{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:41:11Z","timestamp":1776922871359,"version":"3.51.2"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100009997","name":"Earmarked Fund for Modern Agro-industry Technology Research System","doi-asserted-by":"publisher","award":["2023CYJSTX02-23"],"award-info":[{"award-number":["2023CYJSTX02-23"]}],"id":[{"id":"10.13039\/501100009997","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013805","name":"Shanxi Agricultural University","doi-asserted-by":"publisher","award":["Z135050009017-3-4"],"award-info":[{"award-number":["Z135050009017-3-4"]}],"id":[{"id":"10.13039\/501100013805","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31871571"],"award-info":[{"award-number":["31871571"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.compag.2026.111731","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T07:09:22Z","timestamp":1775718562000},"page":"111731","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A GDD-constrained allometric framework for UAV-based estimation of winter wheat aboveground biomass"],"prefix":"10.1016","volume":"247","author":[{"given":"Xinglong","family":"Duan","sequence":"first","affiliation":[]},{"given":"Bokun","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yidan","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Yufei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qianyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhenwei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Chenbo","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lujie","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Meijun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoyan","family":"Song","sequence":"additional","affiliation":[]},{"given":"Xianjie","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Wude","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Meichen","family":"Feng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111731_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110110","article-title":"A comparison of proximal and remote optical sensor platforms for N status estimation in winter wheat","volume":"232","author":"Argento","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0010","doi-asserted-by":"crossref","first-page":"4300","DOI":"10.3390\/rs16224300","article-title":"Estimation of winter wheat stem biomass by a novel two-component and two-parameter stratified model using proximal remote sensing and phenological variables","volume":"16","author":"Chen","year":"2024","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.111731_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2025.127885","article-title":"Estimating wheat above-ground biomass by integrating dry matter allocation and phenology information","volume":"172","author":"Cheng","year":"2026","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.111731_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2024.127338","article-title":"Monitoring aboveground organs biomass of wheat and maize: a novel model combining ensemble learning and allometric theory","volume":"161","author":"Cheng","year":"2024","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.111731_b0025","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1016\/S2095-3119(19)62686-9","article-title":"Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution","volume":"18","author":"Duan","year":"2019","journal-title":"J. Integr. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0030","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2013.10.010","article-title":"Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements","volume":"100","author":"Fu","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2023.113860","article-title":"Insights from field phenotyping improve satellite remote sensing based in-season estimation of winter wheat growth and phenology","volume":"299","author":"Graf","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.111731_b0040","doi-asserted-by":"crossref","first-page":"3723","DOI":"10.3390\/rs14153723","article-title":"Monitoring key wheat growth variables by integrating phenology and UAV multispectral imagery data into random forest model","volume":"14","author":"Han","year":"2022","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.111731_b0045","doi-asserted-by":"crossref","first-page":"13943","DOI":"10.1038\/s41598-020-70951-w","article-title":"Comparing methods for estimating leaf area index by multi-angular remote sensing in winter wheat","volume":"10","author":"He","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111731_b0050","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3389\/fpls.2018.00237","article-title":"High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR","volume":"9","author":"Jimenez-Berni","year":"2018","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111731_b0055","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cj.2019.06.005","article-title":"Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index","volume":"8","author":"Jin","year":"2020","journal-title":"The Crop Journal"},{"key":"10.1016\/j.compag.2026.111731_b0060","doi-asserted-by":"crossref","unstructured":"KC, K., & Khanal, S. (2025). Scaling Biomass Estimation by Expanding Ground Truth with UAS-Derived Training Data. Remote Sensing, 17, 3163. Doi: 10.3390\/rs17123163.","DOI":"10.3390\/rs17183163"},{"key":"10.1016\/j.compag.2026.111731_b0065","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.3390\/s17122711","article-title":"Off-nadir hyperspectral sensing for estimation of vertical profile of leaf chlorophyll content within wheat canopies","volume":"17","author":"Kong","year":"2017","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111731_b0070","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.rse.2015.08.021","article-title":"Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes","volume":"169","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.111731_b0075","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.isprsjprs.2023.05.012","article-title":"RSARE: a physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background","volume":"200","author":"Li","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111731_b0080","article-title":"Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photography point cloud data","volume":"15","author":"Li","year":"2024","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111731_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2022.112967","article-title":"Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data","volume":"273","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.111731_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109076","article-title":"Estimation