Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts

Abstract

Satellite passive microwave (PMW) observations are important data sources for obtaining land surface temperature (LST) especially for cloudy conditions. Considering the generally poorer spatial representativeness of PMW observations, fusion of PMW-based and thermal infrared-(TIR-) based observations have been considered as an important approach to generate all-weather LST with appropriate resolution. However, the existing penetration difference between PMW-based and TIR-based observations has been an obstacle against this objective. This is especially the case for desert regions, where the penetration depth of PMW observations is larger than under other conditions and the relationship between TIR-based and PMW-based signals are thus more complicated. In this regard, this study develops an easy and high-efficiency model suitable for transforming PMW-based subsurface LST to TIR-like skin surface temperature in four regions across the world characterized by typical arid climate. The developed model employs microwave-derived surface soil moisture (SSM) and vegetation transmissivity (VT) as major inputs to quantify that “resampling depth difference” between TIR-based and PMW-based observations. Results show that the model can significantly reduce that difference (RMSE) from >10 K to around 2–5 K in the typical desert regions. The developed model is beneficial to obtaining high-resolution all-weather LST for the vast desert regions across the world in subsequent studies.