Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

doi:10.1016/j.cj.2022.06.005

	Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing
	Li, Qing ; Jin, Shichao 1; Zang, Jingrong ; Wang, Xiao ; Sun, Zhuangzhuang ; Li, Ziyu ; Xu, Shan ; Ma, Qin 2; Su, Yanjun 3; Guo, Qinghua4,5 ; Jiang, Dong
	2022
发表期刊	CROP JOURNAL
ISSN	2095-5421
卷号	10 期号:5 页码:1334-1345
摘要	Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. 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 information to yield estimation. Wheat yield could be accurately (R-2 = 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 CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution 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 (especially 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.(C) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V.
关键词	LiDAR Multispectral Yield Phenotype Hyper-temporal
学科领域	Agronomy ; Plant Sciences
DOI	10.1016/j.cj.2022.06.005
收录类别	SCI
语种	英语
WOS关键词	PREDICTING GRAIN-YIELD ; PROTEIN-CONTENT ; RANDOM FOREST ; BIOMASS ; LEAF ; NITROGEN ; MODELS
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000895382300004
出版者	KEAI PUBLISHING LTD
文献子类	Article
出版地	BEIJING
EISSN	2214-5141
资助机构	Jiangsu Agricultural Science and Technology Independent Innovation Fund Project [CX(21) 3107] ; National Natural Science Foundation of China [32030076] ; High Level Personnel Project of Jiangsu Province [JSSCBS20210271] ; China Postdoctoral Science Foundation [2021 M691490] ; Jiangsu Planned Projects for Postdoctoral Research Funds [2021K520C] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA24020202] ; Jiangsu 333 Program
作者邮箱	jinshichao1993@gmail.com ; jiangd@njau.edu.cn
作品OA属性	gold
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/28496
专题	植被与环境变化国家重点实验室
作者单位	1.Nanjing Agr Univ, Plant Phen Res Ctr,Acad Adv Interdisciplinary Stu, Reg Tech Innovat Ctr Wheat Prod,Minist Agr,Collab, Key Lab Crop Physiol & Ecol Southern China,Jiangs, Nanjing 210095, Jiangsu, Peoples R China 2.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China 3.Mississippi State Univ, Dept Forestry, Mississippi State, MS 39759 USA 4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 5.Peking Univ, Dept Ecol, Coll Environm Sci, Beijing 100871, Peoples R China 6.Peking Univ, Minist Educ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Li, Qing,Jin, Shichao,Zang, Jingrong,et al. Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing[J]. CROP JOURNAL,2022,10(5):1334-1345.
APA	Li, Qing.,Jin, Shichao.,Zang, Jingrong.,Wang, Xiao.,Sun, Zhuangzhuang.,...&Jiang, Dong.(2022).Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing.CROP JOURNAL,10(5),1334-1345.
MLA	Li, Qing,et al."Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing".CROP JOURNAL 10.5(2022):1334-1345.

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