中下游地区的洞庭湖、鄱阳湖、太湖和洪泽湖湖面高程与面积则呈现出明显的季节变化, 但总的变化趋势不明显. 色林错、纳木错、青海湖、鄱阳湖、呼伦湖和博斯腾湖的湖面高程与面积表现出高的相关性(<em>R</em><sup>2</sup>>0.80), 太湖、洪泽湖和兴凯湖相关性中等(<em>R</em><sup>2</sup>≈0.70), 东洞庭湖相关性较小(<em>R</em><sup>2</sup>=0.37).所有湖泊的高程变化与体积变化表现出较高的相关性(<em>R</em><sup>2</sup>>0.98). 根据湖泊高程与面积变化, 对其水量平衡进行了估算, 色林错、纳木错、青海湖和兴凯湖表现出正平衡, 分别为9.08, 4.07, 2.88和1.09 km<sup>3</sup>; 博斯腾湖和呼伦湖则显示出负平衡, 分别为-3.01 和-4.73 km<sup>3</sup>. 另外, 根据湖面高程变化的特点, 选择湖面升高、下降和无明显趋势的代表性湖泊, 对其变化的原因进行了分析. 此研究表明, 可利用遥感卫星数据快速有效地估算湖泊的水量平衡.</p> |
| [121] | In this paper, percent vegetation cover is estimated from vegetation indices using simulated Advanced Very High Resolution Radiometer (AVHRR) data derived from in situ spectral reflectance data. Spectral reflectance measurements were conducted on grasslands in Mongolia and Japan. Vegetation indices such as the normalized difference, soil-adjusted, modified soil-adjusted and transformed soil-adjusted vegetation indices (NDVI, SAVI, MSAVI and TSAVI) were calculated from the spectral reflectance of various vegetation covers. Percent vegetation cover was estimated using pixel values of red, green and blue bands of digitized colour photographs. Relationships between various vegetation indices and percent vegetation cover were compared using a second-order polynomial regression. TSAVI and NDVI gave the best estimates of vegetation cover for a wide range of grass densities. |
| [122] | Imaging spectrometry has the potential to provide improved discrimination of crop types and better estimates of crop yield. Here we investigate the potential of Hyperion to discriminate three Brazilian soybean varieties and to evaluate the relationship between grain yield and 17 narrow-band vegetation indices. Hyperion analysis focused on two datasets acquired from opposite off-nadir viewing directions but similar solar geometry: one acquired on 08 February 2005 (forward scattering) and the other on 14 January 2006 (back scattering). In 2005, the soybean canopies were observed by Hyperion at later reproductive stages than in 2006. Additional Hyperion datasets were not available due to cloud cover. To further examine the impact of viewing geometry within the same season, Hyperion data were complemented by 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) images (bands 1 and 2) acquired in consecutive days (05 06 February 2005) with opposite viewing geometries ( 42 and + 44, respectively). MODIS data analysis was used to keep reproductive stage as a constant factor while isolating the impact of viewing geometry. For discrimination purposes, multiple discriminant analysis (MDA) was applied over each dataset using surface reflectance values as input variables and a stepwise procedure for band selection. All possible Hyperion band ratios and the 17 narrow-band vegetation indices with soybean grain yield were evaluated across years through Pearson's correlation coefficients and linear regression. MODIS-derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were evaluated within the same growing season. Results showed that: (1) the three soybean varieties were discriminated with highest accuracy in the back scattering direction, as deduced from MDA classification results from Hyperion and MODIS data; (2) the highest correlation between Hyperion vegetation indices and soybean yield was observed for the Normalized Difference Water Index (NDWI) ( r= + 0.74) in the back scattering direction and this result was consistent with band ratio analysis; (3) higher Hyperion correlation results were observed in the back scattering direction when compared to the forward scattering image. For the same reproductive stage, stronger shadowing effects were observed over the MODIS red band in the forward scattering direction producing lower and lesser variable reflectance for the sensor. As a result, the relationship between MODIS-derived NDVI and soybean yield improved from the forward ( r of + 0.21) to the back scattering view ( r of + 0.60). The same trend was observed for SR that increased from + 0.22 to + 0.58. |
| [123] | The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics. |
| [124] | 本文研究了星载微波SSM/Ⅰ1996年在中国东北华北平原农田上7个通道辐射亮度温度(<i>T</i><sub>B</sub>)的遥感数据, 提出用几个通道<i>T</i><sub>B</sub>组合的散射指数和极化指数来分析中国平原地区农田的微波辐射特征, 及其随生长季节的时间性变化。星载SSM/Ⅰ数据可以监视农作物的生长和平原地区地面湿度的变化。本文还给出了大气和农作物地表矢量辐射传输的数值模拟结果。 . 本文研究了星载微波SSM/Ⅰ1996年在中国东北华北平原农田上7个通道辐射亮度温度(<i>T</i><sub>B</sub>)的遥感数据, 提出用几个通道<i>T</i><sub>B</sub>组合的散射指数和极化指数来分析中国平原地区农田的微波辐射特征, 及其随生长季节的时间性变化。星载SSM/Ⅰ数据可以监视农作物的生长和平原地区地面湿度的变化。本文还给出了大气和农作物地表矢量辐射传输的数值模拟结果。 |
| [125] | Microwave brightness temperatures at five frequencies from 4.9 GHz to 94 GHz of an oat field were measured from seeding to harvest in 1989. The data were compared with observed soil and vegetation parameters. The reduction of the soil moisture sensitivity during the growth phase was quantitatively related to plant water content with data at 4.9 GHz. At higher frequencies the influence of vegetation is almost abruptly changing the emission with respect to the plant development. The different spectral and polarization behavior can be related to geometrical vegetation parameters such as leaf and panicle size and orientation. |
| [126] | |
| [127] | |
| [128] | Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation. |
| [129] | . |
