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基于反射光谱和叶绿素荧光数据的作物病害遥感监测研究进展

本站小编 Free考研考试/2021-12-26
竞霞,1, 邹琴1, 白宗璠1, 黄文江,2,*1西安科技大学测绘科学与技术学院, 陕西西安 710054
2中国科学院空天信息创新研究院遥感科学国家重点实验室, 北京 100101

Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data

JING Xia,1, ZOU Qin1, BAI Zong-Fan1, HUANG Wen-Jiang,2,*1College of Geometrics, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

通讯作者: * 黄文江, E-mail:huangwj@aircas.ac.cn

收稿日期:2020-09-29接受日期:2021-04-26网络出版日期:2021-05-21
基金资助:国家自然科学基金项目(41601467)
国家自然科学基金项目(52079103)


Corresponding authors: * E-mail:huangwj@aircas.ac.cn
Received:2020-09-29Accepted:2021-04-26Published online:2021-05-21
Fund supported: National Natural Science Foundation of China(41601467)
National Natural Science Foundation of China(52079103)

作者简介 About authors
E-mail:jingxiaxust@163.com



摘要
作物病害是影响粮食产量和质量的生物灾害, 病害的侵染消耗了作物营养和水分, 扰乱了其正常的生命过程, 引起了作物内部生理生化和外部表观形态的改变。冠层反射光谱能够较好地探测作物群体结构信息, 叶绿素荧光能敏感反映作物光合生理上的变化, 二者均能够实现作物病害的遥感探测。本文从作物病害遥感探测的方法和尺度两个方面综述了基于反射率光谱的作物病害遥感监测现状, 概括了主动荧光、被动荧光以及协同日光诱导叶绿素荧光和反射率光谱在作物病害遥感监测中的研究进展, 分析了反射率光谱和叶绿素荧光数据在作物病害遥感探测方面的优缺点, 探讨了不同数据源、不同监测方法在作物病害遥感探测中可能存在的问题, 并在此基础上展望了作物病害遥感监测的未来发展, 旨在为后续利用反射率光谱和叶绿素荧光数据探测作物病害提供重要的参考依据。
关键词： 反射率;叶绿素荧光;作物病害;遥感监测

Abstract
Crop diseases are biological disasters that affect grain production and quality. The infestation of diseases consumes the nutrients and water, disrupts its normal life process, and causes changes in the internal physiological and biochemical state and external appearance of the crop. Canopy reflectance spectrum can detect crop population structure information well, and chlorophyll fluorescence data can sensitively reflect changes in crop photosynthetic physiology, both methods are capable of detecting crop diseases via remote sensing technology. This article outlined the current research status of crop diseases detection based on reflectance spectrum through remote sensing technology from the aspects of monitoring methods and monitoring scales, summarized the research progress of using active fluorescence, passive fluorescence and coordinated solar-induced chlorophyll fluorescence and reflectance spectroscopy to monitor crop diseases, analyzed the advantages and disadvantages of reflectance spectrum and chlorophyll fluorescence data in crop disease early warning detection, and discussed the possible problems in the remote sensing detection of crop diseases. On the basis, we made a prospect for the development of remote sensing monitoring crop diseases. This paper provides an important reference for the subsequent applications of crop diseases detection based on reflectance spectrum and chlorophyll fluorescence data.
Keywords：reflectance;chlorophyll fluorescence;crop diseases;remote sensing monitoring


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本文引用格式
竞霞, 邹琴, 白宗璠, 黄文江. 基于反射光谱和叶绿素荧光数据的作物病害遥感监测研究进展. 作物学报, 2021, 47(11): 2067-2079 DOI:10.3724/SP.J.1006.2021.03057
JING Xia, ZOU Qin, BAI Zong-Fan, HUANG Wen-Jiang. Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data. Acta Agronomica Sinica, 2021, 47(11): 2067-2079 DOI:10.3724/SP.J.1006.2021.03057


受近年来极端天气的影响, 作物病害出现来势早、灾情重和大面积爆发等特点[1], 严重影响了作物产量和质量, 快速、无损、高精度、大范围的监测和预警是有效防控作物病害的关键[2]。传统的作物病害监测主要由植保专家等通过田间调查的方法判断病害严重度, 该方法费时费力, 时效性差, 且受观测者的主观因素影响较大[3], 难以适应大范围病害实时监测和预报的需求[4]。遥感技术具有快速、大范围和无破坏等显著优点, 已被广泛应用于作物长势及病害胁迫监测中[5,6]。

作物受到病菌侵染后, 叶片色素及水分含量、光合生理状态等均会发生变化, 病害的不同侵染阶段其生理变化强度及其症状显现程度均不相同[7]。在作物受到病害胁迫的早期阶段, 主要是通过生理机制的调整使其快速适应外在胁迫的变化, 而叶绿素荧光能够灵敏反映作物光合生理上的变化, 实现作物病害的早期探测[8,9]。当作物受到持续的病害胁迫后, 不但其细胞活性、生化组分等发生变化, 叶片形态、叶倾角分布及冠层结构、密度等均会随之改变, 进而引起植物叶片、冠层反射光谱发生变化。因此, 利用反射率和叶绿素荧光光谱均能实现作物病害的遥感监测。

