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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 R 2 ≈ 0. 9 , and on soil loss and sediment yield of R 2 ≈ 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. • We developed a deep learning model estimating the ground cover inputs of the RHEM. • Litter and Shrubs are the most and Bio Crusts is the least accurate estimations. • We achieved an R 2 ≈ 0. 9 for runoff and ≈ 0. 4 for soil loss by the estimated covers. [ABSTRACT FROM AUTHOR]

• A novel early crop prediction system to produce crop maps early in the growing season. • Bidirectional ConvLSTM and Decoder end-to-end neural network modules were used. • Deep neural network shown to learn historical trends better than Markov Chain method. • The approach has potential to predict early crop type maps with acceptable accuracy. Reliable crop type maps are needed early in the growing season to retrieve crop type information from satellite imagery in order to produce in-season crop condition and yield outlooks. However, the inability to have early crop maps due to limited earth observations available during the beginning of the season is a major challenge for developing reliable satellite-based early warning systems. This paper introduces a novel crop type prediction modeling system based on deep Neural Networks (NN) to produce preseason crop type maps at the field scale resolution using historical crop maps. The architecture of this modeling system comprises of two end-to-end NN based modules that form an autoencoder configuration: a spatio-temporal Encoder, built off of the Bidirectional ConvLSTM network, and a Decoder, which can learn both spatial and temporal patterns necessary to accurately predict the crop sequences. To build this system, we used USDA-NASS historical Cropland Data Layer (CDL) data of Nebraska and trained nine different neural network models using a combination of three rotation cycles (3-, 4- and 5-year rotations) and three sets of historical CDL data with varying durations (2010–2016; 2006–2016; and 2002–2016). All trained models performed well when applied to predict 2017 land cover, and achieved maximum accuracy of 88%. However, the amount of training time taken to reach 88% accuracy varies for each model. Models with more temporal information were found to learn faster and achieve maximum accuracy quickly. Further, we compared the NN model with a Markov Chain (MC) based approach, a common approach used for crop pattern recognition, by applying them to predict 2018 and 2019 land cover maps using 2006–2016 CDL data. Results showed that the NN model outperformed the MC model with higher overall accuracy (0.77) and kappa coefficient (0.57) compared to that of the MC model (overall accuracy = 0.67 and kappa coefficient = 0.44). In summary, our novel system based on deep neural networks showed that it can learn complex spatial and temporal patterns from the historical land cover data and produce reasonable early crop maps, which can be used in satellite-based early warning systems. [ABSTRACT FROM AUTHOR]

• A machine learning framework for prediction of crop planting maps is developed. • The large-area crop planting map is created before the beginning of a growing season. • Prediction result is expected to reach 88% agreement with future CDL. • The predicted crop acreage is highly correlated (R2 > 0.9) with the official statistics. An accurate crop planting map can provide essential information for decision support in agriculture. The method of post-season and in-season crop mapping has been widely studied in the land use and land cover community. However, it remains a challenge to predict the spatial distribution of crop planting before the growing season. This paper is the first attempt to use machine learning approach on the prediction of field-level annual crop planting from historical crop planting maps. We present an end-to-end machine learning framework for crop planting prediction using Cropland Data Layer (CDL) time series as reference data and multi-layer artificial neural network as prediction model. The proposed framework was first tested at Lancaster County of Nebraska State, then scaled up to the U.S. Corn Belt. According to the experiment results from 53 Agricultural Statistics Districts, we found the machine-learned crop planting map was expected to reach 88% agreement with the future CDL. Meanwhile, the crop acreage estimates derived from the machine-learned prediction were highly correlated (R2 > 0.9) with the crop acreage estimates of CDL and official statistics by the U.S. Department of Agriculture National Agricultural Statistics Service. This study provides a low-cost and efficient way to predict annual crop planting map, which can be used to support many agricultural applications and decision makings before the beginning of a growing season. [ABSTRACT FROM AUTHOR]