Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China  

作　　者：Hua-sheng SUN Jing-feng HUANG Alfredo R. HUETE Dai-liang PENG Feng ZHANG 

机构地区：[1]Institute of Agricultural Remote Sensing and lnformation Application, Zhejiang University, Hangzhou 310029, China [2]Key Lab of Agricultural Remote Sensing and lnformation System, Hangzhou 310029, China [3]MOE Key Lab of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310029, China [4]Department of Soil, Water, and Environmental Science, University of Arizona, Tucson, AZ85721, USA [5]Resources and Environment Department, Xinjiang Agricultural University, Urumchi 830052, China [6]College of Surveying and Mapping, Xuzhou Normal University, Xuzhou 221116, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2009年第10期1509-1522,共14页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：supported by the National High-Tech Research and Development Program (863) of China(No.2006AA120101);the National Natural Science Foundation of China(No.40871158/D0106);the Key Technologies Research and Development Program of China(No.2006BAD10A01)

摘　　要：The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identifying the unique char-acteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization.The characteristic could be reflected by the enhanced vegetation index(EVI) and the land surface water index(LSWI) derived from MODIS sensor data.Algorithms for single,early,and late rice identification were obtained from selected typical test sites.The algorithms could not only separate early rice and late rice planted in the same fields,but also reduce the uncertainties.The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics,and the spatial matching was examined by ETM+(enhanced thematic mapper plus) images in a test region.Major factors that might cause errors,such as the coarse spatial resolution and noises in the MODIS data,were discussed.Although not suitable for monitoring the inter-annual variations due to some inevitable factors,the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale,and they might provide reference for further studies.The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderateresolution imaging spectroradiometer （MODIS） data in China. Paddy rice fields were extracted by identifying the unique characteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization. The characteristic could be reflected by the enhanced vegetation index （EVI） and the land surface water index （LSWI） derived from MODIS sensor data. Algorithms for single, early, and late rice identification were obtained from selected typical test sites. The algorithms could not only separate early rice and late rice planted in the same fields, but also reduce the uncertainties. The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics, and the spatial matching was examined by ETM＋ （enhanced thematic mapper plus） images in a test region. Major factors that might cause errors, such as the coarse spatial resolution and noises in the MODIS data, were discussed. Although not suitable for monitoring the inter-annual variations due to some inevitable factors, the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale, and they might provide reference for further studies.

关 键 词：Remote sensing Moderate-resolution imaging spectroradiometer （MODIS） Enhanced vegetation index （EVI） Land surface water index （LSWI） Paddy rice China 

分 类 号：TH744.1[机械工程—光学工程]