1 基于反射率光谱的作物病害遥感监测

作物受到病害胁迫后引起的叶片表面“可见-近红外”波段光谱反射率的变化, 反映了植被物理生化组分的状况, 是遥感探测病害的直接依据[10]。根据病害对作物生理生化及冠层结构的影响程度, 作物的反射率光谱会发生相应改变[11], 为受胁作物的生理胁迫提供丰富的信息[12], 被广泛应用于作物病害的遥感监测研究。

1.1 基于反射率光谱的作物病害监测方法

受病害胁迫作物生理生化特性及表观形态的改变会引起光谱特征的改变[13], 其光谱响应特性是由病害胁迫导致的植物损伤所引起的色素、水分、形态、结构等变化的函数[14]。作物不同, 病害种类及其发展阶段不同, 导致了光谱特征的多样性[15], 因此不同病害类型具有不同波段的光谱响应特性, 利用光谱响应的敏感波段及异常光谱的变化程度可实现作物病害的识别及发病程度的预测(表1)。目前主要采用过滤法(Filter)、包裹法(Wrapper)和嵌入法(Embedded)三类特征选择算法挑选作物病害遥感探测的敏感因子[16]。Filter算法从数据特征的结构出发, 利用光谱特征参量与病情指数之间的相关性作为敏感因子的优选标准, 特征参量的选择独立于模型算法[17], 能够快速实现作物病害的诊断, 但该方法忽略了各特征参量间的相关性, 难以挖掘出特征参量之间的组合效应, 影响了模型构建的精度[9]。为提高模型的泛化能力与预测精度[18], 结合特征选择和模型构建方法的Wrapper算法诞生, Wrapper算法需要定义启发策略, 复杂性高, 在作物病害监测的实现上具有一定的难度[19]。Embedded方法是基于Filter算法和Wrapper算法的折中方案, 能通过学习器自身主动选择特征, 包括基于惩罚项的特征选择法[20]和基于树模型的特征选择法[21,22]等, 具有良好的统计性质, 但参数设置需要深厚的背景知识[23]。

Table 1
表1
表1特征选择算法及敏感波段
Table 1Feature selection algorithm and sensitive band
作物病害类型
Type of crop diseases		光谱响应波段
Spectral response band (nm)		特征选择算法
Feature selection algorithm		参考文献
References
小麦条锈病 Wheat stripe rust560-670Filter[24]
小麦白粉病 Wheat powdery mildew490, 510, 516, 540, 780, 1300Filter[25]
水稻穗颈瘟 Rice panicles blast430-530, 580-680, 1480-2000Filter[26]
番茄晚疫病 Tomato late blight700-750, 750-930, 950-1030, 1040-1130Filter[27]
棉花黄萎病 Cotton verticillium wilt680-760, 731-1371Filter[28]
玉米大斑病 Corn leaf blight725-740Filter[29]
小麦条锈病、小麦白粉病
Wheat yellow rust, wheat powdery mildew		480, 633, 934Wrapper[30]
水稻颖枯病 Rice panicles450-850Wrapper[31]
番茄叶斑病 Tomato bacterial spot395, 633-635, 750-760Wrapper[32]
花生叶斑病 Peanut leaf spots761, 938Wrapper[33]
苹果黑星病 Venturia inaequalis infection1350-1750, 2200-2500Embedded[34]
马铃薯晚疫病 Potato late blight600-900Embedded[35]

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作物病害遥感探测精度除与所选特征因子有关外, 建模算法也是影响其精度的重要因素。作物病害遥感监测模型主要包括统计模型和人工智能模型(表2)。统计模型能够综合描述两组变量之间典型的相关关系, 方法简单且在样本充足的情况下能达到较好的监测精度, 但由于数据获取时外界条件的差异, 该方法在空间维和时间维上的普适性较差[36]。因此一些****提出了能够兼顾训练误差和泛化能力的模式识别和机器学习的作物病害监测模型[37], 该方法具有较好的非线性拟合能力, 能不断训练样本数据使目标达到最优化[38], 解决了反射系数轻微变化而导致作物病害探测困难的问题[39], 但基于机器学习的作物病害遥感监测需要海量数据样本, 且存在着过学习、局部极值点和维数灾难等缺点[40]。

