Advances in Methodology and Generation of All-Weather Land Surface Temperature Products From Polar-Orbiting and Geostationary Satellites: A comprehensive review

Land surface temperature (LST) is crucial for understanding surface energy budgets, hydrological cycling, and land-atmosphere interactions. However, cloud cover leads to numerous data gaps in existing remote sensing thermal infrared (TIR) LST products, seriously restricting their applications. This article provides a comprehensive review concerning both LST recovery methodologies and 26 emerging all-weather products derived from polar-orbiting and geostationary (GEO) satellites. Clarifying product distinctions will enable end users to select suitable options for diverse research. Methodologies are categorized into spatiotemporal interpolation, surface energy balance (SEB)-based physical estimation, passive microwave (PMW)-based methods, and simulated temperature-based approaches. Historical research trajectories, strengths, limitations, and potential research directions of the methodologies and products are discussed. The review reports that existing all-weather LST products generally exhibit root-mean-square errors (RMSEs) of <4 (2.5) K at instantaneous (daily mean) scales based on extensive ground measurements, comparable to clear sky retrievals. Deep learning (DL) models prominently feature in state-of-the-art interpolation and fusion approaches [e.g., long short-term memory (LSTM) and extreme gradient boosting (XGBoost)]. Product intercomparisons in various application scenarios reveal that interpolation-based products offer better texture details; however, noticeable biases exist compared to fusion-based products, especially in arid and semiarid regions, despite the high availability of clear sky samples. The bias shifts to negative at higher latitudes, due to ignored cloud radiative effects. The review emphasizes the underexplored recovery of diurnal temperature cycles (DTCs) from GEO satellites. This focus will benefit heat exposure monitoring for public health, understanding circadian rhythm responses of ecosystems to environmental changes, and harmonizing existing and forthcoming high-resolution TIR missions.