Reconstructing Modis Lst Products Over Tibetan Plateau based on Random Forest

Land Surface Temperature (LST) is an indicator of the thermal condition at the ground-atmosphere interface. The MODIS LST products from Terra and Aqua satellites provide spatially continuous monitoring ground temperature. But it still has limitations at local scale due to atmospheric disturbances and the spatiotemporal heterogeneity of land surface. In this study, a novel algorithm based on Random Forest (RF) is proposed to reconstruct MODIS LST. The RF was trained using MOD11A1, MOD09A1 and MOD15A2 from Terra MODIS and digital elevation data from ASTER DEM as inputs. LST observations from The Tibetan Plateau Soil Moisture and Temperature Monitoring Network (TP-SMTMN) are used as target data. Experimental results indicate the algorithm can improve the estimation MODIS LST products from the aspects of accuracy and data availability.