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UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases

文献类型：期刊论文

作者Zhu, Wanxue1,2,9; Rezaei, Ehsan Eyshi2; Nouri, Hamideh1,3; Sun, Zhigang4,5,6,8,9; Li, Jing6,9; Yu, Danyang7; Siebert, Stefan1
刊名FIELD CROPS RESEARCH
出版日期2022-08-01
卷号284页码:15
ISSN号0378-4290
关键词Unmanned aerial vehicle Remote sensing Maize Precision agriculture Multispectral
DOI10.1016/j.fcr.2022.108582
通讯作者Sun, Zhigang(sun.zhigang@igsnrr.ac.cn)
英文摘要Unmanned aerial vehicle (UAV) remote sensing and machine learning have emerged as a practical approach with ultra-high temporal and spatial resolutions to overcome the limitations of ground-based sampling for continuous crop monitoring. However, little is known on the suitability of distinct sensing indices for different crop management and distinct crop development phases. In this study, we assessed the potential of the UAV-based modeling to monitor field-scale crop growth under different water and nutrient supply considering distinct phenological phases of maize. UAV multispectral observations were deployed over two long-term experimental sites in three growing seasons. Calibration and validation of the random forest model took place at the Nutrient Balance Experimental Site (NBES) and the Water Nitrogen Crop Relation Site (WNCR), respectively. Leaf area index, leaf chlorophyll concentration, and aboveground dry matter were measured at the jointing, heading, and grain filling phases of maize in 2018-2020. Our results revealed that the suitability of sensing indicators differed at distinct maize phenological phases. Overall, red edge, red edge reflectance ratio, and chlorophyll index green are the most appropriate UAV indicators for estimating maize growth variables. The random forest model developed and calibrated at NBES with nutrient supply detected the signal of nitrogen x irrigation interactions at the other experimental site (WNCR) in different development phases and years very well, suggesting that random forest models developed by UAV images of same spatial and spectral attributes could be transferred across sites with the same cultivar while different irrigation and fertilizer management. We conclude that the selected number of UAV detected indicators processed with a random forest model could be used for robustly estimating environment x management (fertilizer and irrigation) interactions on maize growth variables.
WOS关键词LEAF-AREA INDEX ; ABOVEGROUND BIOMASS ; VEGETATION INDEXES ; WINTER-WHEAT ; MAIZE ; YIELD ; CHLOROPHYLL ; MODEL ; LAI ; SATELLITE
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDA23050102] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDA19040303] ; Key Projects of the Chinese Academy of Sciences, China[KJZD-SW-113] ; National Natural Science Foundation of China, China[31870421] ; Program of Yellow River Delta Scholars, China[2020-2024]
WOS研究方向Agriculture
语种英语
出版者ELSEVIER
WOS记录号WOS:000819230500002
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences, China ; Key Projects of the Chinese Academy of Sciences, China ; National Natural Science Foundation of China, China ; Program of Yellow River Delta Scholars, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/180662]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Zhigang
作者单位1.Univ Gottingen, Dept Crop Sci, Von Siebold Str 8, D-37075 Gottingen, Germany
2.Leibniz Ctr Agr Landscape Res ZALF, D-15374 Muncheberg, Germany
3.Dept Environm & Water, Strategy Sience & Corp Serv, Adelaide, SA 5000, Australia
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
5.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
7.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan 430072, Hubei, Peoples R China
8.Inst Geog Sci & Nat Resources Res, Datun Rd 11, Beijing 100101, Peoples R China
9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Wanxue,Rezaei, Ehsan Eyshi,Nouri, Hamideh,et al. UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases[J]. FIELD CROPS RESEARCH,2022,284:15.
APA Zhu, Wanxue.,Rezaei, Ehsan Eyshi.,Nouri, Hamideh.,Sun, Zhigang.,Li, Jing.,...&Siebert, Stefan.(2022).UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases.FIELD CROPS RESEARCH,284,15.
MLA Zhu, Wanxue,et al."UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases".FIELD CROPS RESEARCH 284(2022):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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