陈晓璐^{1, 2,},
王彦芳³,
张红梅⁴,
刘峰贵¹,
沈彦俊^2,,
1.青海师范大学地理科学学院 西宁 810008
2.中国科学院遗传与发育生物学研究所农业资源研究中心 石家庄 050022
3.河北地质大学土地科学与空间规划学院/自然资源与环境遥感研究中心 石家庄 050031
4.河北省农村供水总站 沧州 062152
基金项目: 河北省科技厅重点研发计划项目18397002D
中国科学院农业水资源重点实验室开放基金KFKT201901
内蒙古自治区科技成果转化专项2020CG0054

详细信息

作者简介:陈晓璐, 主要研究方向为气候变化与生态水文。E-mail: 1173223106@qq.com

通讯作者:沈彦俊, 主要从事生态水文、环境遥感与应用等方面的研究。E-mail: yj.shen@sjziam.ac.cn

中图分类号:TP79

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被引次数:0

出版历程

收稿日期:2020-10-30
录用日期:2021-01-10
网络出版日期:2021-06-22
刊出日期:2021-06-01

Extraction method of irrigated arable land in the Chahannur Basin based on the ESTARFM NDVI

CHEN Xiaolu^{1, 2,},
WANG Yanfang³,
Zhang Hongmei⁴,
LIU Fenggui¹,
SHEN Yanjun^2,,
1. College of Geosciences, Qinghai Normal University, Xining 810008, China
2. Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
3. Center for Resources and Environment Remote Sensing, College of Land Science and Planning, Hebei GEO University, Shijiazhuang 050031, China
4. Rural Water Supply Station of Hebei Province, Cangzhou 062152, China
Funds: the Key Research and Development Program of Hebei Province18397002D
the Open Fund of the Key Laboratory of Agriculture Water Resources of Chinese Academy of SciencesKFKT201901
the Science and Technology Achievement Transformation Special Project in Inner Mongolia Autonomous Region2020CG0054

More Information

Corresponding author:SHEN Yanjun, E-mail: yj.shen@sjziam.ac.cn

摘要
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摘要
摘要:运用遥感精准识别技术提取灌溉耕地可以为区域内农业耗水管理提供关键数据支持，但受限于遥感数据的时空分辨率，对于光谱特性相似容易混淆的灌溉耕地作物识别有一定难度。为此，本研究通过增强型自适应反射率时空融合模型（Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model，ESTARFM）对MODIS和Landsat数据进行融合，得到察汗淖尔流域时间分辨率为8 d、空间分辨率为30 m的NDVI数据，并与同期真实Landsat NDVI进行对比验证，相关系数为0.94。利用HANTS滤波建立ESTARFN NDVI时间序列数据，选取灌溉耕地样本通过支持向量机进行灌溉耕地空间分布的提取，以弥补数据源、特征提取等限制因素对复杂灌溉耕地提取的空缺。结果表明：流域内灌溉耕地总面积为1958.24 km²，商都、兴和、尚义、康保和化德5县耕地总面积占流域耕地总面积的94%，灌溉耕地分别为616.67 km²、337.36 km²、409.85 km²、290.93 km²和239.38 km²，主要种植葵花、甜菜、马铃薯等生长季从4月初到9月底的长生育期作物和生长季从5月初到8月初的蔬菜；张北、察哈尔右翼前旗、后旗和镶黄旗耕地占流域耕地总面积为6%，4县灌溉耕地面积共64.05 km²。最后，通过真实样本进行验证，总分类精度为93.18%，Kappa系数为0.91。结果表明，用该数据融合模型获得的NDVI时间序列能反映作物真实变化情况，并且使用支持向量机提取察汗淖尔流域灌溉耕地效果较好。
关键词:灌溉耕地/
NDVI/
ESTARFM/
数据融合/
支持向量机
Abstract:Remote sensing extraction technology for crops is a promising research method for remote sensing applications. However, it is limited by the spatial and temporal resolution of the remote sensing data; it is difficult to identify crops with similar spectral characteristics. In this study, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to fuse MODIS and Landsat data to obtain normalized difference vegetation index (NDVI) time series data of the Chahannur Basin with a temporal resolution of 8 days and a spatial resolution of 30 m. The correlation coefficient between the ESTARFM NDVI and the Landsat NDVI during the same period was 0.94. Harmonic analysis of the NDVI time series filter was used to smooth the ESTARFM NDVI data. Finally, the irrigated arable land was extracted using a support vector machine (SVM) with irrigated cultivated land samples. The results showed that the total area of irrigated arable land in the watershed is 1958.24 km². The total area of Shangdu, Xinghe, Shangyi, Kangbao, and Huade Counties accounts for 94% of the watershed, and the irrigated arable land is 616.67 km², 337.36 km², 409.85 km², 290.93 km², and 239.38 km², respectively. The region primarily grows sunflowers, beets, potatoes, and other long-growing crops from the beginning of April to the end of September and vegetables (including celery, Chinese cabbage, cabbage) from the beginning of May to the beginning of August. Cultivated land in Zhangbei, Chahar Youyiqianqi, Houqi, and Xianghuangqi Counties accounts for 6% of the total cultivated land in the basin, and the total irrigated land in the four counties is 64.05 km². Real sample verification indicates that the total classification accuracy is 93.18%, and the Kappa coefficient is 0.91. The results show that the NDVI time series obtained from the data fusion model reflects real changes in crops, and the use of SVMs to extract irrigation arable land in the Chahannur Basin is suitable.
Key words:Irrigated land/
Normalized difference vegetation index (NDVI)/
Enhanced spatial and temporal adaptive reflectance fusion mode (ESTARFM)/
Data fusion/
Support vector machine (SVM)

