An integrated reconstruction–downscaling framework for generating daily, all-weather 30 m land surface temperature in cloudy mountainous regions

作者：Junli Zhao, Wei Zhao, Yanqing Yang, Jiujiang Wu, Yuxin Li · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2026 · DOI：10.1016/j.jag.2026.105224 · 被引用次数：2 · 研究领域：Urban Heat Island Mitigation、Geothermal Energy Systems and Applications、Climate change and permafrost

• An integrated reconstruction–downscaling framework was developed for LST data. • The decomposition of the ATC model offers a key solution for this unified solution. • An adaptive window was adopted to ensure robust training under cloudy conditions. • Terrain and soil-moisture features enhance the explanatory capability. • It advances continuous high-resolution thermal monitoring in mountainous areas. As a key driver of land–atmosphere interactions, land surface temperature (LST) has been widely applied across various geoscientific disciplines. In mountainous regions, complex terrain and frequent cloud cover create a strong demand for LST products with higher spatiotemporal resolution. Previous studies have developed methods for cloud-covered LST reconstruction or for downscaling to alleviate the challenges of cloud obstruction and coarse resolution in thermal infrared observations. However, most of these approaches focus on a single task and seldom account for the unique characteristics of mountainous areas, which limits the applicability in cloudy mountainous regions. This study, therefore, proposes an integrated reconstruction–downscaling framework designed to simultaneously address the limitations of cloud obstruction and coarse resolution in cloudy mountainous regions. The framework was developed based on the annual temperature cycle (ATC) model, which partitions daily LST into a background temperature component estimated from the ATC coefficients and a residual term re...