| A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data |
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| 作者单位： | MAO KeBiao1，2，3，SHI JianCheng2，LI ZhaoLiang4，QIN ZhiHao1，5，LI ManChun4 & XU Bin1 1 Key Laboratory of Resources Remote Sensing and Digital Agriculture，MOA，Institute of Agricultural Resources and Regional Planning，Chinese Academy of Agricultural Sciences，Beijing 100081，China; 2 State Key Laboratory of Remote Sensing Science，Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University，Beijing 100101，China. 3 Graduate University of Chinese Academy of Sciences，Beijing 100049，China; 4 Institute of Geographical Science and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China; 5 International Institute for Earth System Science，Nanjing University，Nanjing 210093，China |
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| 基金项目： | 国家自然科学基金;农业部资源遥感与数字农业重点开放实验室开放基金;国家高技术研究发展计划(863计划) |
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| 摘 要： | AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface tem-perature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM,the difference of different frequen-cies can eliminate the influence of water in soil and atmosphere,and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately,the land surface should be at least classified into three types:water covered surface,snow covered surface,and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm,we built different equations for different ranges of temperature. The average land surface temperature er-ror is about 2―3℃ relative to the MODIS LST product.
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A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data |
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| Authors: | Mao KeBiao Shi JianCheng Li ZhaoLiang Qin ZhiHao Li ManChun Xu Bin |
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| Institution: | 1.Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA, Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing,China;2.State Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University,Beijing,China;3.Graduate University of Chinese Academy of Sciences,Beijing,China;4.Institute of Geographical Science and Natural Resources Research,Chinese Academy of Sciences,Beijing,China;5.International Institute for Earth System Science,Nanjing University,Nanjing,China |
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| Abstract: | AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between
the brightness of all AMSR-E bands and the MODIS land surface temperature product indicated that the 89 GHz vertical polarization
is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM, the difference of
different frequencies can eliminate the influence of water in soil and atmosphere, and also the surface roughness partly.
The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve
land surface temperature more accurately, the land surface should be at least classified into three types: water covered surface,
snow covered surface, and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of
the algorithm, we built different equations for different ranges of temperature. The average land surface temperature error
is about 2–3°C relative to the MODIS LST product.
Supported by the National Natural Science Foundation of China (Grant Nos. 90302008 and 40571101), the Open Fund of Key Laboratory
of Resources Remote Sensing and Digital Agriculture, MOA, and Project 863 (Grant No. 2006AA12Z103) |
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| Keywords: | brightness temperature LST AMSR-E MODIS AIEM |
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