An Artificial Neural Network to Estimate the Foliar and Ground Cover Input Variables of the Rangeland Hydrology and Erosion Model

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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.



Artificial Intelligence and Robotics, Environmental Monitoring, Hydrology, Natural Resources and Conservation, Natural Resources Management and Policy, Soil Science


Deep learning, remote sensing, runoff, Soil Loss, sediment yield


Published: 2023-07-12 16:24


CC BY Attribution 4.0 International

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Data Availability (Reason not available):
The preprocessed remote sensing data along with the RHEM outputs of the field data plots, our training pipeline, all of the Python scripts we used for cross-validation, and our model's pre-trained weights are open source and publicly available via \url{}. Due to data restrictions on the NRI dataset, we removed the latitude and longitude details of the field data plots from the repository.