Table 2
表2
表2作物病害遥感监测算法
Table 2Remote sensing monitoring algorithm for crop diseases
模型
Model		作物病害种类
Type of crop diseases		算法
Algorithm		文献
Reference
统计模型
Statistical model		小麦白粉病
Wheat powdery mildew		相关分析、方差分析
Correlation analysis and variance analysis		[41]
小麦条锈病
Wheat stripe rust		线性回归、非线性回归
Linear regression and nonlinear regression		[42]
小麦白粉病
Wheat powdery mildew		Logistic回归
Logistic regression		[43]
番茄叶斑病
Tomato bacterial spot		偏最小二乘回归、多元逐步回归
Partial least squares regression and multiple stepwise regression		[44]
小麦条锈病
Wheat stripe rust		偏最小二乘法
Partial least squares		[9]
人工智能模型
Artificial
intelligence model		水稻颖枯病、曲霉病
Rice glume blight disease and false smut disease		主成分分析
Principal component analysis		[31]
黄瓜花叶病毒
Cucumber mosaic virus		人工神经网络
Artificial neural network		[45]
小麦白粉病
Wheat powdery mildew		Fisher线性判别分析、AdaBoost和支持向量机
Fisher linear discriminant analysis, support vector machine, and AdaBoost model		[46]
大豆枯萎病
Soybean sudden death syndrome		偏最小二乘判别分析
Partial least squares discriminant analysis		[47]
油棕茎腐病
Oil palm basal stem rot		决策树、随机森林和支持向量机
Decision tree, random forest, and support vector machine		[48]
小麦白粉病
Wheat powdery mildew		随机森林
Random forest		[49]
蚕豆病虫害
Broad bean disease and pests		聚类算法
K-Means and the FCM clustering		[50]

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1.2 基于不同尺度的作物病害遥感监测

在叶片及冠层尺度上, 作物病害的遥感监测主要基于手持仪器[51]、塔基平台[52,53,54]等近地平台搭载的光谱仪获取不同发病状态下的作物光谱信息。叶片水平的光谱特征不受土壤和形态等因素影响, 适用于作物病害的机理研究, 受限于观测范围难以实现大区域的病害监测。1927年Taubenhaus等[55]利用飞机搭载的黑白相机实现了棉花根腐病判定, 首次实现了地块尺度上的作物病害识别。随着光谱成像技术的发展, 目前地块尺度的作物病害遥感监测主要利用高光谱影像和多光谱影像等得到不同病情严重度下作物病害信息[56,57,58,59,60,61]。尽管基于航空的作物病害监测取得了较好的诊断效果, 但仪器昂贵限制了其广泛使用[62], 区域尺度的病害监测以卫星影像作为数据源, 在观测范围和成本上具有一定优势。利用卫星数据获取作物病害胁迫信息要求使用的卫星传感器具有较高的光谱分辨率以及可区分健康作物和受病害胁迫作物的光谱通道[63], 高分辨率和高精度的卫星遥感影像与其他多源异构信息结合形成时空序列数据集, 为区域内作物病害监测、大尺度病害预报和流行趋势提供依据[64,65]。表3对叶片及冠层、地块和区域尺度上的部分研究成果进行了总结和归纳。

Table 3
表3
表3不同尺度的作物病害遥感监测应用案例
Table 3Application cases of remote sensing monitoring crop diseases at different scales
监测尺度
Monitoring scale		设备
Devices		特点
Characteristics		病害类型
Type of diseases		参考文献
Reference
叶片及冠层尺度
Leaf and canopy scale		非成像高光谱扫描仪、成像高光谱仪。
Non-imaging hyperspectral scanner and imaging hyperspectral spectrometer.		方便、灵活以及受外界因素影响较小、监测精度高, 通常用于作物病害的遥感探测机理研究, 受探测范围限制, 难以实现大区域作物病害的遥感探测。
Convenient, flexible and less affected by external factors, with high detection accuracy. It is usually used to study the mechanism of early warning and detection of crop diseases. Due to the limitation of the detection range, it is difficult to realize the correction and detection of large-area crop diseases.		甜菜叶斑病、叶锈病和白粉病
Sugar beet leaf spot, leaf rust and powdery mildew		[66]
小麦白粉病
Wheat powdery mildew		[67]
地块尺度
Plot scale		成像多光谱仪、成像高光谱相机、热红外成像仪。
Imaging multi-spectrometer, imaging hyperspectral camera and thermal infrared imager.		通常利用搭载于航空平台的传感器监测, 探测范围较大, 数据源获取相对航天数据更为灵活且受天气状况影响较小, 对爆发性流行病害具有一定的应急监测能力。
Usually, the sensors carried on the aviation platform are used for monitoring, which has a large detection range, more flexible data source acquisition compared with space data, and less affected by weather conditions, and have a certain emergency monitoring ability for explosive epidemic diseases.		水稻穗瘟病
Rice panicle blast		[26]
番茄晚疫病
Tomato late blight		[68]
区域尺度
Regional scale		多光谱卫星、高光谱卫星、热红外卫星。
Multispectral satellites, hyperspectral satellites and thermal infrared satellites.		探测范围广, 以卫星数据为数据源, 能周期性地对同一地区进行重复监测, 为大尺度病害预报和流行趋势提供依据。
Wide detection range. Using satellite data as a data source, it can periodically re-monitor the same area and provide a basis for large-scale disease forecasts and epidemic trends.		小麦锈病
Wheat rust		[69]
小麦白粉病
Wheat powdery midew		[70]
小麦黄锈病
Wheat yellow rust		[71]
芦笋紫斑病
Asparagus purple spot disease		[72]
黄锈病和蚜虫
Wheat yellow rust and aphid		[73]

