A simple retrieval method of land surface temperature from AMSR-E passive microwave data—A case study over Southern China during the strong snow disaster of 2008 |
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| Authors: | Shui-sen Chen Xiu-zhi Chen Wei-qi Chen Yong-xian Su Da Li |
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| Institution: | 1. Guangzhou Institute of Geochemistry, CAS, Guangzhou 510070, China;2. Guangzhou Institute of Geography, Guangzhou 510070, China;3. Graduate School of Chinese Academy of Sciences, Beijing 100049, China;4. Department of Geography, Florida State University, Tallahassee, FL 32304, USA |
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| Abstract: | The analysis of the passive microwave radiance transfer equation certifies that there is a linear relationship between satellite-generated brightness temperatures (BT) and in situ observation temperature and that land surface temperature (LST) is largely influenced by vegetation cover conditions. Microwave polarization difference index (MPDI) is an effective indicator for characterizing the land surface vegetation cover density. Based on the analysis of LST models from AMSR-E BT with 6.9 GHz MPDI intervals at 0.04, 0.02 and 0.01, respectively, this paper developed a simplified LST regression model with MPDI-based five land cover types, combining observation temperatures from 86 meteorological observation stations. The study shows that smaller MPDI intervals can obtain higher accuracy of AMSR-E LST simulation, and that the combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and BT information from the AMSR-E HDF imagery files. The RMSE of the five LST simulation algorithms is between 1.47 and 1.92 °C, with an average LST retrieval error of 0.91–1.30 °C. Besides, only 7 polarization bands and 5 land surface types are required by the proposed simplified model. The new LST simulation models appears to be more effective for producing LST compared to past most studies, of which the accuracy used to be more than 2 °C. This study is one of the rare applications that combine the meteorological observation temperature with MPDI to produce the LST regression analysis algorithms with less RMSE from AMSR-E data. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions. |
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| Keywords: | AMSR-E LST MPDI Vegetation classification Microwave remote sensing GIS Southern China |
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