Generation of global 1 km daily land surface–air temperature difference and sensible heat flux products from 2000 to 2020

作者：Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Han Ma, Jianglei Xu, Wenyuan Li, Yichuan Ma, Fengjiao Zhang, Husheng Fang · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-5571-2025 · 被引用次数：4 · 研究领域：Meteorological Phenomena and Simulations、Urban Heat Island Mitigation、Climate variability and models

Abstract. Accurate estimation of land surface sensible heat flux (H) is crucial for comprehending the dynamics of surface energy transfer and the cycles of water and carbon. Yet, existing H products mainly are meteorological reanalysis datasets with coarse spatial resolutions and high uncertainties. FLUXCOM is the sole remotely sensed product with its 0.0833° spatial and 8- temporal resolution spanning from 2001 to 2015, so there is still a need for accurate and high spatial resolution global product based on satellite data. To address these issues, we generated the first global high resolution (1 km) daily H product from 2000 to 2020 using long short-term memory (LSTM) deep learning models, incorporating data from the Global LAnd Surface Satellite (GLASS) product suite. Furthermore, considering that the difference between land surface temperature and air temperature (Ts-a) is a key driver of H, we introduce the first global accurate satellite-based Ts-a product. This product refines the uncertainty compared with obtaining Ts-a directly from existing products by subtracting air temperature from land surface temperature. Our model, distinct from previous models that estimate H per pixel through physically-based models requiring parameters that are not readily accessible, can conveniently derive global values and efficiently capture nonlinear interactions. Additionally, it accounts for the temporal variation of H. Validation against independent in-situ measurements yielded a ro...