Mahmoud Saeedimoghaddam , Grey Nearing , David C. Goodrich, Mariano Hernandez, D. Phillip Guertin, Loretta J. Metz, Haiyan Wei, Guillermo E. Ponce-Campos, Shea Burns, Sarah E. McCord, Mark Nearing, C Jason Williams, Carrie-Ann Houdeshell, Mashrekur Rahman, Menberu B. Meles, Steve Barker

Models like the Rangeland Hydrology and Erosion Model (RHEM) are useful for estimating soil erosion, however, they rely on input parameters that are sometimes difficult or expensive to measure. Specifically, RHEM requires information about foliar and ground cover fractions that generally must be measured in situ, which makes it difficult to use models like RHEM to produce erosion or soil risk maps for areas exceeding the size of a hillslope such as a large watershed. We previously developed a deep learning emulator of RHEM that has low computational expense and can, in principle, be run over large areas (e.g., over the continental US). In this paper, we develop a deep learning model to estimate the RHEM ground cover inputs from remote sensing time series, reducing the need for extensive field surveys to produce erosion maps. We achieve a prediction accuracy on hillslope runoff of R2 ≈ 0.9, and on soil loss and sediment yield of R2 ≈ 0.4 at 66,643 field locations within the US. We demonstrate how this approach can be used for mapping by developing runoff, soil loss, and sediment yield maps over a 1356 km2 region of interest in Nebraska.