Reconstruction of Cloudy Land Surface Temperature by Combining Surface Energy Balance Theory and Solar-Cloud-Satellite Geometry

Abstract

Reconstruction of land surface temperature (LST) under clouds has been an area of significant research interest in recent years. Solar-cloud-satellite geometry has significant impacts on satellite-derived land surface biophysical parameters, such as radiation flux and LST; however, current studies often neglect these influences on reconstruction of cloudy LST. To address this challenge, we developed an integrated methodology for generating seamless all-weather LST based on surface energy balance (SEB) theory with consideration of the solar-cloud-satellite geometry effects both on LST and radiation. Cloudy pixels were categorized (radiation-unobstructed and radiation-obstructed clouds) and reconstructed separately to account for geometry effects. Moreover, corrections were incorporated to mitigate geometry effects on net surface shortwave radiation (NSSR), the crucial intermediate input data for estimating cloudy LST. Compared to the existing method, validation results using ground measurements from the Surface Radiation Budget (SURFRAD) network demonstrate significant improvements, with average errors decreasing from 5.62 to 1.86 K under radiation-unobstructed conditions and from 3.26 to 1.33 K under radiation-obstructed conditions, respectively. This study contributes valuable insights to reconstructing LST under varying cloudy conditions, indicating the importance of considering geometry effects for robust and reliable cloudy LST assessments.