A practical method for reducing terrain effect on land surface temperature using random forest regression

Land surface temperature (LST) plays a key role in connecting land surface energy and water exchanges with near-surface atmosphere. However, the spatial distribution of LST over mountainous areas is not only strongly affected by the differences in surface thermal properties but also by the differences in thermal or radiative environment induced by the topographic variations, presenting significant terrain effect. This effect greatly hinders researches on surface energy fluxes and soil moisture estimation in these regions. To normalize the terrain effect, a random forest (RF)-based normalization approach was developed in this study and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data over a typical mountain region in the eastern part of the Tibetan Plateau, China. An LST linking model was first constructed to express the complicated interrelationship between LST and other surface variables, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), surface albedo (ALB), cumulative incident solar radiation (CSR), normalized difference water index (NDWI), surface elevation (ELV), and surface slope (SLP). The estimation results well indicated the good accuracy of the model with the coefficient of determination (R²) above 0.92 for 90 selected days in 2015. Based on the good LST linking model, the LST normalization was achieved by replacing the topography affected factors (CSR, ELV, and SLP) with reference values for each pixel when keeping the rest status factors as their original values. The normalization result was indirectly validated by the normalization results with the traditional method based on the linear correction upon the regression between surface elevation and LST. The cross-validation clearly indicated the proposed method had an obvious advantage in reducing the topography-induced LST difference based on the average decrease in the temperature range (9.51 K) and the standard deviation (1.09 K) for the images on the 90 selected days over the study area. In contrast, the traditional method was hard to capture the terrain effect only based on the relationship between LST and surface elevation due to the complex interaction between LST and other factors. Overall, the proposed method shows a good application potential for normalizing the terrain effect on LST, and the results will be helpful for the LST-based estimation of surface energy fluxes or soil moisture over mountainous areas.