{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:47:59Z","timestamp":1773330479626,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key scientific and technological projects of Heilongjiang province","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Key scientific and technological projects of Heilongjiang province","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Key scientific and technological projects of Heilongjiang province","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Key scientific and technological projects of Heilongjiang province","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"National Natural Science Foundation of China","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"National Natural Science Foundation of China","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"National Natural Science Foundation of China","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"National Natural Science Foundation of China","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Above-ground biomass (AGB) is an important indicator for monitoring crop growth and plays a vital role in guiding agricultural management, so it must be determined rapidly and nondestructively. The present study investigated the extraction from UAV hyperspectral images of multiple variables, including canopy original spectra (COS), first-derivative spectra (FDS), vegetation indices (VIs), and crop height (CH) to estimate the potato AGB via the machine-learning methods of support vector machine (SVM), random forest (RF), and Gaussian process regression (GPR). High-density point clouds were combined with three-dimensional spatial information from ground control points by using structures from motion technology to generate a digital surface model (DSM) of the test field, following which CH was extracted based on the DSM. Feature bands in sensitive spectral regions of COS and FDS were automatically identified by using a Gaussian process regression-band analysis tool that analyzed the correlation of the COS and FDS with the AGB in each growth period. In addition, the 16 Vis were separately analyzed for correlation with the AGB of each growth period to identify highly correlated Vis and excluded highly autocorrelated variables. The three machine-learning methods were used to estimate the potato AGB at each growth period and their results were compared separately based on the COS, FDS, VIs, and combinations thereof with CH. The results showed that (i) the correlations of COS, FDS, and VIs with AGB all gradually improved when going from the tuber-formation stage to the tuber-growth stage and thereafter deteriorated. The VIs were most strongly correlated with the AGB, followed by FDS, and then by COS. (ii) The CH extracted from the DSM was consistent with the measured CH. (iii) For each growth stage, the accuracy of the AGB estimates produced by a given machine-learning method depended on the combination of model variables used (VIs, FDS, COS, and CH). (iv) For any given set of model variables, GPR produced the best AGB estimates in each growth period, followed by RF, and finally by SVM. (v) The most accurate AGB estimate was achieved in the tuber-growth stage and was produced by combining spectral information and CH and applying the GPR method. The results of this study thus reveal that UAV hyperspectral images can be used to extract CH and crop-canopy spectral information, which can be used with GPR to accurately estimate potato AGB and thereby accurately monitor crop growth.<\/jats:p>","DOI":"10.3390\/rs14215449","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T09:01:42Z","timestamp":1667120502000},"page":"5449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression"],"prefix":"10.3390","volume":"14","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-6200","authenticated-orcid":false,"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-5313","authenticated-orcid":false,"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Yiguang","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-214X","authenticated-orcid":false,"given":"Xiuliang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences\/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"}]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0294-5705","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Huiling","family":"Long","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.13031\/2013.29493","article-title":"Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop","volume":"53","author":"Swain","year":"2010","journal-title":"Trans. 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