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反射率光谱数据能够有效反映冠层结构变化[11,74], 为实现大面积作物病害的遥感探测奠定了重要基础, 然而病害对光谱的影响依赖其生理变化强度、病害发展阶段和症状显现程度[7]。病害早期, 作物主要通过调整自身生理机制以适应病害胁迫, 而生化组分则无显著变化[61,65,75]。随着病情严重度的增加, 病害胁迫导致的植被冠层结构变化在生物系统遭受严重伤害时才表现出来[76], 作物冠层结构对病害胁迫响应具有明显的滞后性, 当作物病情指数低于20%时, 反射率光谱难以探测到作物病害胁迫信息[8]。叶绿素荧光与植物光合生理密切相关并参与了作物的能量分布, 在作物受到病害等胁迫时, 叶绿素荧光先于叶绿素含量发生变化, 因此叶绿素荧光能够提供病害胁迫的早期探测信息[77], 更适于作物病害的早期监测[9]。

2 基于叶绿素荧光的作物病害遥感探测

不同于反射率光谱是叶片入射辐射与植物的生物物理和生化特性之间多次相互作用的结果[7], 叶绿素荧光是叶绿素分子吸收光子后, 被激发的叶绿素分子重新发射光子回到基态而产生的一种光信号[78]。叶片叶绿素荧光在红光和远红光光谱区域中存在由光系统PS I和PS II引起的最大值(图1), 且叶绿素对转移到叶片表面的红光波段荧光吸收作用更强, 因此健康的绿色叶片中红光波段峰值通常低于远红光[79]。PS II对生物和非生物胁迫的敏感性致使光化学电子传递能力受到损害, 通常在荧光的发射变化中有明显响应[80], 并能反映出叶绿素荧光和碳同化之间的复杂关系[81], 已有研究表明, 作物受水胁迫时, F440/F690和F440/F740荧光比值相对恒定, 而温度胁迫则致使叶片叶绿素荧光参数和非光化学猝灭敏感性高、F440/F690和F440/F740比值减小[82], 受病害胁迫的作物的Fv/Fm比值减小, 病原菌入侵会破坏叶绿素分子合成和降解的动态平衡, 使得患病植株的PS II活性降低[83]。当作物同时处于生物胁迫(病害胁迫)和非生物胁迫(水、温度、养分胁迫)时, 常引入热红外成像仪监测, 受生物胁迫时冠层温度升高, 而受水、养分胁迫时, 冠层温度不变甚至会下降[84], 对于病害胁迫和热胁迫的区分, 则依赖于病症表现与否进行判别。

图1

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图1稳态条件下叶片吸收光能后的释放途径及叶片荧光发射概念图[79]

Fig. 1Release path of absorbed light energy in leaves under steady-state conditions and conceptual figure of leaf fluorescence emission[79]



2.1 基于主动叶绿素荧光的作物病害探测

主动叶绿素荧光的探测主要包括叶绿素荧光动力学技术和激光诱导荧光技术2种方法。叶绿素荧光动力学技术多借助(非)调制式叶绿素荧光仪的叶片“点”式接触方式测量叶绿素荧光参数[85]。而激光诱导荧光技术以紫外光作为激发光源, 测量单色光激发照明条件下荧光波长的发射荧光[86]。

通过主动方法探测的叶绿素荧光已被广泛应用于作物病害监测以及病害识别和分类等研究中。如Atta等[87]在实验条件下记录了叶绿素荧光光谱随病情严重度的变化, 基于同步荧光光谱特征实现了小麦条锈病的早期监测。周丽娜等[88]基于激光诱导的叶绿素荧光实现了稻瘟病发病等级预测; 隋媛媛等[89]利用叶绿素荧光光谱指数在显症前完成了黄瓜霜霉病预测。在胁迫的分类上, Belasque等[90]利用激光诱导荧光技术实现了人工损伤、养分胁迫和病害胁迫的准确分类; Wang等[91]利用ΦPSII、Fv/Fm和F550/F510三个指标实现了氮、干旱和灰霉病胁迫的分类。上述研究主要是利用非成像荧光技术进行作物病害的遥感监测, 该方法具有成本低、数据量小、处理速度快的优势, 然而叶片不同部位的组织结构和叶绿素含量存在差异, 导致叶片不同部位的光合作用具有横向异质性, 而荧光成像技术能够获取植物的颜色纹理等特征信息和荧光强度信息, 揭示受生物或非生物因素胁迫的植物叶片或表面的时空异质性[92], 因此一些研究者利用荧光影像的这种特性实现了染病与健康植物的区分[93,94]、染病作物的早期诊断[95]和作物病害的实时检测[96]等。