| [130] | . A geometric-optical forest canopy model that treats conifers as cones casting shadows on a contrasting background can explain the major portion of the variance in a remotely sensed image of a forest stand. The model is driven by interpixel variance generated from three sources: 1) the number of crowns in the pixel; 2) the size of individual crowns; and 3) overlapping of crowns and shadows. The model uses parallel-ray geometry to describe the illumination of a three-dimensional cone and the shadow it casts. Cones are assumed to be randomly placed and may overlap freely. Cone size (height) is distributed lognormally, and cone form, described by the apex angle of the cone, is-fixed in the model but allowed to vary in its application. The model can also be inverted to provide estimates of the size, shape, and spacing of the conifers as cones using remote imagery and a minimum of ground measurements. Field tests using both 10-and 80-m multispectral imagery of two test conifer stands in northeastern California produced reasonable estimates for these parameters. The model appears to be sufficiently general and robust for application to other geometric shapes and mixtures of simple shapes. Thus it has wide potential use not only in remote sensing of vegetation, but also in other remote sensing situations in which discrete objects are imaged at resolutions sufficiently coarje that they canot be resolved individually. |
| [131] | In the case where a vegetation cover can be regarded as a collection of individual, discrete plant crowns, the geometric-optical effects of the shadows that the crowns cast on the background and on one another strongly condition the brightness of the... |
| [132] | 遥感BRDF物理模型均建立于一定的假设或基于某些理想状况，其模拟的数据与观测数据之间多少会存在一些差异（误差）。利用BRDF模型反演地表参数时，如果不加选择地使用所有观测数据，势必会影响模型参数反演的准确度。遥感反演时一般都采用代价函数进行参数拟合。经典的最小二乘（LS）拟合代价函数对正态分布误差具有一定的抗干扰性，但是当观测数据含有异常值时却会导致反演结果的不稳定。最小中值平方（LMS）方法具有鲁棒性特点，反演时若将其作为代价函数，则可以有效地检测出观测数据中含有的异常值，从而可以使模型反演准确度提高。本文以遥感BRDF物理模型――SAIL模型为例，使用模拟数据与真实地面观测数据，构建LMS与LS两种代价函数，分阶段地进行地表参数的反演方法研究。结果显示，针对具有一定误差或模型不能完全表示的观测数据，本文采用的分阶段方法可以对模型参数鲁棒地反演。 . 遥感BRDF物理模型均建立于一定的假设或基于某些理想状况，其模拟的数据与观测数据之间多少会存在一些差异（误差）。利用BRDF模型反演地表参数时，如果不加选择地使用所有观测数据，势必会影响模型参数反演的准确度。遥感反演时一般都采用代价函数进行参数拟合。经典的最小二乘（LS）拟合代价函数对正态分布误差具有一定的抗干扰性，但是当观测数据含有异常值时却会导致反演结果的不稳定。最小中值平方（LMS）方法具有鲁棒性特点，反演时若将其作为代价函数，则可以有效地检测出观测数据中含有的异常值，从而可以使模型反演准确度提高。本文以遥感BRDF物理模型――SAIL模型为例，使用模拟数据与真实地面观测数据，构建LMS与LS两种代价函数，分阶段地进行地表参数的反演方法研究。结果显示，针对具有一定误差或模型不能完全表示的观测数据，本文采用的分阶段方法可以对模型参数鲁棒地反演。 |
| [133] | 基于贝叶斯网络理论，建立用于植被地表参数估计的混合反演模式，结合遥感物理模型实现了冬小麦叶片叶绿素含量（Cab）和冠层叶面积指数（LAI）的反演。用模型模拟数据以及2001年顺义遥感实验数据验证结果表明，LAI和Cab均有较好的反演精度。针对含噪声模拟数据反演结果中约有10％的噪声数据反演失败的情况，用不确定知识的处理方法有效地降低了失败点的比例。混合反演模式本质上是一个融合先验知识与观测数据的知识推理方案，本文实现了对反演过程中参数后验概率更新算法并引入热力学中的信息熵概念实现了参数后验信息动态定量计算，同时简单探讨了现阶段定量评价遥感反演过程中信息流控制存在的难点问题。 . 基于贝叶斯网络理论，建立用于植被地表参数估计的混合反演模式，结合遥感物理模型实现了冬小麦叶片叶绿素含量（Cab）和冠层叶面积指数（LAI）的反演。用模型模拟数据以及2001年顺义遥感实验数据验证结果表明，LAI和Cab均有较好的反演精度。针对含噪声模拟数据反演结果中约有10％的噪声数据反演失败的情况，用不确定知识的处理方法有效地降低了失败点的比例。混合反演模式本质上是一个融合先验知识与观测数据的知识推理方案，本文实现了对反演过程中参数后验概率更新算法并引入热力学中的信息熵概念实现了参数后验信息动态定量计算，同时简单探讨了现阶段定量评价遥感反演过程中信息流控制存在的难点问题。 |
| [134] | This study evaluates the performance of a simple geometric-optics reflectance model, used in combination with multi-spectral clustering, to map spatial patterns of effective Leaf Area Index ( L e ) within Boreal Picea mariana stands. Two metre resolution Compact Airborne Spectrographic Imager (CASI) images, acquired during the winter to minimize variability in understory reflectance, are used to map L e over BOREAS northern and southern Old Black Spruce tower flux sites. A combined multi-spectral clustering and ray-tracing approach is used to map open areas in each site at 2 m scale. A modified version of the Forest Light Interaction Model (FLIM) is then applied over canopy areas using 30 m scale red and near-infrared reflectance values derived from CASI images. Comparison of the combined FLIM and clustering approach (FLIMCLUS) with surface L e measurements in areas with overstory cover indicate a R 2 of 0.67 for the SSA-OBS site and 0.16 for the NSA-OBS site. The poor NSAOBS performance may be due to the low observed range of L e along the transect selected since additional measurements in a sparse canopy area closely match FLIM estimates. The relative standard error at both sites is under 10% and is close to the 5% precision error in surface L e estimates. Comparison of FLIMCLUS with FLIM and the simple ratio indicate substantial differences over open areas where the latter methods map-zero L e values. Further validation over other study sites, including surface data mapping edges between canopy and open areas, is proposed. The FLIM-CLUS L e maps may be useful for testing scale dependent assumptions within remote sensing algorithms and ecosystem flux models applied to the study sites and similar Picea mariana stands. |
| [135] | An inversion of linked radiative transfer models (RTM) through artificial neural networks (ANN) was applied to MODIS data to retrieve vegetation canopy water content (CWC). The estimates were calibrated and validated using water retrievals from AVIRIS data from study sites located around the United States that included a wide range of environmental conditions. The ANN algorithm showed good performance across different vegetation types, with high correlations and consistent determination coefficients. The approach outperformed a multiple linear regression approach used to independently retrieve the same variable. The calibrated algorithm was then applied at the MODIS 500 m scale to follow changes in CWC for the year 2005 across the continental United States, subdivided into three vegetation types (grassland, shrubland, and forest). The ANN estimates of CWC correlated well with rainfall, indicating a strong ecological response. The high correlations suggest that the inversion of RTM through an ANN provide a realistic basis for multi-temporal assessments of CWC over wide areas for continental and global studies. |
| [136] | River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (6136°) and forward (+36°) scatter direction. The 6136° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way. |
