An Adjacency-Effect-Based Approach for Accuracy Improvement in Satellite Land Surface Temperature Disaggregation

One of the key parameters that affects the accuracy of land surface temperature (LST) disaggregation is the environmental variables that are fed to the disaggregation model. The aim of this article is to present a new strategy for the disaggregation of LST based on adjacency effects. To do this, a dataset obtained from satellite images and auxiliary information from five European cities was used. First, maps of environmental variables that affect LST were collected. Second, a map of effective environmental variables was produced by calculating and applying the influence of the adjacency effects of each environmental variable based on the proposed weighted inverse distance kernel. Finally, the datasets of environmental variables and effective environmental variables were used separately in the disaggregation process to convert LST at 990 m to disaggregated LST (DLST) at 90 m. The mean RMSEs between LST and DLST obtained without considering the adjacency effects approach for the built-up, agricultural, pasture, forest, and water lands in the cold (warm) season were 0.85 (1.55), 0.72 (1.31), 0.98 (1.63), 0.59 (1.2), and 0.40 (1.12) K, respectively. Taking into account the adjacency effects, the mean RMSE between LST and DLST on built-up, agricultural, pasture, forest, and water lands used in the cold season decreased by 0.35, 0.17, 0.13, 0.09, and 0.03 K, respectively. These values were 0.54, 0.36, 0.33, 0.34, and 0.07 K for the warm season, respectively. The result showed that considering adjacency effects increases the accuracy of LST disaggregation.