Modeling the Topographic Effect on Directional Anisotropies of Land Surface Temperature from Thermal Remote Sensing

Abstract

In mountainous surfaces, land surface temperature (LST) plays a vital role in surface energy budget and vegetation–soil ecosystems. Despite advancements in retrieving LST from thermal infrared measurements at various spatial and temporal scales, accurately estimating LST for complex terrain remains challenging. This challenge arises from the conflict between the topographic effect and the assumption of flatten surface in many existing studies. In the absence of a simple and practical model for the topographic effect on the directional anisotropies of LST (LSTDA) over mountainous areas, the equivalent slope method is introduced to bridge the gap between studies conducted on flat surfaces and complex terrain. The proposed thermal equivalent slope kernel-driven (TESKD) model is validated using measurements and simulations from an unmanned aerial vehicle (UAV) system and a 3-dimensional raytracing model, respectively. Results indicate the following: (a) Under varying topographic conditions, vegetation cover, and solar zenith angles, there is a significant impact of topography on LSTDA. The average effect is greater than 0.5 K and can reach up to 1.5 K at the higher solar zenith angle (50°). (b) Based on UAV data, TESKD provides a better explanation and fitting effect for LSTDA in 3 typical mountainous surfaces including valley, peak, and solo-slope, with an average root mean square error (RMSE) of 0.27 K and an average coefficient of determinations of 0.628 of the 3 conditions, compared to a flat model (0.35 K and 0.335). (c) Based on simulations, TESKD exhibits more than a 30% improvement in accuracy, and for sparsely vegetated surfaces, the difference in RMSE can be up to 0.8 K when considering the topographic effect compared to not considering it. The new model can help to understand the radiative transfer process in heterogeneous mountainous surfaces and serves as a valuable tool for studies associated with water and carbon cycles.