| [137] | The technique described earlier (Goel and Thompson, 1984b) for estimating agronomic parameters from bidirectional crop reflectance data is applied to a fully covered soybean canopy, using data measured in the field. This technique employs the inversion of a canopy reflectance model. It is shown that using the SAIL model one can estimate leaf area index (LAI) as well as average leaf angle (ALA) quite well, provided that the other canopy parameters (leaf reflectance and transmittance, soil reflectance, and fraction of diffused skylight) are known. Some suggestions are made for improving the SAIL model. This should improve the accuracy of estimation of not only LAI and ALA but should also allow the estimation of the complete leaf angle distribution. |
| [138] | |
| [139] | The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises. |
| [140] | This study describes the retrieval of state variables (LAI, canopy chlorophyll, water and dry matter contents) for summer barley from airborne HyMap data by means of a canopy reflectance model (PROSPECT02+02SAIL). Three different inversion techniques were applied to explore the impact of the employed method on estimation accuracies: numerical optimization (downhill simplex method), a look-up table (LUT) and an artificial neural network (ANN) approach. By numerical optimization (Num Opt), reliable estimates were obtained for LAI and canopy chlorophyll contents (LAI02×02) with of 0.85 and 0.94 and RDP values of 1.81 and 2.65, respectively. Accuracies dropped for canopy water (LAI02×02) and dry matter contents (LAI02×02). Nevertheless, the range of leaf water contents () was very narrow in the studied plant material. Prediction accuracies generally decreased in the order Num Opt02>02LUT02>02ANN. This decrease in accuracy mainly resulted from an increase in offset in the obtained values, as the retrievals from the different approaches were highly correlated. The same decreasing order in accuracy was found for the difference between the measured spectra and those reconstructed from the retrieved variable values. The parallel application of the different inversion techniques to one collective data set was helpful to identify modelling uncertainties, as shortcomings of the retrieval algorithms themselves could be separated from uncertainties in model structure and parameterisation schemes. |
| [141] | Optical remote sensing provides information on important vegetation variables such as leaf area index (LAI), biomass, and chlorophyll content. In this study, rice crops, which are rarely studied, were selected because of their high economic importance and the role they play in food security in the study area. The aim was to obtain a reliable estimate of canopy chlorophyll content as an important indicator for the evaluation of the plant status. PROSAIL radiative transfer model and the multispectral image data of ALOS AVNIR-2 were used. A field campaign was carried out in July 2010 in the northern part of Iran, Amol. Sixty sample plots of 20 20 m-2 were randomly selected, and their chlorophyll content was measured using a SPAD-502 chlorophyll meter. The PROSAIL was inverted using a lookup-table (LUT) approach. The LUTs were generated in different sizes. The effect of the LUT size on the retrieval accuracy of the canopy's chlorophyll content was studied using analysis of variance (ANOVA). The outcome of the inversion was evaluated using the calculated R2 and RMSE values with the field measurements. The obtained results demonstrate the ability of PROSAIL to estimate rice plant chlorophyll content using ALOS AVNIR-2 multispectral data (R2= 0.65; RMSE = 0.45). The results also confirmed the usefulness of such an approach for crop monitoring and ecological applications. |
| [142] | The retrieval of biophysical variables using canopy reflectance models is hampered by the fact that the inverse problem is ill-posed. This leads to unstable and often inaccurate inversion results. In order to regularize the model inversion, a novel approach has been developed and tested on synthetic Landsat TM reflectance data. The method takes into account the neighbouring radiometric information of the pixel of interest, named object signature. The neighbourhood data can either be extracted from gliding windows, already segmented images, or using digitized field boundaries. The extracted radiometric data of the neighbourhood pixels are used to calculate 42 descriptive statistical properties that comprehensively characterize the spectral (co)variance of the image object (e.g. mean and standard deviation of the distributions, intercorrelations between spectral bands, etc.). Together with the habitual spectral signature of the pixel being inverted (6 variables), this object signature (42 variables) is used as input in an artificial neural net to estimate simultaneously three important biophysical variables (i.e. leaf area index, leaf chlorophyll, and leaf water content). The use of neural nets for the model inversion avoids time-consuming iterative optimizations and provides a computational effective way to consider simultaneously pixel and object signatures. In order to earn the relation between spectral signatures and biophysical variables, the neural nets were previously trained on large synthetic data sets. The data sets consist of pixel signatures and the corresponding signatures of image objects representing various agricultural fields. The signatures were simulated with the SAILH+PROSPECT canopy reflectance model, assuming largely varying intra- and interfield distributions of the model input parameters. To demonstrate the benefits of the object-based inversion, neural