{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T12:40:33Z","timestamp":1778330433877,"version":"3.51.4"},"reference-count":81,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture\u2019s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers\u2019 feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies\/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat\u2019s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.<\/jats:p>","DOI":"10.3390\/rs13214227","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:27:39Z","timestamp":1634858859000},"page":"4227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1505-6723","authenticated-orcid":false,"given":"David M.","family":"Johnson","sequence":"first","affiliation":[{"name":"United States Department of Agriculture\/National Agricultural Statistics Service, 1400 Independence Ave. SW, Washington, DC 20250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arthur","family":"Rosales","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture\/National Agricultural Statistics Service, 1400 Independence Ave. SW, Washington, DC 20250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Mueller","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture\/National Agricultural Statistics Service, 1400 Independence Ave. SW, Washington, DC 20250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Curt","family":"Reynolds","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture\/Foreign Agricultural Service, 1400 Independence Ave. SW, Washington, DC 20250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald","family":"Frantz","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture\/Foreign Agricultural Service, 1400 Independence Ave. SW, Washington, DC 20250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assaf","family":"Anyamba","sequence":"additional","affiliation":[{"name":"Goddard Space Flight Center, National Aeronautics and Space Administration\/Earth Sciences Division, Code 610, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ed","family":"Pak","sequence":"additional","affiliation":[{"name":"Goddard Space Flight Center, National Aeronautics and Space Administration\/Earth Sciences Division, Code 610, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Compton","family":"Tucker","sequence":"additional","affiliation":[{"name":"Goddard Space Flight Center, National Aeronautics and Space Administration\/Earth Sciences Division, Code 610, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","unstructured":"World Bank (2011). 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