MODIS_LST与AMSR-E_BT的相关性及地表温度反演

本文以2007年和2008年MODIS每日地表温度（LST）数据及AMSR-E地表亮温（BT）数据为研究对象,结合土地覆盖类型数据,统计分析MODIS_LST与AMSR-E_BT在不同土地覆盖类型、频率和极化方式条件下的相关性。结果表明,频率在18.7、23.8和36.5 GHz的AMSR-E-BT与MODIS_LST的相关性较大,且在垂直极化通道上的相关性较在水平极化上大;不同土地覆盖类型,与MODIS_LST相关性较大所对应的AMSR-E微波通道不同。同时,考虑混合像元问题对相关性的影响,对25种不同地物类型组合下MODIS_LST与AMSR-E-BT的相关性进行统计分析,发现混合像元中地物类型越多,则二者相关性越小。最后,采用多元线性回归分析法,根据不同土地覆盖类型建立反演回归模型,对部分研究区域MODIS-LST进行反演,误差平均在±3.15 K以内,与不考虑下垫面覆盖的模型比较,反演MODIS_LST精度平均提高了1.5 K。

The Correlation Analysis Between MODIS_LST and AMSR-E_BT and Study of LST Retrieval Method

By taking MODIS daily land surface temperature (LST) data and AMSR-E brightness temperature (BT) data from 2007 to 2008 as the input, combining with the land cover type data, the statistics and analysis of the relativity between MODIS_LST and AMSR-E_BT in different land cover types, channels and polarization ways are produced. Based on the International Geosphere-Biosphere Program (IGBP) vegetation classification scheme, land cover data is re-classified into seven types, including water, forest land, grass land, farmland, urban land, desert land and other land cover types. The statistical result shows that the correlation is apparent between MODIS_LST and AMSR-E_BT in 18.7 GHz, 23.8 GHz and 36.5 GHz channel, and it reveals a higher correlation in the vertical channel compared to the horizontal channel. Moreover, this paper finds out that the microwave channel of AMSR-E_BT, which has the highest relativity with MODIS-LST, is different with respect to different land cover types. In addition, by considering the impact of mixed pixel, this paper analyzes the correlation between MODIS_LST and AMSR-E_BT for 25 types of land cover type combinations. It is inferred that the correlation declines as the quantity of land cover types in the single mixed pixel increases. Finally, according to different land cover type combinations, the inversion model is established by adopting the multivariate linear regression method, and this model has been applied to inverse MODIS_LST in a part of the study area. Inversion results reveal that the error is limited in ±3.15 K in average, and the inversion accuracy is raised by 1.5 K successfully in comparison with the inversion model without considering land cover type combination. However, there are some problems with MODIS land surface temperature inversion by using AMSR-E brightness temperature, such as the space resolution variation between MODIS LST and AMSR-E_BT, changes of land cover type with changing seasons and the influence of relative humidity of land cover on the retrieval accuracy. Therefore, selecting land surface bright temperature with a high quality and space resolution, considering season variations and classification accuracy of land cover type factors are future research directions.