nets were also trained on the pixel signatures alone. For this purpose, the object signatures were simply replaced by randomly generated white noise ll other conditions being the same. The intercomparison based on 30,000 independent validation patterns showed that the proposed method significantly enhances the estimation accuracies: for example, the leaf area index (LAI) is estimated with a percental root mean square error (PRMSE) of 18.3% (object-based) compared to 25.1% (pixel based); the corresponding numbers for the leaf chlorophyll content are 12.6% compared to 15.9%; for the equivalent leaf water thickness, 10.6% and 13.9%, respectively. The benefit of the object signature was strongest for the LAI. Concerning this important biophysical variable, the novel concept accounted for almost one-half of the remaining unexplained variance of the traditional pixel-based approach. Increased accuracies were attributed to the fact that intrafield variations of biophysical canopy variables lead to object signatures that are modulated by the actual average leaf angle (ALA) of the canopy. Since ALA can be considered constant insight given an agricultural field, the concurrent use of pixel and object signatures significantly reduces confounding effects between LAI and ALA typical for traditional inversion approaches. Compared to competing approaches, the algorithm can also be applied to monotemporal imagery and does not require a priori information or the identification of crop type. |
| [143] | Leaf area index (LAI) is an important structural property of vegetation canopy and is also one of the basic quantities driving the algorithms used in regional and global biogeochemical, ecological and meteorological applications. LAI can be estimated from remotely sensed data through the vegetation indices (VI) and the inversion of a canopy radiative transfer (RT) model. In recent years, applications of the genetic algorithms (GA) to a variety of optimization problems in remote sensing have been successfully demonstrated. In this study, we estimated LAI by integrating a canopy RT model and the GA optimization technique. This method was used to retrieve LAI from field measured reflectance as well as from atmospherically corrected Landsat ETM+ data. Four different ETM+ band combinations were tested to evaluate their effectiveness. The impacts of using the number of the genes were also examined. The results were very promising compared with field measured LAI data, and the best results were obtained with three genes in which the R 2 is 0.776 and the root-mean-square error (RMSE) 1.064. |
| [144] | |
| [145] | At present, two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the NASA Terra and Aqua Spacecraft platforms are operational for global remote sensing of the land, ocean, and atmosphere. In this paper, we describe an algorithm for water vapor derivations using several MODIS near-IR channels. The derivations are made over areas that have reflective surfaces in the near-IR, such as clear land areas, clouds, and oceanic areas with Sun glint. The algorithm relies on observations of water vapor attenuation of near-IR solar radiation reflected by surfaces and clouds. Techniques employing ratios of water vapor absorbing channels centered near 0.905, 0.936, and 0.940 m with atmospheric window channels at 0.865 and 1.24 m are used. The ratios partially remove the effects of variation of surface reflectance with wavelengths and result in the atmospheric water vapor transmittances. The column water vapor amounts are derived from the transmittances based on theoretical calculations and using lookup table procedures. Typical errors in the derived water vapor values are in the range between 5% and 10%. The daily pixel-based near-IR water vapor product, which is a standard MODIS level 2 data product, at the 1-km spatial resolution of the MODIS instrument, and the daily, 8-day, and monthly near-IR water vapor products, which are standard MODIS level 3 products, at a 1 by 1 latitude-longitude grid globally are now routinely produced at a NASA computing facility. We present samples of water vapor images and comparisons to ground-based measurements by microwave radiometers. |
| [146] | The Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) mission responds to the need to improve the understanding of the interactions between cloud, aerosol, and radiation processes. The fundamental mission objective is to constrain retrievals of cloud and aerosol properties such that their impact on top-of-atmosphere (TOA) radiative fluxes can be determined with an accuracy of 10 W m-2. However, TOA fluxes cannot be measured instantaneously from a satellite. For the EarthCARE mission, fluxes will be estimated from the observed solar and thermal radiances measured by the Broadband Radiometer (BBR). This paper describes an approach to obtain shortwave (SW) fluxes from BBR radiance measurements. The retrieval algorithms are developed relying on the angular distribution models (ADMs) employed by Clouds and the Earth's Radiant Energy System (CERES) instrument. The solar radiance-to-flux conversion for the BBR is performed by simulating the Terra CERES ADMs us ing a backpropagation artificial neural network (ANN) technique. The ANN performance is optimized by testing different architectures, namely, feedforward, cascade forward, and a customized forward network. A large data set of CERES measurements used to resemble the forthcoming BBR acquisitions has been collected. The CERES BBR-like database is sorted by their surface type, sky conditions, and scene type and then stratified by four input variables (solar zenith angle and BBR SW radiances) to construct three different training data sets. Then, the neural networks are analyzed, and the adequate ADM classification scheme is selected. The results of the BBR ANN-based ADMs show SW flux retrievals compliant with the CERES flux estimates. |
