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 watersheds and grid-based remote sensing data presents challenges in ecohydrological modeling. To address this issue, we developed a comprehensive SWAT model for the Upper Mississippi River Basin, utilizing USDA-delineated HUC (Hydrologic Unit Code)-12 subbasins averaging approximately 100 km 2 . This approach facilitated the effective integration of remotely sensed data on evapotranspiration (ET), leaf area index (LAI), and net primary productivity (NPP). To reduce prediction uncertainty, 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 R 2 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 efficacy of a HUC-12-based model in simulating water and carbon fluxes across diverse landscapes with remote sensing data. • A high-resolution SWAT model was developed for the UMRB at the HUC-12 scale. • MODIS data were used to calibrate ET, LAI, and NPP for refined land use clusters. • The high-resolution SWAT model was sensitive to land use and climate variations. • The study improved water and carbon cycling processes across diverse landscapes.