All-Weather Near-Surface Air Temperature Estimation Based on Satellite Data Over the Tibetan Plateau

Abstract

Near-surface air temperature (NSAT) plays an important role in land surface and atmosphere interactions. It is widely used in many fields, such as hydrology, climatology, and environment. Although remote sensing-based approaches for NSAT estimation have been proposed by the scientific communities, many of them are limited in cloudy areas and, thus, are not able to provide all-weather NSAT estimates. To satisfy NSAT-related applications for all-weather conditions over the Tibetan Plateau (TP), this study develops a novel model for estimating daily 1-km all-weather NSAT (AW-NSAT) based on machine learning techniques. The input variables for the AW-NSAT model include land surface temperature (LST) from a newly released satellite all-weather LST dataset (i.e., TRIMS LST), as well as other parameters. The model is trained with in situ NSAT, and the results show that the random forest model with all-weather LST as the main input yields the best performance. Validation with independent in situ NSAT shows that the AW-NSAT estimate has good accuracy: an overall root mean square error of 2.43 and an R2 of 0.93. Intercomparison with an existing NSAT dataset based on MODIS LST shows that AW-NSAT has similar accuracy. Nevertheless, AW-NSAT has an evident spatial seamless characteristic, indicating that the developed model has good ability to overcome cloud contamination by introducing TRIMS LST. The developed model provides the possibility for generating the AW-NSAT for the whole TP. Furthermore, the proposed model can also be extended to other areas and then support NSAT's subsequent applications.