Abstract
The Tibetan Plateau has experienced a rapid climate rise in recent years, which is a hot issue in the study of global change. However, the meteorological stations are sparsely and unevenly distributed on the Tibetan Plateau, which negatively influences the spatial representativeness of meteorological data on the climate change of the whole plateau. Satellite remote sensing provides a new approach to the large-scale research on the climate change. First, we extracted the monthly daytime land surface temperature, monthly nighttime land surface temperature, monthly clear sky days, monthly surface albedo, monthly NDVI, monthly NDSI, altitude, astronomical radiation radiance and CTI from MODIS remote sensing data, DEM data and CTI datasets. Then, we employed the Cubist algorithm to develop models for estimating monthly average, maximum and minimum air temperature through cross-validation and parameter tuning. Finally, we obtained dataset of MODIS-based monthly air temperature with a spatial resolution of 1 km on the Tibetan Plateau from 2001 to 2020. The dataset is helpful for further understanding the climate change on the Tibetan Plateau, and provides important database for the climate change research on the Tibetan Plateau.
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