基于主动荧光的作物病害遥感监测对于揭示叶片光合状态、解释病害胁迫机理具有重要意义, 但该方式测定的叶绿素激发荧光与自然条件光合作用荧光的物理意义差别较大, 而且由于使用条件的限制(激光激发或叶片接触式测量等), 难以推广到大范围的遥感应用[97,98]。

2.2 基于日光诱导叶绿素荧光探测作物病害

日光诱导叶绿素荧光(Sun-induced Chlorophyll Fluorescence, SIF)是植物在太阳光照条件下, 由光合中心发射出的光谱信号(650~800 nm), 具有红光(685 nm左右)和远红外(740 nm左右)两个波峰, 能直接反映植物实际光合作用的动态变化[99], 实现作物病害的遥感监测。

自然条件下, 遥感传感器探测的冠层光谱信号中SIF信号与植被反射光谱信号混叠, 且冠层SIF信号很微弱、通常不足入射辐射的2% [100], 因此对于SIF的信息提取具有很大挑战性[101]。随着遥感技术的进步, 研究者发现SIF在Fraunhofer暗线处具有填充效应(图2), 这使得SIF的直接遥感探测成为可能。****们基于此原理提出了标准FLD (Fraunhofer Line Discriminator)[102]、3FLD (3-bands Fraunhofer Line Discriminator)[103]、iFLD (improved Fraunhofer Line Discriminator)[104]、pFLD (PCA-based FLD)[105]和光谱拟合法(Spectral Fitting Method, SFM)[106]等单波段SIF反演算法和全波段SFM[107]、FSR (Fluo rescence Spectral Reconstruction)[108]及F-SFM (Full-spectrum Spectral Fitting Method)[109]等全波段SIF反演算法。

图2

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图2狭窄的大气吸收带对太阳辐照度的影响(左)和荧光对吸收带内的填充效应(右)[104]

Fig. 2Effect of narrow atmospheric absorption zones on solar irradiance (left) and filling effect of fluorescence emission on absorption zone (right)[104]



标准FLD算法是在假设Fraunhofer吸收线内外的反射率和透过率相等的基础上, 通过建立吸收谷内外的辐亮度光谱方程解求SIF[102]。为了克服标准FLD方法在吸收线内外波段的反射率和荧光值实际上存在差异的局限性[86], Maier等[103]提出了3FLD的SIF提取算法, 该方法假设反射率在很窄的Fraunhofer吸收线内外呈线性变化, 通过吸收线内外波段的加权平均值减弱SIF和反射率随波长变化带来的影响。Luis等[104]提出引入2个校正系数表示吸收线内外反射率和荧光关系的iFLD算法, 利用三次样条函数插值获得的表观反射率代替真实反射率进行计算, 从而消除荧光和反射率对SIF反演算法的影响。Liu等[105]以主成分分析代替插值拟合吸收线处的反射率曲线, 以更精确的估算发射率及荧光校正系数。SFM则是假定Fraunhofer吸收线内外的荧光和发射率是变化的, 利用二次函数拟合SIF光谱和反射率光谱[106]。

全波段SIF光谱反演算法的精度取决于反射率和SIF光谱的估算精度, Mazzoni等[107]分别利用2个Voigt函数之和以及三次样条函数代替二次函数拟合SIF光谱和反射率光谱, 基于SFM实现了675~ 770 nm波段范围内的SIF光谱反演, 并用模拟数据进行了验证。FSR算法利用SFM反演出5条吸收线处的SIF辐照度, 通过奇异值分解提取3个具有SIF光谱一般分布特征的基谱, 利用加权线性最小二乘和5个反演的SIF值拟合确定基谱系数, 重建全波段SIF光谱[108]。F-SFM算法则利用主成分分析提取反射率和SIF的特征波段, 根据不同权重的反射率和SIF主成分重建反射率和SIF光谱, 并引入迭代过程提高反射率的估算精度[109]。表4归纳了目前常用的单波段和全波度SIF反演方法, 为今后研究者选择合适的SIF估测算法提供参考。