of wheat biomass based on phenological identification and spectral response","volume":"222","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0095","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13007-019-0402-3","article-title":"Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system","volume":"15","author":"Lu","year":"2019","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111731_b0100","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2026.111731_b0105","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.3390\/rs14133005","article-title":"Assessing the yield of wheat using satellite remote sensing-based machine learning algorithms and simulation modeling","volume":"14","author":"Meraj","year":"2022","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.111731_b0110","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.isprsjprs.2023.03.010","article-title":"Spectral saturation in the remote sensing of high-density vegetation traits: a systematic review of progress, challenges, and prospects","volume":"198","author":"Mutanga","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111731_b0115","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2018.09.028","article-title":"Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model","volume":"218","author":"Punalekar","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.111731_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2022.126548","article-title":"Improving water status prediction of winter wheat using multi-source data with machine learning","volume":"139","author":"Shi","year":"2022","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.111731_b0125","doi-asserted-by":"crossref","unstructured":"Subrahmaniyan, K., VEERAMANI, P., & ZHOU, W. (2021). Does heat accumulation alter crop phenology, fibre yield and fibre properties of sunnhemp (Crotalaria juncea L.) genotypes with changing seasons? Journal of Integrative Agriculture, 20, 2395-2409. Doi: 10.1016\/S2095-3119(21)63651-4.","DOI":"10.1016\/S2095-3119(20)63357-3"},{"key":"10.1016\/j.compag.2026.111731_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108438","article-title":"Potato late blight severity monitoring based on the relief-mRmR algorithm with dual-drone cooperation","volume":"215","author":"Sun","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0135","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.3390\/agronomy12071729","article-title":"Estimation of leaf area index and above-ground biomass of winter wheat based on optimal spectral index","volume":"12","author":"Tang","year":"2022","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111731_b0140","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.3390\/rs14051251","article-title":"Estimation of above-ground biomass of winter wheat based on consumer-grade multi-spectral UAV","volume":"14","author":"Wang","year":"2022","journal-title":"Remote Sens. (Basel)"},{"issue":"1","key":"10.1016\/j.compag.2026.111731_b0145","first-page":"187","article-title":"On the use of log-transformation vs. nonlinear regression for analyzing biological power laws","volume":"269","author":"Xiao","year":"2011","journal-title":"J. Theor. Biol."},{"key":"10.1016\/j.compag.2026.111731_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109851","article-title":"A new feature selection algorithm combining genetic algorithm, exponential decay function, and machine learning to realize hyperspectral estimation of winter wheat leaf area index","volume":"230","author":"Yang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110398","article-title":"A robust two-stage framework for maize above-ground biomass prediction integrating spectral remote sensing and allometric growth model","volume":"235","author":"Yang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0160","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.fcr.2013.12.007","article-title":"Using leaf dry matter to quantify the critical nitrogen dilution curve for winter wheat cultivated in eastern China","volume":"159","author":"Yao","year":"2014","journal-title":"Field Crop Res"},{"key":"10.1016\/j.compag.2026.111731_b0165","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111731_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107627","article-title":"Estimating vertically growing crop above-ground biomass based on UAV remote sensing","volume":"205","author":"Yue","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108250","article-title":"Winter wheat yield prediction using integrated Landsat 8 and Sentinel-2 vegetation index time-series data and machine learning algorithms","volume":"213","author":"Zhang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.111014","article-title":"Winter wheat yield estimation based on multisource remote sensing data: a dual-branch TCN-Transformer model and analysis of growth-stage feature transition mechanisms","volume":"239","author":"Zhang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0185","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014a worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106654","article-title":"Hyperspectral estimation of canopy chlorophyll of winter wheat by using the optimized vegetation indices","volume":"193","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111731_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2025.127638","article-title":"A UAV-based hybrid approach for improving aboveground dry biomass estimation of winter wheat","volume":"168","author":"Zhao","year":"2025","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.111731_b0200","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1016\/j.cj.2022.08.003","article-title":"Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?","volume":"10","author":"Zhao","year":"2022","journal-title":"The Crop Journal"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003261?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003261?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:54:04Z","timestamp":1776920044000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926003261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":40,"alternative-id":["S0168169926003261"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111731","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A GDD-constrained allometric framework for UAV-based estimation of winter wheat aboveground biomass","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111731","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111731"}}