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

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.

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

“…Following the sample allocation concept, 20 of the 90 groups of samples were utilized as test samples, while the measured SPAD values of the remaining 70 groups were randomly chosen as modeling samples. To minimize sampling error, canopy leaves of comparable size, color, and shape were chosen for the sampling procedure (Han et al, 2021). The measurements were performed at the leaf tip, center, and base, and the mean value was used to represent the leaf 's SPAD characteristic parameter.…”

Disturbed boundaries extraction in coal–grain overlap areas with high groundwater levels using UAV-based visible and multispectral imagery

“…Random forest, partial least square method, BP neural network and support vector machine were applied to estimate the chlorophyll content of apple leaves, and random forest was regarded to be the best modeling choice (Feng et al 2018). Additionally, MR showed a promising result as an estimator for LAI, which is supported by LAI estimation of a natural forest study (Pu 2012) and one winter wheat growth study (Han et al 2021).…”

Spatiotemporal Winter Wheat Water Status Assessment Improvement Using a Water Deficit Index Derived from an Unmanned Aerial System in the North China Plain

“…WDI combines thermal and multispectral remotely sensed data, and the method relies on good delineation of crop coverage ( x -axis in Figure 2 ). LAI approximation using various VIs based on combinations of RGB and near-infrared spectral bands has shown good results [ 13 , 21 ]. However, it is known that near-infrared (e.g., NDVI) tends to saturate at high canopy densities [ 21 ], so it may be beneficial to make use of red-edge spectral bands (e.g., NDRE and NDVIi, Table 3 , Equations (4) and (5)).…”

“…Thermal imagery is highly sensitive to environmental conditions and prone to distortions during acquisition and processing [ 11 ], and further interpretation may become more challenging due to the inability to separate canopy and soil in a frame [ 12 ]. These effects can be alleviated complementarily by visible/near-infrared images in order to evaluate crop canopy coverage since UAS multispectral imagery has a high correlation to leaf area index [ 13 ].…”