Monthly air temperature dataset at 1-km spatial resolution over the Tibetan Plateau from 2001 to 2020 derived from MODIS

However, the meteorological stations are sparsely and unevenly distributed in the Tibetan Plateau, which influences the spatial representativeness of meteorological data on the climate change of the whole plateau. Satellite remote sensing provides a new approach to monitor the climate change at large scale.

This paper aims to develop a 1-km resolution monthly air temperature dataset during 2001-2020 over the Tibetan Plateau by remote sensing. First, 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 were derived from MODIS, DEM and CTI datasets. Cubist was employed to develop models for estimating monthly average, maximum and minimum air temperature, and the optimal models were determined by cross-validation and parameter tuning.