Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery
Guomin Shao,
Wenting Han,
Huihui Zhang,
Yi Wang,
Liyuan Zhang,
Yaxiao Niu,
Yu Zhang,
Pei Cao
Affiliations
Guomin Shao
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China; Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, Shaanxi, China
Wenting Han
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China; Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, Shaanxi, China; Corresponding author.
Huihui Zhang
Water Management and Systems Research Unit, USDA-ARS, 2150 Centre Avenue, Bldg. D., Fort Collins, CO 80526, USA
Yi Wang
College of Information, Xi’an University of Finance and Economics, Xi’an 710100, Shaanxi, China
Liyuan Zhang
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
Yaxiao Niu
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
Yu Zhang
Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, Shaanxi, China; University of Chinese Academy of Sciences, Beijing 100049, China
Pei Cao
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
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 CTc and VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTc estimation model. The UAV VIs-derived CTc using the RFR estimation model yielded the highest accuracy (R2 = 0.91, RMSE = 0.0526, and nRMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTc model. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy (R2 = 0.76, RMSE = 282.8 g m−2, and nRMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTc performed 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.
