A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas - Wang, Ju; Tang, Bo-Hui; Zhu, Xinming; Fan, Dong; Li, Menghua; Chen, Junyi (Frontiers in Earth Science, 2025)
Land surface temperature (LST) is a crucial factor for reflecting climate change. High spatial resolution LST is particularly significant for environmental monitoring in plateau and mountainous areas, which are characterized by rugged landscapes, diverse ecosystems, and high spatial variability in LST. Typical plateau mountainous areas in Diqing Tibetan Autonomous Prefecture and Dali Bai Autonomous Prefecture were selected as study areas. Three machine learning models, including Back Propagation (BP) Neural Network, random forest (RF), and extreme gradient boosting (XGBoost), and two classic single-factor linear regression models (DisTrad and TsHARP) were compared. Particle Swarm Optimization (PSO) was introduced to optimize hyperparameters of three machine learning methods. Regression factors suitable for plateau mountainous areas, including normalized vegetation index (NDVI), normalized multi-band drought index (NMDI), bare soil index (BSI), normalized difference snow index (NDSI), elevation, surface roughness (SR), and Hillshade were selected. The performance of five models was analyzed from the perspective of different spatial resolutions and land cover types. The results revealed that the performance of machine learning models is better than traditional linear models in both study areas. Based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. For study area A, the results were R2 = 0.891, RMSE = 2.67 K, and MAE = 1.83 K, while for study area B, the values were R2 = 0.832, RMSE = 1.98 K, and MAE = 1.54 K. In addition, among different land cover types, the XGBoost model has the best performance in both study areas. Moreover, the larger the ratio of initial resolution to target resolution, the lower the accuracy of downscaled LST (DLST). In summary, the XGBoost model is more suitable for downscaling LST in plateau mountainous areas.