Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

摘要: Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly difficult, large-scale flux mapping is hindered by heterogeneity. Previous studies have demonstrated that can be estimated assimilating land temperature (LST) soil moisture to determine two key parameters: neutral bulk transfer coefficient (CHN) an evaporative fraction (EF). Here methodology proposed estimate Soil Moisture Active Passive (SMAP) data Geostationary Operational Environmental Satellite (GOES) LST into dual-source (DS) model using hybrid particle assimilation strategy. SMAP assimilated filter (PF), GOES adaptive batch smoother (APBS) account for large gap spatial temporal resolution. The implemented area U.S. Southern Great Plains. Assessment against observations suggests estimates better agreement with after assimilation. RMSD 30 min (daytime) reduced 6.3% (8.7%) 31.6% (37%) H LE on average. Comparison LST-only moisture-only case despite coarse resolution, not only beneficial but also successful robust estimation, particularly when uncertainties large.