Abstract:According to the requirements of weather, climate and ecological environment monitoring for land surface temperature (LST) at high spatial resolution and all-weather, this paper develops the spatial downscaling algorithm of LST from geostationary meteorological satellite observation and the reconstruction algorithm of LST under cloud from polar orbiting meteorological satellite observation respectively. The LST spatial downscaling model of geostationary meteorological satellite makes full use of the advantages of geostationary meteorological satellite observation in both high time-frequency and multi-spectrum. A nonlinear statistical regression model is established based on the daily variation characteristics of LST and brightness temperature of relevant channels of the same remote sensor of Fengyun-4A (FY-4A). And the underlying surface types are also taken into consideration. The method is applied to the LST downscaling of FY-4A AGRI (advanced geosynchronous radiation imager). The results show that the developed downscaling model can not only downscale the LST of FY-4A AGRI from 4 km to 2 km, but also maintain the accuracy of LST before downscaling, and the maximum RMSE of LST before and after downscaling is 1.35 K. For the LST reconstruction under cloud of polar orbiting meteorological satellite, the DINEOF model is developed, and the secondary correction of the results is carried out based on Land-Use data (LU), so as to realize the all-weather acquisition of polar orbiting meteorological satellite LST. The method is applied to polar orbiting meteorological satellite FY-3D medium mesolution spectral imager LST, and the results are as expected.