| [147] | This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active–passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds.Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km)2 domains constructed from MODIS, CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m612 for 6535% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation 6565% are within ±10 W m612. Copyright 08 2011 Royal Meteorological Society and Crown in the right of Canada |
| [148] | The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint ESA-JAXA EarthCARE satellite mission, scheduled for launch in 2017, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, CALIPSO, and Aqua. Specifically, EarthCARE's Cloud Profling Radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle and raindrop fall speeds. EarthCARE's 355-nm High Spectral Resolution Lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The Multi-Spectral Imager will provide a context for, and the ability to construct the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross-section. The consistency of the retrievals will be assessed to within a target of 10 W m 2 on the (10 km)2 54 scale by comparing the multi-view Broad-Band Radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains. |
| [149] | ). These two parameters will be used together to retrieve the geophysical parameters. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements. Here, we present recent results obtained from several studies and field experiments that were part of the SMOS mission, and highlight the issues still to be solved. |
| [150] | CryoSat is currently being prepared for a 2005 launch as the first European Space Agency Earth Explorer Opportunity mission. It is a dedicated cryospheric mission equipped with a Ku-band SIRAL (SAR/Interferometric Radar ALtimeter), whose primary objectives are to measure the variability and trends in the mass of the Arctic sea-ice cover and large terrestrial ice sheets. In this paper, an overview is provided of the mission and of the measurement characteristics of the new SIRAL instrument. Examples of data acquired on recent preparatory campaigns are presented, illustrating the operating characteristics of the key SIRAL modes. Preparatory plans for calibration and validation of CryoSat data are described. |
| [151] | On global and regional scales, earth observation (EO)-based estimates of leaf area index (LAI) provide valuable input to climate and hydrologic modelling, while fraction of absorbed photosynthetically active radiation (fAPAR) is a key variable in the assessment of vegetation productivity and yield estimates. Validation of moderate resolution imaging spectroradiometer (MODIS) LAI and fAPAR products is an important prerequisite to using these variables for global modelling or for local water resource modelling and net primary production (NPP) assessment, as in semi-arid West Africa and Senegal. In situ measurements of LAI and fAPAR from three sites in semi-arid Senegal were carried out in 2001 and 2002 for comparison with remotely sensed MODIS data. The seasonal dynamics of both in situ LAI and fAPAR were captured well by MODIS LAI and fAPAR. MODIS LAI is overestimated by approximately 2 15% and the overall level of fAPAR is overestimated by 8 20%. Both MODIS LAI and fAPAR are characterised by a moderate offset, which is slightly higher than can be explained by model and input data uncertainty. In situ fAPAR and normalised differential vegetation index (NDVI) for three different vegetation types showed a strong linear relationship, suggesting that covariance between fAPAR and NDVI is insensitive to variations in leaf angle distribution (LAD) and vegetative heterogeneity. A strong linear relation also exists between MODIS fAPAR and NDVI but with different regression coefficients than the in situ relation because of MODIS' tendency to overestimate fAPAR. The fAPAR/NDVI relations found here, however, do not apply on a global scale but are only valid for similar sun-sensor view geometry and soil colour. |
| [152] | The influence of wind speed (WS) on sea surface emission at L-band is revisited using the updated version v6.2 of Soil Moisture and Ocean Salinity (SMOS) brightness temperatures (TB) and an incoherent two-layer foam emissivity model. The influence of the roughness effect due to surface waves is consistent with the one found with an older version of SMOS TB. The two-layer incoherent model of foam emissivity accounts for weak volume scatterings and multiple reflections within a medium with an exponential vertical permittivity profile. The foam emissivity simulated using this model at L band varies from 0.35 to close to 1 with thickness varying from 0.0102cm to 202cm. The wind induced brightness temperature components deduced from the multi-angular SMOS TB is used to optimize the foam void fraction (defined as the fraction of a unit volume of seawater that is occupied by air) at the air–sea interface, an effective thickness of foam layer and the dependency of foam coverage with wind speed. A new set of parameters for the foam emissivity model and the foam coverage model that can be used for WS up to 2202m02s 61021 for the SMOS sea surface salinity retrieval is proposed. Our foam coverage model derived from SMOS data is now in much better agreement with other estimates derived from other sensors, although it predicts slightly lower coverage at all winds speeds, likely due to the longer wavelength of SMOS measurements. |