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图1研究区地理位置示意图
Figure1.Location map of the study area


下载: 全尺寸图片幻灯片


图2研究区Landsat NDVI影像与ESTARFM融合NDVI影像对比
a: 2017年8月25日Landsat-NDVI影像; b: ESTARFM融合的2017年8月25日NDVI; (c)和(d)分别是局部放大图像。a: Landsat-NDVI images in August 25, 2017; b: ESTARFM-integrated NDVI in August 25, 2017; (c) and (d) are partially enlarged images, respectively.
Figure2.Comparison of Landsat NDVI image and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) fusion NDVI image of the study area


下载: 全尺寸图片幻灯片


图3研究区耕地掩膜后ESTARFM融合NDVI影像
Figure3.Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) fusion NDVI image after farmland masking of the study area


下载: 全尺寸图片幻灯片


图4Landsat NDVI影像与ESTARFM NDVI影像散点图
Figure4.Scatter plot of Landsat NDVI image and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) NDVI image


下载: 全尺寸图片幻灯片


图5研究区26景高时空分辨率ESTARFM NDVI影像
DOY(day-of-year)为年积日, 表示一年中的一天。DOY is the accumulated day of the year, which means the day of the year.
Figure5.26 scenes high spatial and temporal resolution Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) NDVI image of the study area


下载: 全尺寸图片幻灯片


图6HANTS滤波后耕地NDVI时间序列变化曲线
Figure6.Time series change curves of cultivated land NDVI after Harmonic Analysis of NDVI Time-Series (HANTS) filtering


下载: 全尺寸图片幻灯片


图7研究区内基于SVM灌溉耕地空间分布提取结果
Figure7.Extraction results of arable land spatial distribution based on SVM of the study area


下载: 全尺寸图片幻灯片

表1察汗淖尔流域主要作物生育期
Table1.Growth periods of main crops in the Chahannur basin

	4月April			5月May			6月June			7月July			8月August			9月September			10月上旬 Early in October
	上旬 Early	中旬 Mid-term	下旬 Late	上旬 Early	中旬 Mid-term	下旬 Late	上旬 Early	中旬 Mid-term	下旬 Late	上旬 Early	中旬 Mid-term	下旬 Late	上旬 Early	中旬 Mid-term	下旬 Late	上旬 Early	中旬 Mid-term	下旬 Late
马铃薯 Potato			播种 Sowing	芽条生长期 Sprout growth period		幼苗期 Seedling stage		块茎形成期 Tuber formation				块茎累积期 Tuber accumulation period				淀粉累积期 Starch accumulation period			成熟 Mature
葵花 Sunflower						播种 Sowing		出苗 Emergence				现蕾 Budding	开花 Flowering
甜菜 Beat				块根分化形成期 Tuber diiferentiation			叶从快速牛长期 Rhizome rapid growth period				块根及糖份增长期 Root and sugar growth period					糖分累积期 Sugar accumulation period
芹菜 Celery	台苗(大棚) Nerser (greenhouse planting)					定植 Colonization						采收 Harvest
甘蓝 Cabbage								苗期、莲座期 Seedling and rosette stage		结球期 Heading phase
大白菜 Chinese Cabbage								苗期 Seedling stage		莲座期 Rosette stage			结球期 Heading phase
小麦 Wheat			播种 Sowing	出苗 Emergence		分蘖 Tiller	拔节 Jointing			抽穗 Earing					成熟 Mature
莜麦 Naked Oats			播种 Sowing	出苗 Emergence		分蘖 Tiller	拔节 Jointing				抽穗 Earing				成熟 Mature
胡麻 Flax				播种 Sowing		出苗 Emergence		现蕾期 Budding period			花期 Flowering		灌浆期 Grouting period			成熟 Mature

下载: 导出CSV
表2研究区不同类型作物的支持向量机(SVM)分类结果混淆矩阵
Table2.Confusion matrix of Support Vector Machine (SVM) classification results of different types of crops in the study area ?%

项目 Item	灌溉耕地作物Irrigated crop					雨养Rainfed crop
	马铃薯Potato	甜菜Beet	葵花Sunflower	蔬菜Vegetables		小麦Wheat
制图精度Producer accuracy	96.00	96.23	88.24	85.45		96.23
用户精度User accuracy	96.30	94.44	90.00	97.92		87.93
错分误差Commission error	3.70	5.56	10.00	2.08		12.07
漏分误差Omission error	0	3.77	11.76	14.55		3.77
总分类精度=93.18%, Kappa系数=0.91。Overall accuracy=93.18%; Kappa coefficient=0.91.

下载: 导出CSV

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