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Full-Text Articles in Social and Behavioral Sciences
Utilizing Collocated Crop Growth Model Simulations To Train Agronomic Satellite Retrieval Algorithms, Nathaniel Levitan, Barry Gross
Utilizing Collocated Crop Growth Model Simulations To Train Agronomic Satellite Retrieval Algorithms, Nathaniel Levitan, Barry Gross
Publications and Research
Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional …