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Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery

查看全文 作　　者：Guomin [1,2,3]Shao;Wenting [1,2,3]Han;Huihui [4]Zhang;Yi [5]Wang;Liyuan [1,2]Zhang;Yaxiao [1,2]Niu;Yu [6,7]Zhang;Pei [1]Cao 高影响力作者 机构地区：[1]College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,Shaanxi,China;[2]Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture,Yangling 712100,Shaanxi,China;[3]Institute of Water-Saving Agriculture in Arid Areas of China,Northwest A&F University,Yangling 712100,Shaanxi,China;[4]Water Management and Systems Research Unit,USDA-ARS,2150 Centre Avenue,Bldg.D.,Fort Collins,CO 80526,USA;[5]College of Information,Xi’an University of Finance and Economics,Xi’an 710100,Shaanxi,China;[6]Institute of Soil and Water Conservation,Northwest A&F University,Yangling 712100,Shaanxi,China;[7]University of Chinese Academy of Sciences,Beijing 100049,China高影响力机构 出　　处：《The Crop Journal》索引2022年第10卷第5期,共10页高影响力期刊 基　　金：funded by the National Natural Science Foundation of China (51979233);the Natural Science Basic Research Plan in Shaanxi Province of China (2022JQ-363)。 摘　　要：Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R^(2)= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R^(2)= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale. 关 键 词：Crop transpiration Normalized difference red-edge index Unmanned aerial vehicles Random forest regression BIOMASS
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