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

引用

The Crop Journal 2022年第5期10卷 1376-1385页

作者： Guomin Shao Wenting Han Huihui Zhang Yi Wang Liyuan Zhang Yaxiao Niu Yu Zhang Pei Cao College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of AgricultureYangling 712100ShaanxiChina Institute of Water-Saving Agriculture in Arid Areas of China Northwest A&F UniversityYangling 712100ShaanxiChina Water Management and Systems Research Unit USDA-ARS2150 Centre AvenueBldg.D.Fort CollinsCO 80526USA College of Information Xi’an University of Finance and EconomicsXi’an 710100ShaanxiChina Institute of Soil and Water Conservation Northwest A&F UniversityYangling 712100ShaanxiChina University of Chinese Academy of Sciences Beijing 100049China

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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