{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:00:06Z","timestamp":1774026006695,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USDA-NIFA","award":["no number"],"award-info":[{"award-number":["no number"]}]},{"DOI":"10.13039\/100014272","name":"New York Farm Viability Institute","doi-asserted-by":"publisher","award":["no number"],"award-info":[{"award-number":["no number"]}],"id":[{"id":"10.13039\/100014272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004855","name":"New York State Department of Environmental Conservation","doi-asserted-by":"publisher","award":["no number"],"award-info":[{"award-number":["no number"]}],"id":[{"id":"10.13039\/100004855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1916208"],"award-info":[{"award-number":["1916208"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix\u2122 Mapper) imagery for corn (Zea mays L.) yield prediction. A field trial was conducted with N sidedress treatments applied at four growth stages (V4, V6, V8, or V10) compared against zero-N and N-rich controls. Normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), based on flights at R4, resulted in the most accurate yield estimations, as long as sidedressing was performed before V6. Yield estimations based on earlier flights were less accurate. Estimations were most accurate when imagery from both N-rich and zero-N control plots were included, but elimination of the zero-N data only slightly reduced the accuracy. Use of a ratio approach (VITrt\/VIN-rich and YieldTrt\/YieldN-rich) enables the extension of findings across fields and only slightly reduced the model performance. Finally, a smaller plot size (9 or 75 m2 compared to 150 m2) resulted in a slightly reduced model performance. We concluded that accurate yield estimates can be obtained using NDVI and EVI2, as long as there is an N-rich strip in the field, sidedressing is performed prior to V6, and sensing takes place at R3 or R4.<\/jats:p>","DOI":"10.3390\/rs13193948","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5160-1146","authenticated-orcid":false,"given":"S.","family":"Sunoj","sequence":"first","affiliation":[{"name":"Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, NY 14853, USA"}]},{"given":"Jason","family":"Cho","sequence":"additional","affiliation":[{"name":"Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, NY 14853, USA"}]},{"given":"Joe","family":"Guinness","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, USA"}]},{"given":"Jan","family":"van Aardt","sequence":"additional","affiliation":[{"name":"Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"given":"Karl J.","family":"Czymmek","sequence":"additional","affiliation":[{"name":"Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, NY 14853, USA"},{"name":"PRO-DAIRY, Department of Animal Science, Cornell University, Ithaca, NY 14853, USA"}]},{"given":"Quirine M.","family":"Ketterings","sequence":"additional","affiliation":[{"name":"Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, NY 14853, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s13593-016-0349-y","article-title":"Factors of Yield Resilience under Changing Weather Evidenced by a 14-Year Record of Corn-Hay Yield in a 1000-Cow Dairy Farm","volume":"36","author":"Long","year":"2016","journal-title":"Agron. 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