| [153] | 高分辨率遥感蒸散数据集的构建受到数据源的限制和云的影响，单一传感器无法达到高时空分辨率覆盖。本文分析了ETWatch不同尺度遥感蒸散结果的空间特征，通过几种融合方法的比较，分析数据融合前后的数据特征和信息量，将时空适应性反射率融合模型（STARFM）集成到ETWatch，用于不同尺度遥感蒸散数据的融合，该方法可以很好的结合高低分辨率数据的空间分布和时间分布信息，在时间上保留了高时间分辨率数据的时间变化趋势，空间上又反映了高空间分辨率数据的空间细节差异，STARFM融合后的日ET数据与融合前1 km 日ET数据的平均相对误差为1.75%，融合后的月ET数据与融合前1 km 月ET数据的平均相对误差为0.2%，STARFM适合于不同尺度下遥感ET数据的融合。 . 高分辨率遥感蒸散数据集的构建受到数据源的限制和云的影响，单一传感器无法达到高时空分辨率覆盖。本文分析了ETWatch不同尺度遥感蒸散结果的空间特征，通过几种融合方法的比较，分析数据融合前后的数据特征和信息量，将时空适应性反射率融合模型（STARFM）集成到ETWatch，用于不同尺度遥感蒸散数据的融合，该方法可以很好的结合高低分辨率数据的空间分布和时间分布信息，在时间上保留了高时间分辨率数据的时间变化趋势，空间上又反映了高空间分辨率数据的空间细节差异，STARFM融合后的日ET数据与融合前1 km 日ET数据的平均相对误差为1.75%，融合后的月ET数据与融合前1 km 月ET数据的平均相对误差为0.2%，STARFM适合于不同尺度下遥感ET数据的融合。 |
| [154] | A simple scheme is proposed to estimate instantaneous net radiation over large heterogeneous areas for clear sky days using only remote sensing observations. Our method attempts to develop an algorithm which primarily uses remote sensing information and eliminates the need for ground information as model input, by using various land and atmospheric data products available from Terra ODIS. It explicitly recognizes the need for spatially varied parameters and provides a distributed net radiation map over large heterogeneous domain with fine spatial resolution. Since instantaneous net radiation estimates have limited scope compared to daily average values or diurnal cycle, a sinusoidal model is proposed to estimate diurnal cycle of net radiation. The sinusoidal model is capable of retrieving the diurnal variations of net radiation with a single instantaneous net radiation estimate from the satellite. Preliminary results, using data over Southern Great Plains, show good agreement with ground-based observations. It appears that the methodology presented here can estimate instantaneous and daily net radiation with comparable accuracy to those of current methods that use ground-based observations and mainly provide point estimates. |
| [155] | This paper proposes a mathematical method based on a one-dimensional diffusion equation to derive diurnal variation of surface soil heat flux using cloudless MODIS data for a sparse vegetation and bare soil. Diurnal variation of the surface temperature and near-surface real soil thermal inertia are both simulated from the MODIS data. The study was carried out for the Yingke oasis plains area and the Arou alpine meadow area, which are located in the midstream and upstream, respectively, of the Heihe River Basin. Statistical results showed that the proposed method performs well to estimate soil heat flux for both study areas. In the oasis plains case, the coefficient of determination (R2) was 0.958 and the index of agreement (d) was 0.989. In the alpine meadow case, the coefficient of determination (R2) was 0.972 and the index of agreement (d) was 0.993. Close agreement of the estimated surface soil heat flux with measured observations indicates that the method is promising. Future work will focus on scaling this mathematical method to create a diurnal surface soil heat flux map. |
| [156] | . |
| [157] | 尺度概念是理解地球系统复杂性的关键,尺度问题被认为是对地观测的主要挑战之一,而结合具体研究应用领域,由地学现象的尺度本身出发,选择所需遥感影像的最佳尺度和分辨率,是非常有现实意义的.本文在深入剖析了遥感影像的尺度特性和遥感影像尺度选择的意义的基础上,探讨了基于地统计学方法定量选择遥感影像最佳空间分辨率的方法.阐明了传统局部方差方法不能得到理想结果的原因:传统的局部方差方法的实质是基于变化地面面积计算影像局部方差的均值,而基于这样不同甚至是相差悬殊的地面面积进行局部方差计算,其结果必然不具有可比性.对此,本文提出了基于可变窗口与可变分辨率的改进局部方差方法,即依次降低空间分辨率时,高分辨率采用大窗口尺寸,低分辨率采用小窗口尺寸来维持计算窗口内的地面面积的一致,由此计算出的局部方差作比较来判定遥感影像最佳分辨率.进行了系列实验分析,得到了相关结论,分析得出这种基于地统计的方法来选择遥感影像最佳分辨率的方法,对遥感和GIS研究与地学应用具有一定的理论意义和指导意义. . 尺度概念是理解地球系统复杂性的关键,尺度问题被认为是对地观测的主要挑战之一,而结合具体研究应用领域,由地学现象的尺度本身出发,选择所需遥感影像的最佳尺度和分辨率,是非常有现实意义的.本文在深入剖析了遥感影像的尺度特性和遥感影像尺度选择的意义的基础上,探讨了基于地统计学方法定量选择遥感影像最佳空间分辨率的方法.阐明了传统局部方差方法不能得到理想结果的原因:传统的局部方差方法的实质是基于变化地面面积计算影像局部方差的均值,而基于这样不同甚至是相差悬殊的地面面积进行局部方差计算,其结果必然不具有可比性.对此,本文提出了基于可变窗口与可变分辨率的改进局部方差方法,即依次降低空间分辨率时,高分辨率采用大窗口尺寸,低分辨率采用小窗口尺寸来维持计算窗口内的地面面积的一致,由此计算出的局部方差作比较来判定遥感影像最佳分辨率.进行了系列实验分析,得到了相关结论,分析得出这种基于地统计的方法来选择遥感影像最佳分辨率的方法,对遥感和GIS研究与地学应用具有一定的理论意义和指导意义. |
| [158] | The objective of this paper is to quantitatively validate the global MODIS and CYCLOPES leaf area index (LAI) products using a global LAI field measurement database created on the basis of a literature review and major validation campaigns. The MODIS LAI product suite, containing the Terra Collection 4 (C4), Terra Collection 5 (C5) and Terra02+02Aqua combined C5, was analyzed, with considerable attention paid to the quality control (QC) information. The CYCLOPES V3.1 LAI product was similarly analyzed with regard to the status map (SM) layer. In general, the MODIS LAI has improved consistently over all releases. MODIS C5 data retrieved with the main algorithm (QC02<0264) and CYCLOPES data showed a similar range of uncertainties (1.0–1.2). Uncertainties for the best MODIS C5 (QC02=020) and CYCLOPES (SM02=020) estimates were around 0.9–1.1. The overall mean differences between the best MODIS C5 and CYCLOPES were within ±020.10. The highest correspondence was obtained for woody biomes from the best MCD15 C5 data (RMSE02=020.80). Results indicate that the uncertainties in current LAI products (around ±021.0) are still unable to meet the accuracy requirement of GCOS (±020.5). Although there are limitations, we recommend MODIS C5 retrieved with the main algorithm (QC02<0264) and CYCLOPES for the user community. This study demonstrates the necessity of exploring uncertainties related to the true and effective LAIs separately, and reveals the importance of referring to the quality assessment information. More field measurements are required for further studies, which should focus on under-sampled biome types and areas. |
| [159] | Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China. |
