{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:38:15Z","timestamp":1784043495196,"version":"3.55.0"},"reference-count":70,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23050102"],"award-info":[{"award-number":["XDA23050102"]}]},{"name":"Key Projects of the Chinese Academy of Sciences","award":["KJZD-SW-113"],"award-info":[{"award-number":["KJZD-SW-113"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31870421"],"award-info":[{"award-number":["31870421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6187030909"],"award-info":[{"award-number":["6187030909"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program of Yellow River Delta Scholars","award":["2020-2024"],"award-info":[{"award-number":["2020-2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t\u00b7ha\u22121). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.<\/jats:p>","DOI":"10.3390\/rs14071559","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"1559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"given":"Bin","family":"Yang","sequence":"first","affiliation":[{"name":"CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"},{"name":"Yusense Information Technology and Equipment (Qingdao) Inc., Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-0688","authenticated-orcid":false,"given":"Wanxue","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 M\u00fcncheberg, Germany"},{"name":"Department of Crop Sciences, University of G\u00f6ttingen, 37075 G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2603-8034","authenticated-orcid":false,"given":"Ehsan Eyshi","family":"Rezaei","sequence":"additional","affiliation":[{"name":"Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 M\u00fcncheberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Sun","sequence":"additional","affiliation":[{"name":"CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"},{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yusense Information Technology and Equipment (Qingdao) Inc., Qingdao 266000, China"},{"name":"Fine Mechanics and Physics, Changchun Institute of Optics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.fcr.2014.05.001","article-title":"Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images","volume":"164","author":"Wang","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. 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