Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing.

Baozhong Zhang,Taisheng Du,He Chen,Xin Han,Zheng Wei,Yinong Li

doi:10.3389/fpls.2021.609876

Abstract

In recent years, the unmanned aerial vehicle (UAV) remote sensing system has been rapidly developed and applied in accurate estimation of crop parameters and yield at farm scale. To develop the major contribution of UAV multispectral images in predicting winter wheat leaf area index (LAI), chlorophyll content (called soil and plant analyzer development [SPAD]), and yield under different water treatments (low water level, medium water level, and high water level), vegetation indices (VIs) originating from UAV multispectral images were used during key winter wheat growth stages. The estimation performances of the models (linear regression, quadratic polynomial regression, and exponential and multiple linear regression models) on the basis of VIs were compared to get the optimal prediction method of crop parameters and yield. Results showed that LAI and SPAD derived from VIs both had high correlations compared with measured data, with determination coefficients of 0.911 and 0.812 (multivariable regression [MLR] model, normalized difference VI [NDVI], soil adjusted VI [SAVI], enhanced VI [EVI], and difference VI [DVI]), 0.899 and 0.87 (quadratic polynomial regression, NDVI), and 0.749 and 0.829 (quadratic polynomial regression, NDVI) under low, medium, and high water levels, respectively. The LAI and SPAD derived from VIs had better potential in estimating winter wheat yield by using multivariable linear regressions, compared to the estimation yield based on VIs directly derived from UAV multispectral images alone by using linear regression, quadratic polynomial regression, and exponential models. When crop parameters (LAI and SPAD) in the flowering period were adopted to estimate yield by using multiple linear regressions, a high correlation of 0.807 was found, while the accuracy was over 87%. Importing LAI and SPAD obtained from UAV multispectral imagery based on VIs into the yield estimation model could significantly enhance the estimation performance. This study indicates that the multivariable linear regression could accurately estimate winter wheat LAI, SPAD, and yield under different water treatments, which has a certain reference value for the popularization and application of UAV remote sensing in precision agriculture.

Highlights

The estimation of crop parameters is helpful in improving the level of crop monitoring, which is key to accurate monitoring and estimation of crop growth in agricultural management (Huang et al, 2016; Li et al, 2016; Liu et al, 2017; Yebra et al, 2017; Sun et al, 2021)
Four vegetation indices (VIs) (NDVI, SAVI, EVI, and DVI) derived from unmanned aerial vehicle (UAV) multispectral imagery were used for the linear regression model, quadratic polynomial regression, exponential model, and multiple linear regression for winter wheat leaf area index (LAI) and SPAD under low water, medium water, and high water
It is shown that the best agreement of predicted winter wheat LAI and SPAD values was for the medium water level (120– 180 mm), followed by the low water level (0–60 mm) (LAI); the worst was the high water level (210–240 mm)

Summary

Introduction

The estimation of crop parameters (leaf area index [LAI] and chlorophyll content) is helpful in improving the level of crop monitoring, which is key to accurate monitoring and estimation of crop growth in agricultural management (Huang et al, 2016; Li et al, 2016; Liu et al, 2017; Yebra et al, 2017; Sun et al, 2021). There are two kinds of satellite remote sensing data for crop parameters, namely, optical image and synthetic aperture radar data (Cougo et al, 2015; Castillo et al, 2017; Du et al, 2017; Pham and Yoshino, 2017; Pandit et al, 2018; Li et al, 2019), providing different spatial resolutions, such as SPOT (20 m), MODIS (250 m), Sentinel 1A (10 m), and ALOS-2 PLASAR2 (6 m) (Niu et al, 2019) Several limitations such as deficient spatiotemporal resolution and cloud cover contamination restrain the application of satellite-based platforms. The operation cost of manned airborne platforms is relatively high, and ground-based spectral devices are laborious and suffer from inefficient operations (Zhang and Kovacs, 2012; Yang et al, 2017; Yao et al, 2017; Katja et al, 2018)

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