Table 4
表4
表4单波段和全波段SIF的提取算法
Table 4SIF extraction algorithm for single spectrum and full spectrum
反演算法
Retrieval algorithms		Fraunhofer线内外反射率和荧光关系
The relationship of reflectance and SIF between the internal and external Fraunhofer dark line respectively		参考文献
Reference
单波段
Single spectrum		FLDr(λout) = r(λin), F(λout) = F(λin)[102]
3FLDr(λin) = r(λleft)×ωleft+r(λright)×ωright, F(λout) = F(λin)[103]
iFLDr(λout) = αR r(λin), F(λout) = αFF(λin)[104]
SFMr(λ) = f(rλ), F(λ) = f(Fλ)[106]
pFLD$\ddot{R}(\lambda )=\sum\limits_{i=1}^{n}{{{k}_{i}}{{\phi }_{i}}(\lambda )} $[105]
全波段
Full spectrum		SFMr(λ) = S(rλ), F(λ) = V(Fλ)[107]
FSRF(λ) ≈ b0+b1∙(λ-λ0)+b2∙(λ-λ0)², r(λ) ≈ b3+b4∙(λ-λ0)+b5∙(λ-λ0)²[108]
F-SFM$r(\lambda )=\sum\limits_{i}^{m}{{{k}_{i}}{{\varphi }_{i}}}(\lambda )$., $F(\lambda)=\sum_{i}^{n} j_{i} {\phi }_{i}(\lambda)$[109]
λin、λout、λleft、λright分别表示吸收线内、外、左、右波段的波长值; ωleft、ωright分别表示吸收线左右波段反射率的权重系数; αR和αF分别为反射率和荧光的校正系数; f(rλ)和f(Fλ)为利用数学函数拟合的反射率和荧光曲线; ${{\phi }_{i}}$和${\varphi }_{i}$分别为反射率和荧光的主成分, ki和ji分别为反射率和荧光的主成分权重。
λin, λout, λleft, and λright represent the wavelength values of the inner, outer, left, and right bands of the absorption line; ωleft and ωright represent the weight coefficients of reflectivity in the left and right bands of the absorption line, respectively; αR and αF represent the correction factor for reflectance and fluorescence, respectively; f(rλ) and f(Fλ) are the reflectance and fluorescence curves fitted by mathematical functions, respectively; ${{\phi }_{i}}$and $ are principal components of reflectance and fluorescence, and ki, ji are the principal component weights of reflectance and fluorescence, respectively.

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SIF数据能够快速、无损地探知植物光合生理及其胁迫状况, 被广泛应用于作物病害遥感监测。张永江等[110]基于FLD提取了O2-A (760 nm)和O2-B (688 nm)波段的SIF强度, 构建了用于反映作物受胁迫状况的荧光比值指数F688/F760, 证实了利用FLD提取的SIF信息可以反映田间小麦条锈病的发病状况。Hernández-Clemente等[111]利用不同分辨率下的SIF监测了受疫霉菌侵染的橡树林, 基于提出的FluorFLIGHT模型实现了健康和发病橡树林的分类。Raji等[112]利用O2-A和O2-B波段SIF数据构建了荧光比值F687/F760, 实现了木薯花叶病的早期探测。赵叶等[8]对比分析了反射率光谱和SIF数据对小麦条锈病不同发病状态下的敏感性, 发现当病情指数低于20%, SIF数据对小麦条锈病害胁迫响应更为敏感, 冠层SIF数据比反射率光谱数据更适于作物病害的早期探测[9]。但叶绿素荧光光谱范围有限, 无法探测到光谱响应位置不在此范围内的病害类型, 且监测精度受SIF提取精度的影响。

3 SIF与反射率光谱协同的作物病害遥感探测

作物受到病菌侵染后, 其水分及叶绿素含量、光合速率和光能转换率等一些生理生化指标均会发生变化[113], 反射光谱信号对作物群体生物量具有较稳定的敏感光谱特征, 能够有效反映冠层几何结构的变化[11], 但难以揭示植被光合生理状态[12], 且受土壤颜色、阴影或者其他非绿色景观成分等背景噪声的影响较大[114]。叶绿素荧光与光合作用之间具有直接联系[114], 能够敏感反映作物光合生理上的变化[115], 且荧光能探测到肉眼不可见的植物病害, 受湿度影响小[116]。但传感器探测到的SIF信息同时受到作物胁迫状况和冠层几何结构等综合因素的影响[117], 直接利用冠层SIF监测植物的胁迫状况具有一定难度。综合利用反射率光谱在作物生化参数探测方面的优势[118,119]和SIF在光合生理诊断方面的优势[114], 能够更加客观的映射作物受病害胁迫的真实状况, 提高作物病害的遥感探测精度[4,21]。