| [160] | Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981-1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05. During 2000-2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps (RMSE = 0.7848 and R2 = 0.8095) than the GEOV1 LAI values (RMSE = 0.9084 and R2 = 0.7939) and the MOD15 LAI values (RMSE = 1.1173 and R2 = 0.6705). |
| [161] | A new set of recently developed leaf area index (LAI) algorithms has been employed for producing a global LAI dataset at 1km resolution and in time-steps of 10 days, using data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor. In this paper, this new LAI product is compared with the global MODIS Collection 4 LAI product over four validation sites in North America. The accuracy of both LAI products is assessed against seven high resolution ETM+ LAI maps derived from field measurements in 2000, 2001, and 2003. Both products were closely matched outside growing season. The MODIS product tended to be more variable than the VGT product during the summer period when the LAI was maximum. VGT and ETM+ LAI maps agreed well at three out of the four sites. The median relative absolute error of the VGT LAI product varied from 24% to 75% at 1 km scale and it ranged from 34% to 88% for the MODIS LAI product. The importance of correcting field measurements for the clumping effect is illustrated at the deciduous broadleaf forest site (HARV). Inclusion of the sub-pixel land cover information improved the quality of LAI estimates for the prairie grassland KONZ site. Further improvement of the global VGT LAI product is suggested by production and inclusion of pixel-specific global foliage clumping index and forest background reflectance maps that would serve as an input into the VGT LAI algorithms. |
| [162] | <p>精确测量具有强烈时空变异性的降水,是水文气象学颇具挑战的科学研究目标之一。基于多传感器联合反演降水（Multi-sensor Precipitation Estimation,MPE）的方法已成为卫星反演降水的主流趋势。首先介绍MPE方法的定义与分类,回顾MPE方法的历史发展阶段及研究现状;然后介绍主要的MPE算法,包括TRMM多卫星降水分析算法（TMPA）、气候预测中心算法（CMORPH）、全球卫星降水制图算法（GSMaP）、美国海军研究实验室联合算法（NRLB）和神经网络降水算法（PERSIANN）;对比这5种主要算法的优缺点和反演精度（PERSIANN精度范围为-56%~200%,其他产品为-67%~10%）,指出存在的主要问题,并且评价不同类型MPE算法的性能;最后结合目前存在的问题探讨MPE方法研究发展趋势。</p> . <p>精确测量具有强烈时空变异性的降水,是水文气象学颇具挑战的科学研究目标之一。基于多传感器联合反演降水（Multi-sensor Precipitation Estimation,MPE）的方法已成为卫星反演降水的主流趋势。首先介绍MPE方法的定义与分类,回顾MPE方法的历史发展阶段及研究现状;然后介绍主要的MPE算法,包括TRMM多卫星降水分析算法（TMPA）、气候预测中心算法（CMORPH）、全球卫星降水制图算法（GSMaP）、美国海军研究实验室联合算法（NRLB）和神经网络降水算法（PERSIANN）;对比这5种主要算法的优缺点和反演精度（PERSIANN精度范围为-56%~200%,其他产品为-67%~10%）,指出存在的主要问题,并且评价不同类型MPE算法的性能;最后结合目前存在的问题探讨MPE方法研究发展趋势。</p> |
| [163] | 作为定量描述地表异质性和时空分布规律的主要方法，遥感需要与模型相结合，才能对陆表蒸散进行估算。ETWatch是面向流域规划与管理和农业水管理的实用需求，针对遥感应用而设计的遥感蒸散监测系统，可用于计算流域地表净辐射、感热、潜热(ET)的空间分布及其时间过程，提高ETWatch模型的精度和可靠性的关键在于发展多源遥感数据的参数化方法。本文在调研国内外研究进展的基础上，总结了流域蒸散遥感估算参数化中存在的主要问题，包括非均匀下垫面参数获取、时空尺度转换、多源遥感数据集成、真实性检验与模型校正等，并结合上述问题介绍了ETWatch中的模型与方法。 . 作为定量描述地表异质性和时空分布规律的主要方法，遥感需要与模型相结合，才能对陆表蒸散进行估算。ETWatch是面向流域规划与管理和农业水管理的实用需求，针对遥感应用而设计的遥感蒸散监测系统，可用于计算流域地表净辐射、感热、潜热(ET)的空间分布及其时间过程，提高ETWatch模型的精度和可靠性的关键在于发展多源遥感数据的参数化方法。本文在调研国内外研究进展的基础上，总结了流域蒸散遥感估算参数化中存在的主要问题，包括非均匀下垫面参数获取、时空尺度转换、多源遥感数据集成、真实性检验与模型校正等，并结合上述问题介绍了ETWatch中的模型与方法。 |
| [164] | Net radiation plays an essential role in determining the thermal conditions of the Earth surface and is an important parameter for the study of land-surface processes and global climate change. In this paper, an improved satellite-based approach to estimate the daily net radiation is presented, in which sunshine duration were derived from the geostationary meteorological satellite (FY-2D) cloud classification product, the monthly empiricalasandbsAngstrom coefficients for net shortwave radiation were calibrated by spatial fitting of the ground data from 1997 to 2006, and the daily net longwave radiation was calibrated with ground data from 2007 to 2010 over the Heihe River Basin in China. The estimated daily net radiation values were validated against ground data for 12 months in 2008 at four stations with different underlying surface types. The average coefficient of determination (R2) was 0.8489, and the averaged Nash-Sutcliffe equation (NSE) was 0.8356. The close agreement between the estimated daily net radiation and observations indicates that the proposed method is promising, especially given the comparison between the spatial distribution and the interpolation of sunshine duration. Potential applications include climate research, energy balance studies and the estimation of global evapotranspiration. |
| [165] | The planetary boundary layer is the medium of energy, moisture, momentum and pollutant exchange between the surface and the atmosphere. In this paper, a method to derive the boundary layer mixing height (MH) was introduced and applied over the Heihe river basin. Atmospheric profiles from the MODerate Resolution Imaging Sepctroradiometer (MODIS) instrument onboard the NASA-Aqua satellite were used for the high spatial resolution of this method. A gap-filling method was used to replace missing MODIS data. In situ MH data were also calculated from HIWATER (Heihe Watershed Allied Telemetry Experimental Research) and WATER (Watershed Allied Telemetry Experimental Research) observational radiosonde sounding data from 2008 and 2012 using the Richardson number method combined with a subjective method. The MH occurs where there is an abrupt decrease in the MR (water vapor mixing ratio). The minimum vertical gradient of the MR is used to determine the MH. The method has an average RMSE of 370 m under clear skies and convective conditions. The seasonal variation in the MH at the Gaoya radiosonde station is also presented. The study demonstrates that remote sensing methodologies can successfully estimate the MH without the help of field measurements. |
