AMSR-E亮温数据与MODIS陆表分类产品结合反演全球陆表温度

Global Land Surface Temperature Retrieval with AMSR-E Brightness Temperature and MODIS Land Cover Type Products

AMSR—E被动微波传感器获取的亮温数据与MODIS陆表分类产品（MOD12）相结合，将全球陆表分为16类，并假设每种类型的地表在各个被动微波通道具有较一致的发射率，在此基础上针对每种陆表类型分别建立了陆表温度反演算法。在算法的建立过程中，为了避免混合像元以及冻土、积雪发射率不确定性带来的影响，仅对单一地表类型占90％以上以及MODIS陆表温度产品高于273K的被动微波像元进行回归。同时，考虑到降雨对回归结果的影响，在数据选择中加入了降雨判识，在被动微波亮温数据中除去了降雨像元。利用上述算法，用2004年1～10月的全球部分地区AMSR—E数据在MODIS陆表分类产品的基础上对每种地表类型分别进行了陆表温度反演，并与MODIS陆表温度产品进行对比，结果显示相关性较好，均方根误差为2～4K。

AMSR-E brightness temperature data and MODIS land cover type data are combined to retrieval land surface temperature （LST）. Using the MODIS data, we classified the global land surface into 16 types, and make assumption that in each land surface type, the emissivity of each channel is constant separately. Based on this assumption and the land surface types, we build land surface retrieval algorithms for each land surface types. Before the algorithms were built, we chose the pixels that with above 90% single land type area and pixels that the MODIS land surface temperature exceed 273 K. This is because the emissivity is dubious in snow cover, ice cover, and mixed-pixels. Coequally, considering the precipitation effect, we winkled the rainfall pixel from our dataset. Finally, we employed the algorithms to retrieval global land surface temperature of Jan to Oct, 2004. Compared with the MODIS land surface temperature product, the result is fine, with RMSE of 2-4 K.