Article(id=1305139346290528365, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2026.03.13, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744819200000, receivedDateStr=2025-04-17, revisedDate=1749484800000, revisedDateStr=2025-06-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1789099068471, onlineDateStr=2026-09-11, pubDate=1773504000000, pubDateStr=2026-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789099068471, onlineIssueDateStr=2026-09-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789099068471, creator=13701087609, updateTime=1789099068471, updator=13701087609, issue=Issue{id=1305139324555649224, tenantId=1146029695717560320, journalId=1303687221787852879, year='2026', volume='46', issue='3', pageStart='F2', pageEnd='424', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789099063289, creator='13701087609', updateTime=1789103147683, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305156455804465602, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305156455804465603, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=404, endPage=412, ext={EN=ArticleExt(id=1305139346496049262, articleId=1305139346290528365, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Monitoring Leaf Chlorophyll Content of Winter Wheat Based on Multispectral Imagery and Canopy Morphology Derived from Unmanned Aerial Vehicles, columnId=1305133133406433355, journalTitle=Journal of Triticeae Crops, columnName=Physiology, Ecology and Cultivation, runingTitle=null, highlight=null, articleAbstract=

In order to clarify the impact of coupling canopy morphology with other modal information on estimating winter wheat leaf chlorophyll content (LCC), two wheat varieties, Shinong 086 and Hemai 2020 were used as materials. Four nitrogen application levels (0, 120, 240, and 360 kg·hm^-2) were set up in fields. This study used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera to obtain spectral data of winter wheat at key growth stages. The canopy morphology effect was calculated in combination with the digital elevation model (DEM), and Random Forest Regression (RF), Categorical Boosting (Catboost), and eXtreme Gradient Boosting (XGBoost) were applied to analyze the contribution of spectral information, index information, and canopy morphology information, among other multi-modal data, to the monitoring of LCC in winter wheat. The results showed that compared with spectral and vegetation index features, canopy morphology could improve the monitoring accuracy of LCC (r²=0.66), and the importance of DEM was superior to that of slope. Among the three machine learning algorithms, the LCC model constructed by Catboost could achieve high monitoring accuracy both in the case of single feature and multi-source feature coupling, especially when a single canopy morphology feature was used as the input variable. Multi-source feature coupling does not improve the monitoring accuracy of LCC, but the coupling of other features with canopy morphology features can improve the monitoring accuracy to a certain extent.

, authors=Wenhui WANG, Chao TIAN, Shengquan LU, Yueying AN, Xiaoqi WANG, Songyang ZHAO, Shenghua YUAN, authorsList=Wenhui WANG, Chao TIAN, Shengquan LU, Yueying AN, Xiaoqi WANG, Songyang ZHAO, Shenghua YUAN, authorCompany=null, correspAuthors=Shenghua YUAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1305139346735124591, articleId=1305139346290528365, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=基于无人机多光谱影像和冠层形态的冬小麦叶绿素含量监测研究, columnId=1305133133590982733, journalTitle=麦类作物学报, columnName=生理、生态与栽培, runingTitle=null, highlight=null, articleAbstract=

为了明确冠层形态和其他模态信息的耦合对冬小麦叶绿素含量（leaf chlorophyll content, LCC）监测的影响，以小麦品种石农086和和麦2020为材料，通过设置4个施氮水平（0、120、240、360 kg·hm^-2）的冬小麦大田试验，应用低空无人机遥感平台搭载的多光谱影像机获取冬小麦关键生育时期光谱数据，结合数字高程模型（digital elevation model, DEM）计算冠层形态效应，并应用随机森林回归（random forest regression, RF）、分类提升决策树（categorical boosting, Catboost）和极端梯度提升决策树（eXtreme gradient boosting, XGBoost）3种机器学习算法分析光谱信息、植被指数信息和冠层形态相结合的多模态数据对冬小麦LCC监测的贡献。结果表明，相较于光谱和植被指数特征，冠层形态特征可以提高LCC的监测精度（r²=0.66），且DEM对模型估测的影响优于坡度。相较于RF和XGBoost，利用Catboost构建的LCC模型在单一特征、多源特征耦合、多生育时期的情况下均可获得较高的监测精度，尤其是以单一冠层形态特征作为输入变量的情景下。多源特征耦合并不能够显著提高LCC的监测精度，但冠层形态特征与其他特征耦合在一定程度上可以提高监测精度。

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