{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:38:12Z","timestamp":1784133492333,"version":"3.55.0"},"reference-count":118,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Projects of Hebei Province","award":["21327003D"],"award-info":[{"award-number":["21327003D"]}]},{"DOI":"10.13039\/501100012453","name":"China Agricultural Research System","doi-asserted-by":"publisher","award":["CARS301"],"award-info":[{"award-number":["CARS301"]}],"id":[{"id":"10.13039\/501100012453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. UAV-based multi-spectral data and manually measured AGB of winter wheat, under five nitrogen rates, were obtained from the jointing stage until 25 days after flowering in the growing season 2020\/2021. Forty-four multi-spectral VIs were used in the linear regression (LR), partial least squares regression (PLSR), and random forest (RF) models in this study. Results of LR models showed that the heading stage was the most suitable stage for AGB prediction, with R2 values varying from 0.48 to 0.93. Three PLSR models based on different datasets performed differently in estimating AGB in the training dataset (R2 = 0.74~0.92, RMSE = 0.95~2.87 t\/ha, MAE = 0.75~2.18 t\/ha, and RPD = 2.00~3.67) and validation dataset (R2 = 0.50~0.75, RMSE = 1.56~2.57 t\/ha, MAE = 1.44~2.05 t\/ha, RPD = 1.45~1.89). Compared with PLSR models, the performance of the RF models was more stable in the prediction of AGB in the training dataset (R2 = 0.95~0.97, RMSE = 0.58~1.08 t\/ha, MAE = 0.46~0.89 t\/ha, and RPD = 3.95~6.35) and validation dataset (R2 = 0.83~0.93, RMSE = 0.93~2.34 t\/ha, MAE = 0.72~2.01 t\/ha, RPD = 1.36~3.79). Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models.<\/jats:p>","DOI":"10.3390\/rs14051251","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"1251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6943-3950","authenticated-orcid":false,"given":"Falv","family":"Wang","sequence":"first","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4753-0263","authenticated-orcid":false,"given":"Longfei","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"},{"name":"College of Horticulture, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weilong","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Biological Sciences, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6881-2326","authenticated-orcid":false,"given":"Yinghua","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"},{"name":"Engineering Technology Research Center for Agriculture in Low Plain Areas, Cangzhou 061000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhencai","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"},{"name":"Engineering Technology Research Center for Agriculture in Low Plain Areas, Cangzhou 061000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhimin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"},{"name":"Engineering Technology Research Center for Agriculture in Low Plain Areas, Cangzhou 061000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-6783","authenticated-orcid":false,"given":"Kang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Technical University of Munich, 85354 Freising, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105736","DOI":"10.1016\/j.agwat.2019.105736","article-title":"Effects of micro-sprinkling with different irrigation amount on grain yield and water use efficiency of winter wheat in the North China Plain","volume":"224","author":"Li","year":"2019","journal-title":"Agric. 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