陈思媛等[5]和竞霞等[21]研究结果表明, 在反射率光谱指数及一阶微分光谱指数中加入冠层SIF数据能够改善小麦条锈病的遥感监测精度, 然而利用少量波段信息计算反射率光谱指数或一阶微分光谱指数在一定程度上丢失了对作物病害遥感探测的有用信息。基于此, 白宗璠等[120]利用改进离散粒子群算法从全波段光谱数据中优选遥感探测小麦条锈病严重度的特征变量, 协同冠层SIF数据分别利用随机森林和后向传播神经网络构建小麦条锈病遥感探测模型, 改善了模型的收敛速度和寻优精度并提高了小麦条锈病遥感探测精度。上述研究是将SIF和反射率光谱特征直接拼接形成高维特征向量, 没有考虑不同特征向量与作物病情严重度之间的最优映射关系, 利用单一函数映射所有特征构建作物病害遥感监测模型, 不仅难以充分挖掘特征中包含的信息, 还会增加分类器训练和预测时的计算代价。高媛等[20]基于核函数的特征融合法将SIF和反射率光谱特征用不同的核函数进行映射, 通过多核学习算法构建了反射率与SIF协同的小麦条锈病遥感监测模型。结果表明, 对SIF和反射光谱特征分别利用其最优核进行映射构建的小麦条锈病严重度估测精度优于直接拼接法。叶绿素荧光的发射和NPQ能量耗散都是植物碳固定机制中的重要组成部分[78,121], 均能够敏感反映植物受胁状况及其光合性能, 因此一些专家综合利用反射率光谱、SIF和热红外信息进行作物病害的遥感监测。Poblete等[122]基于高光谱影像和热影像提取的SIF和作物水分胁迫指数(CWSI), 实现了健康橄榄树和受木糖杆菌胁迫橄榄树的分类。Calderón等[123]利用连续3年的机载热、多光谱和高光谱影像提取出SIF、温度信息和窄带光谱指数实现了橄榄黄萎病的早期监测。

4 讨论与展望

极端气候条件的变化导致大面积农作物病虫害频发, 对我国农业可持续发展和粮食安全产生严重的影响, 全世界每年由病虫害导致的粮食减产约为总产量的1/4, 其中病害造成的损失为14%, 虫害造成的损失为10% [15]。及时准确的探测到病害胁迫信息对作物病害的防控以及作物产量和品质的提高, 降低病害防治成本, 减少农药对环境污染具有极为重要的意义。虽然基于反射率和叶绿素荧光数据的作物病害遥感探测取得了丰硕的成果, 但由于每种病害对作物侵染的方式都不相同, 病害的光谱响应具有多效性[1]。因此基于反射率和叶绿素荧光的作物病害遥感探测还存在一些问题和挑战:

(1) 全波段SIF光谱的作物病害遥感监测精度及稳定性有待提高。全波段SIF光谱(650~850 nm)在红光区(685~690 nm附近)和远红光区(730~740 nm附近)存在2个荧光峰值, 不仅包含病害胁迫下的SIF强度信息, 还能提供形状信息, 与植被生理状态存在显著相关关系[78], 更适用于作物病害的识别与监测。然而遥感传感器探测到的SIF信号微弱且与反射率信号混叠, 如何提高全波段SIF信息的提取精度, 是利用全波段SIF光谱进行作物病害遥感监测面临的重要挑战之一。

(2) 群体生物量影响了作物病害的SIF探测精度。作物在受病菌侵染初期即能通过调整光合速率的方式启动光保护机制, 以发射叶绿素荧光消耗过剩的光能等生理机制对病害作出快速响应[11,62], 实现作物病害的早期探测。然而冠层SIF一方面随能量耗散途径的生理调节而改变, 另一方面也受到植物色素组成、叶面积、叶倾角等生化物理参数的影响, 如何消除群体生物量对冠层SIF的影响, 是基于SIF数据进行作物病害早期探测需要解决的关键问题。

(3) 病害微观特性和宏观遥感监测的结合不足。病菌生长、繁殖和侵染过程会消耗寄主养分、破坏其正常的生理过程, 如小麦条锈病夏孢子突破表皮破坏了大量的叶绿素, 从而使各个生育期的叶绿素含量降低, 导致了小麦叶片褪绿、发黄等症状[13], 这些变化在反射率和荧光光谱曲线上均有体现。研究不同病害胁迫下叶片的色素含量、细胞水含量、细胞间隙比等微观特性以及叶面积指数群体参数与SIF和反射率光谱的作用机制, 在病害光谱响应特性分析的基础上, 建立SIF和反射率光谱随病情发展的动态响应规律和关键参数的估算模型, 对作物病害的遥感探测和科学防治具有重要意义。

(4) 作物病害逆向遥感识别问题没有很好地解决。目前作物病害遥感探测主要侧重于研究病害胁迫下反射率光谱和叶绿素荧光数据的响应特性, 并利用实验数据中探测到的光谱差异分析作物是否受到病害胁迫及其病情严重度, 而极少有研究涉及作物病害类型的遥感识别问题, 即作物病害遥感逆向识别与诊断问题尚未得到很好地解决。农作物病害的逆向遥感识别是实现大范围航空航天遥感监测的关键, 是利用遥感影像监测农作物病害不可回避的问题。建立基于大尺度范围的作物病害逆向遥感识别技术方法和体系, 构建具有较强机理解释和一定普适性的作物病害诊断模型, 还有待进一步研究。

