{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T19:55:14Z","timestamp":1772567714168,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"NASA","doi-asserted-by":"publisher","award":["80NSSC21K1469"],"award-info":[{"award-number":["80NSSC21K1469"]}],"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>Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.<\/jats:p>","DOI":"10.3390\/rs16183451","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T09:49:19Z","timestamp":1726652959000},"page":"3451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mapping Field-Level Maize Yields in Ethiopian Smallholder Systems Using Sentinel-2 Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Zachary","family":"Mondschein","sequence":"first","affiliation":[{"name":"School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Ambica","family":"Paliwal","sequence":"additional","affiliation":[{"name":"School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"International Livestock Research Institute (ILRI), Nairobi 00100, Kenya"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6482-2669","authenticated-orcid":false,"given":"Tesfaye Shiferaw","family":"Sida","sequence":"additional","affiliation":[{"name":"International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa P.O. Box 5689, Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9522-3001","authenticated-orcid":false,"given":"Jordan","family":"Chamberlin","sequence":"additional","affiliation":[{"name":"International Maize and Wheat Improvement Center (CIMMYT), Nairobi 00621, Kenya"}]},{"given":"Runzi","family":"Wang","sequence":"additional","affiliation":[{"name":"School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6821-473X","authenticated-orcid":false,"given":"Meha","family":"Jain","sequence":"additional","affiliation":[{"name":"School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food Security: The Challenge of Feeding 9 Billion People","volume":"327","author":"Godfray","year":"2010","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20260","DOI":"10.1073\/pnas.1116437108","article-title":"Global food demand and the sustainable intensification of agriculture","volume":"108","author":"Tilman","year":"2011","journal-title":"Proc. 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