Integrating remote sensing data with watershed-scale ecohydrological modeling in diverse forest and agricultural landscapes of the Upper Mississippi River Basin

The spatial misalignment between natural watershed boundaries and grid-based remote sensing data poses challenges for incorporating spatially distributed observations into watershed-scale modeling frameworks. To tackle this, we developed a comprehensive SWAT model for the Upper Mississippi River Basin, utilizing USDA-delineated HUC (Hydrologic Unit Code)-12 subbasins (∼100 km²), to enable effective integration of remotely sensed evapotranspiration (ET), leaf area index (LAI), and net primary productivity (NPP). To improve accuracy, land use types—forests, crops, and grasslands—were clustered based on climatic and geological characteristics. Following calibration and validation from 1990 to 2020, the model exhibited robust performance, achieving R² and NSE values greater than 0.75 and maintaining percent bias below 25 % for ET, LAI, and NPP across nearly 5,000 subbasins. Additionally, simulated crop yields closely matched USDA observations. These findings highlight the effectivenes of a HUC-12-based model for simulating water and carbon fluxes across diverse landscapes using remote sensing data.