Accurate,efficient,and timely yield estimation is critical for crop variety breeding and management opti-mization.However,the contributions of proximal sensing data characteristics(spectral,temporal,and spatial)to yield estimation have not been systematically evaluated.We collected long-term,hyper-temporal,and large-volume light detection and ranging(LiDAR)and multispectral data to(i)identify the best machine learning method and prediction stage for wheat yield estimation,(ii)characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation,and(iii)elucidate the contribution of time-series data fusion and 3D spatial informa-tion to yield estimation.Wheat yield could be accurately(R2=0.891)and timely(approximately-two months before harvest)estimated from fused LiDAR and multispectral data.The artificial neural network model and the flowering stage were always the best method and prediction stage,respectively.Spectral traits(such as Clgreen)dominated yield estimation,especially in the early stage,whereas the contribu-tion of structural traits(such as height)was more stable in the late stage.Fusing spectral and structural traits increased estimation accuracy at all growth stages.Better yield estimation was realized from traits derived from complete 3D points than from canopy surface points and from integrated multi-stage(espe-cially from jointing to heading and flowering stages)data than from single-stage data.We suggest that this study offers a novel perspective on deciphering the contributions of spectral,structural,and time-series information to wheat yield estimation and can guide accurate,efficient,and timely estimation of wheat yield.

Qing Li;Shichao Jin;Jingrong Zang;Xiao Wang;Zhuangzhuang Sun;Ziyu Li;Shan Xu;Qin Ma;Yanjun Su;Qinghua Guo;Dong Jiang

Plant Phenomics Research Centre,Academy for Advanced Interdisciplinary Studies,Regional Technique Innovation Center for Wheat Production,Ministry of Agriculture,Key Laboratory of Crop Physiology and Ecology in Southern China,Ministry of Agriculture,Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry,Jiangsu Key Laboratory for Information Agriculture,Nanjing Agricultural University,Nanjing 210095,Jiangsu,China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,International Institute for Earth System Sciences,Nanjing University,Nanjing 210023,Jiangsu,China;Department of Forestry,Mississippi State University,Mississippi State,MS 39759,USA;State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,Beijing 100093,China;Department of Ecology,College of Environmental Sciences,and Key Laboratory of Earth Surface Processes of the Ministry of Education,Peking University,Beijing 100871,China

作物学报（英文版）

[1]Qing Li;Shichao Jin;Jingrong Zang;Xiao Wang;Zhuangzhuang Sun;Ziyu Li;Shan Xu;Qin Ma;Yanjun Su;Qinghua Guo;Dong Jiang-.Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing)[J].作物学报（英文版）,2022(05):1334-1345

Clgreen,Fusing

Deciphering,contributions,structural,data,wheat,yield,estimation,from,proximal,sensing,Accurate,efficient,timely,critical,crop,variety,breeding,management,opti,mization,However,characteristics,temporal,spatial,have,not,been,systematically,evaluated,We,collected,long,term,hyper,large,volume,light,detection,ranging,LiDAR,multispectral,identify,best,machine,learning,method,prediction,characterize,multisource,fusion,dynamic,importance,traits,iii,elucidate,series,Wheat,could,accurately,approximately,months,before,harvest,estimated,fused,artificial,neural,network,model,flowering,were,always,respectively,Spectral,such,dominated,especially,early,whereas,height,was,more,stable,late,increased,accuracy,growth,stages,Better,realized,derived,complete,points,than,canopy,surface,integrated,jointing,heading,single,suggest,that,this,study,offers,novel,perspective,deciphering,information,guide

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