(5) 不同尺度作物病害遥感探测之间没能很好地结合。近地高光谱作物病害遥感监测具有航空航天遥感监测难以比拟的方便性、灵活性、经济性等优势, 而且受外界因素的影响较小, 能获得相对比较理想的监测结果, 但在空间上具有一定随机性, 只有结合航空航天遥感影像数据才能反映病害发生发展的空间特征, 从真正意义上实现作物病害的遥感监测[124]。利用航空航天遥感影像监测农作物病害时, 由于传感器接收到的信号是地面分辨率范围内像元目标物的总和, 受下垫面状况、植株的形态结构、天气状况、栽培措施等因子的影响, 因此研究不同尺度作物病害遥感探测之间的关系, 对提高作物病害的探测精度, 实现大范围作物病害的遥感监测具有重要意义。

(6) 作物病害遥感探测模型对植被病理机制和定量遥感机理结合不够充分。将遥感探测机理与植被病理机制相结合, 构建具有一定生理机制的作物病害遥感探测模型对提高模型的实用性具有重要意义。已有研究主要侧重于分析病害胁迫下反射率或荧光数据的响应特性, 忽略了病害发生的生理机制及其遥感探测机理。结合病害生理机制的遥感探测模型较单纯基于光谱响应特性构建的模型更能提升对复杂农田环境的适应能力, 提高作物病害遥感监测精度和模型的普适性。

5 总结

随着农业信息化的不断深入, 利用遥感技术监测作物病害逐步从理论走向应用, 并且在弥补传统病害监测时效性差和人力损耗大等缺陷上显示出极大潜力。论文总结了利用反射率光谱数据进行作物病害遥感探测中常用的特征优选和模型构建方法, 概括了主动荧光、被动荧光以及协同SIF和反射率光谱在作物病害遥感监测中的研究进展, 分析了反射率数据和叶绿素荧光数据在作物病害遥感探测中的优势和局限性, 探讨了目前研究中可能存在的问题及未来的发展方向。尽管目前遥感监测技术与实际生产管理应用存在较大差距, 但在充分考虑病害生理机制和定量遥感机理的基础上, 结合生境条件、农学背景深入挖掘多时相遥感数据所包含的病害信息, 可为现代农业大面积精准管理和植保提供实时动态监测信息, 使得作物病害遥感监测方法和技术在应用中不断走向成熟。

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Chlorophyll a fluorescence (ChlF) has been used for decades to study the organization, functioning, and physiology of photosynthesis at the leaf and subcellular levels. ChlF is now measurable from remote sensing platforms. This provides a new optical means to track photosynthesis and gross primary productivity of terrestrial ecosystems. Importantly, the spatiotemporal and methodological context of the new applications is dramatically different compared with most of the available ChlF literature, which raises a number of important considerations. Although we have a good mechanistic understanding of the processes that control the ChlF signal over the short term, the seasonal link between ChlF and photosynthesis remains obscure. Additionally, while the current understanding of in vivo ChlF is based on pulse amplitude-modulated (PAM) measurements, remote sensing applications are based on the measurement of the passive solar-induced chlorophyll fluorescence (SIF), which entails important differences and new challenges that remain to be solved. In this review we introduce and revisit the physical, physiological, and methodological factors that control the leaf-level ChlF signal in the context of the new remote sensing applications. Specifically, we present the basis of photosynthetic acclimation and its optical signals, we introduce the physical and physiological basis of ChlF from the molecular to the leaf level and beyond, and we introduce and compare PAM and SIF methodology. Finally, we evaluate and identify the challenges that still remain to be answered in order to consolidate our mechanistic understanding of the remotely sensed SIF signal. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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The rate of CO2 fixation (Fc) and 680 nm chlorophyll fluorescence emission (F680) were measured simultaneously during induction of photosynthesis in Zea mays L. leaves under varying experimental conditions in order to assess the validity of fluorescence as an indicator of in vivo photosynthetic carbon assimilation. Z. mays leaves showed typical 'Kautsky' fluorescence induction curves consisting of a fast rise in emission (O to P) followed by a slow quenching via a major transient (S-M) to a steady-state (T). After an initial lag, net CO2 assimilation commenced at a point corresponding to the onset of the S-M transient on the F680 induction curve. Subsequently, Fc and F680 always arrived at a steady-state simultaneously. Decreasing the dark-adaption period increased the rate of induction of both parameters. Alteration of leaf temperature produced anti-parallel changes in induction characteristics of Fc and F680. Reducing the CO2 level to below that required for saturation of photosynthesis also produced anti-parallel changes during induction, however, at CO2 concentrations tenfold greater than the atmospheric level the rate of F680 quenching from P to T was appreciably reduced without a similar change in the induction of Fc. Removal of CO2 at steady-state produced only a small increase in F680 and a correspondingly small decrease in F680 occurred when CO2 was re-introduced. The complex relationship between chlorophyll fluorescence and carbon assimilation in vivo is discussed and the applicability of fluorescence as an indicator of carbon assimilation is considered.

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