{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:24:40Z","timestamp":1764332680333,"version":"build-2065373602"},"reference-count":103,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,6]],"date-time":"2018-12-06T00:00:00Z","timestamp":1544054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC17K0339"],"award-info":[{"award-number":["80NSSC17K0339"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer-recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data).<\/jats:p>","DOI":"10.3390\/rs10121968","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T03:46:14Z","timestamp":1544154374000},"page":"1968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms"],"prefix":"10.3390","volume":"10","author":[{"given":"Nathaniel","family":"Levitan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA"}]},{"given":"Barry","family":"Gross","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,6]]},"reference":[{"key":"ref_1","unstructured":"Levitan, N., and Gross, B. 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