{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T04:18:29Z","timestamp":1784953109392,"version":"3.55.0"},"reference-count":73,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,11]],"date-time":"2019-04-11T00:00:00Z","timestamp":1554940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012164","name":"National High-tech Research and Development Program","doi-asserted-by":"publisher","award":["2013AA102401"],"award-info":[{"award-number":["2013AA102401"]}],"id":[{"id":"10.13039\/501100012164","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g\/m2, MAE = 236.49 g\/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv\/GDD, R2 = 0.86, RMSE = 178.37 g\/m2, MAE = 127.34 g\/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.<\/jats:p>","DOI":"10.3390\/rs11070890","type":"journal-article","created":{"date-parts":[[2019,4,12]],"date-time":"2019-04-12T12:55:04Z","timestamp":1555073704000},"page":"890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-8968","authenticated-orcid":false,"given":"Qi","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenghui","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renshan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan 430079, China"},{"name":"College of Life Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianting","family":"Wu","sequence":"additional","affiliation":[{"name":"Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan 430079, China"},{"name":"College of Life Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s12571-012-0168-1","article-title":"Crops that feed the world 7: Rice","volume":"4","author":"Seck","year":"2012","journal-title":"Food Security"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11449","DOI":"10.3390\/rs70911449","article-title":"Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass","volume":"7","author":"Tilly","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.rse.2017.10.011","article-title":"Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region","volume":"204","author":"Yang","year":"2018","journal-title":"Remote Sens. 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