{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T21:26:46Z","timestamp":1783978006224,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFD0200301"],"award-info":[{"award-number":["2018YFD0200301"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601373"],"award-info":[{"award-number":["41601373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2020YFG0048"],"award-info":[{"award-number":["2020YFG0048"]}]},{"name":"the Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS201712"],"award-info":[{"award-number":["OFSLRSS201712"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["ZYGX2019J070"],"award-info":[{"award-number":["ZYGX2019J070"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths \u2018a\u2019 and \u2018b\u2019 (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil\u2013plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0\u2013565.6, RCRW739.5\u2013743.5, RCRW684.4\u2013687.1 and RCRW667.9\u2013672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.<\/jats:p>","DOI":"10.3390\/rs12183104","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T09:40:56Z","timestamp":1600767656000},"page":"3104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Gangqiang","family":"An","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5369-4638","authenticated-orcid":false,"given":"Minfeng","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Center for Information and Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Center for Information and Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5504-206X","authenticated-orcid":false,"given":"Chunhua","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Geography, Western University, London, ON N6A 5C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3573-718X","authenticated-orcid":false,"given":"Xiaodong","family":"Huang","sequence":"additional","affiliation":[{"name":"Applied Geosolutions, 15 Newmarket Road, Durham, NH 03824, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Shang","sequence":"additional","affiliation":[{"name":"Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiqi","family":"Kang","sequence":"additional","affiliation":[{"name":"Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. 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