| [166] | Aerodynamic roughness (AR) is an important parameter affecting land-atmosphere interactions. Previous studies on inversions based on bidirectional reflectance distribution functions (BRDFs) have focused on spatial analysis over desert and gobi, which are vegetation-free areas, due to the lack of wind gradient data for continuous time periods over vegetated surfaces. This study uses meteorological gradient data from a new long-term site that is supported by the Heihe Watershed Allied Telemetry Experimental Research project and is located in an irrigated farmland area in the Heihe River Basin of northwestern China. These data are used to calculate a field AR time series over maize using an iterative computation method based on Monin-Obukhov similarity theory. A linear relationship is demonstrated between the field AR and the BRDF parameters derived from multiband Moderate Resolution Imaging Spectroradiometer data. The correlation analysis for the near-infrared and shortwave bands reveals R2 values of 0.8739 and 0.8833, respectively; however, R2 is only 0.0146 for the visible band. Various band combinations do not improve the outcome. Thus, the near-infrared and shortwave parameters have the potential to be used to infer AR and its related evapotranspiration at more extensive temporal and spatial scales. |
| [167] | Sensible heat flux is a key component of land–atmosphere interaction. In most parameterizations it is calculated with surface-air temperature differences and total aerodynamic resistance to heat transfer (Rae) that is related to the KB611parameter. Suitable values are hard to obtain since KB611is related both to canopy characteristics and environmental conditions. In this paper, a parameterize method for sensible heat flux over vegetated surfaces (maize field and grass land in the Heihe river basin of northwest China) was proposed based on the radiometric surface temperature, surface resistance (Rs) and vapor pressures (saturated and actual) at the surface and the atmosphere above the canopy. A biophysics-based surface resistance model was revised to compute surface resistance with several environmental factors. The total aerodynamic resistance to heat transfer is directly calculated by combining the biophysics-based surface resistance and vapor pressures. One merit of this method is that the calculation of KB611can be avoided. The method provides a new way to estimate sensible heat flux over vegetated surfaces and its performance compares well to the LAS measured sensible heat and other empirical or semi-empirical KB611based estimations. |
| [168] | |
| [169] | Abstract Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change. |
| [170] | . |
| [171] | 全球范围内的城市扩张已使得大量的不透水面取代了以植被为主的地表自然景观,并给生态环境带来了明显的负面影响.不透水面作为一个影响生态环境的关键因子已引起了全社会的广泛关注.如何及时快速地掌握不透水面的空间分布信息,准确无误地量化不透水面的动态变化信息,是城市规划、环境保护亟待解决的现实问题.而遥感以其快速、大范围、多尺度、可重复的对地观测优势为解决这一问题提供了很好的解决方案.不透水面遥感研究经过近十几年的发展已有了长足的进步,多种针对不透水面信息反演的遥感创新技术与方法被相继提出.本文重点分析了这些针对遥感不透水面提出的创新技术,详细地指出了它们的优势和不足,并在此基础上总结了中国遥感工作者在不透水面遥感方面的研究工作.当前许多不透水面信息的反演精度都可以达到85％以上,但是不透水面与裸土和阴影信息的混淆仍是困扰不透水面信息精准反演的主要问题.由于大部分不透水面材料具有和砂土石同源的特点,因此在现有影像光谱分辨率不足的情况下,单靠光谱是很难进一步提高不透水面信息的反演精度,而借助LiDAR等其他辅助数据,将有望帮助解决这一瓶颈问题. . 全球范围内的城市扩张已使得大量的不透水面取代了以植被为主的地表自然景观,并给生态环境带来了明显的负面影响.不透水面作为一个影响生态环境的关键因子已引起了全社会的广泛关注.如何及时快速地掌握不透水面的空间分布信息,准确无误地量化不透水面的动态变化信息,是城市规划、环境保护亟待解决的现实问题.而遥感以其快速、大范围、多尺度、可重复的对地观测优势为解决这一问题提供了很好的解决方案.不透水面遥感研究经过近十几年的发展已有了长足的进步,多种针对不透水面信息反演的遥感创新技术与方法被相继提出.本文重点分析了这些针对遥感不透水面提出的创新技术,详细地指出了它们的优势和不足,并在此基础上总结了中国遥感工作者在不透水面遥感方面的研究工作.当前许多不透水面信息的反演精度都可以达到85％以上,但是不透水面与裸土和阴影信息的混淆仍是困扰不透水面信息精准反演的主要问题.由于大部分不透水面材料具有和砂土石同源的特点,因此在现有影像光谱分辨率不足的情况下,单靠光谱是很难进一步提高不透水面信息的反演精度,而借助LiDAR等其他辅助数据,将有望帮助解决这一瓶颈问题. |
| [172] | . |
| [173] | . |
| [174] | . |
| [175] | |
| [176] | In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths , which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. |
| [177] | Abstract: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements. |
| [178] | // The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. on the standard PASCAL VOC detection dataset, we perform a large-scale study on the Image Net Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent test bed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image-level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors. |
| [179] | |
| [180] | . , |
| [181] | Abstract Paddy rice in monsoon Asia plays an important role in global food security and climate change. Here we documented annual dynamics of paddy rice areas in the northern frontier of Asia, including Northeastern (NE) China, North Korea, South Korea, and Japan, from 2000-2014 through analysis of satellite images. The paddy rice area has increased by 120% (2.5 to 5.5 million ha) in NE China, in comparison to a decrease in South Korea and Japan, and the paddy rice centroid shifted northward from 41.16 N to 43.70 N (~310 km) in this period. Market, technology, policy, and climate together drove the rice expansion in NE China. The increased use of greenhouse nurseries, improved rice cultivars, agricultural subsidy policy, and a rising rice price generally promoted northward paddy rice expansion. The potential effects of large rice expansion on climate change and ecological services should be paid more attention in the future. |
| [182] | 成像方式的多样化以及遥感数据获取能力的增强,导致遥感数据的多元化和海量化,这意味着遥感大数据时代已经来临.然而,现有的遥感影像分析和海量数据处理技术难以满足当前遥感大数据应用的要求.发展适用于遥感大数据的自动分析和信息挖掘理论与技术,是目前国际遥感科学技术的前沿领域之一.本文围绕遥感大数据自动分析和数据挖掘等关键问题,深入调查和分析了国内外的研究现状和进展,指出了在遥感大数据自动分析和数据挖掘的科学难题和未来发展方向. . 成像方式的多样化以及遥感数据获取能力的增强,导致遥感数据的多元化和海量化,这意味着遥感大数据时代已经来临.然而,现有的遥感影像分析和海量数据处理技术难以满足当前遥感大数据应用的要求.发展适用于遥感大数据的自动分析和信息挖掘理论与技术,是目前国际遥感科学技术的前沿领域之一.本文围绕遥感大数据自动分析和数据挖掘等关键问题,深入调查和分析了国内外的研究现状和进展,指出了在遥感大数据自动分析和数据挖掘的科学难题和未来发展方向. |
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