用被动微波AMSR数据反演地表温度及发射率的方法研究

 针对对地观测卫星多传感器的特点，提出了借助MODIS地表温度产品从被动微波数据中反演地表温度的方法。即利用MODIS地表温度产品和AMSR不同通道之间的亮度温度，建立地表温度的反演方程。该方法克服了以往需要测量同步数据的困难，为不同传感器之间的参数反演相互校正和综合利用多传感器的数据提供实际应用和理论依据。文中以MODIS地表温度产品作为评价标准，对方法进行检验，其平均误差为2~3℃。另外，微波的发射率是土壤水分反演的关键参数，在对微波地表温度反演的基础上，进一步对发射率进行了研究。

THE LAND SURFACE TEMPERATURE AND EMISSIVITY RETRIEVED FROM THE AMSR PASSIVE MICROWAVE DATA

 AMSR and MODIS are two EOS (Earth Observing System) instruments in Aqua satellite. A regression analysis between the brightness of all AMSR bands and the MODIS land surface temperature product provided by NASA indicates good correlation, and hence retrieving land surface temperature from AMSR passive data is available without ground true data (soil moisture and land surface type). The analytical results in such regions as North Africa, Northeast China, Tibet China and India indicate that the radiation mechanism of snow-covered surface is different from mechanism of other conditions. In order to retrieve land surface temperature more accurately, we can classify the land surface at least into two groups. For land surface covered with no snow, the average land surface temperature error is about 2℃ relative to the MODIS LST product. For snow-covered land surface, the average land surface temperature error is about 2~3℃ relative to the MODIS LST product. Besides, the emissivity of passive microwave is a very important parameter for retrieving soil moisture. The authors computed the emissivity through land surface temperature retrieved by the statistical regression method and made an analysis.