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《Remote Sensing Technology and Application》 2012-01
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Comparative Analysis of Vegetation Index of Paddy Rice under Different Radiometric Correction Levels

Li Peng1,2,Jiang Luguang1,Feng Zhiming1(1.Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences, Beijing 100101,China;2.Graduate University of Chinese Academy of Sciences,Beijing 100049,China)  
Normalized Difference Vegetation Index(NDVI) is an important parameter that can well reflect the growth characteristics of vegetation.The retrieval of vegetation indices through satellite data are usually influenced by the sensor itself,the atmosphere,aerosols and others.Thus,the derivation of objective and accurate vegetation index is essential to reveal the vegetation growth changes and other quantitative remote sensing analysis.Based on the different levels of radiation correction(radiometric calibration and atmospheric correction),we used the digital numbers(DN) values originally from Landsat ETM+,TOA reflectance,and surface reflectance for the calculation of NDVI,respectively.According to field survey on paddy rice cropping systems in the Poyang Lake Region in China in mid-to-late September 2010,from the paddy field and the landscape scales,we monitored the temporal dynamics of Landsat-derived NDVI.The results showed that,the NDVI profile calculated from DN values,TOA reflectance and surface reflectance basically reflect the information on the paddy rice growing systems per year,and the corresponding ranges of NDVI became bigger.NDVI dropped to its low level during the transplanting stage and reached a plateau level during the panicle initiation to heading.In the development of paddy rice growth period,the NDVI curves derived from TOA reflectance and Surface reflectance could better demonstrate the growth system characteristics of paddy rice.Therefore,we concluded that the Surface reflectance retrieved from radiometric calibration and atmospheric correction should be a relatively objective and accurate parameter for NDVI calculation in the quantitative remote sensing of vegetation growth condition.
【Fund】: 国家973计划项目(2009CB421106);; 国家自然科学基金项目(40901285)资助
【CateGory Index】: S511;TP79
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