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

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

doi:10.3389/fpls.2021.609876

Cited by 25 publications

(18 citation statements)

References 56 publications

(66 reference statements)

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“…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.…”

Section: Measurement Of Spad At Ground Sampling Pointsmentioning

confidence: 99%

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

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

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Section: Measurement Of Spad At Ground Sampling Pointsmentioning

confidence: 99%

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

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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“…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).…”

Section: Performance Comparison Of Models For Agb Spad Lai Estimationmentioning

confidence: 64%

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

Guo

Zhao

Yan

2022

Environ Sci Pollut Res

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Coal-grain overlap areas (CGOA) with high groundwater levels are vulnerable to subsidence and water logging during a series of mining activities, which have adverse impacts on crop yields. Such damage requires full reports of disturbed boundaries for the agricultural reimbursement and ongoing reclamation. Since direct measurements are difficult in such a case because of vast, unreachable areas, so it is necessary to be able to identify out-of-production boundary (OB) and reduced-production boundary (RB) in the corresponding region. In this study, OB was extracted by setting thresholds through characteristics of the cultivated land elevation based on UAV-generated digital surface model (DSM) and digital orthophoto map (DOM).Meanwhile, aboveground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI), were used to select the appropriate vegetation indexes (VI) to perform a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR) and random forest (RF) algorithms. Finally, an improved OTSU segmentation algorithm was applied to extract mild RB and severe RB. The results show elevation threshold segmentation method and the improved OTSU segmentation method can accurately recognize and extract disturbed boundaries, which are consistent with the tonal difference after crop damage in the image. This study provides reference methods and theoretical supports for disturbed boundaries determination in CGOA with high groundwater levels for further agricultural compensation and reclamation processes.

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“…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)).…”

Section: Discussionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Antoniuk

Zhang

Andersen

et al. 2023

Sensors

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Agricultural droughts cause a great reduction in winter wheat productivity; therefore, timely and precise irrigation recommendations are needed to alleviate the impact. This study aims to assess drought stress in winter wheat with the use of an unmanned aerial system (UAS) with multispectral and thermal sensors. High-resolution Water Deficit Index (WDI) maps were derived to assess crop drought stress and evaluate winter wheat actual evapotranspiration rate (ETa). However, the estimation of WDI needs to be improved by using more appropriate vegetation indices as a proximate of the fraction of vegetation cover. The experiments involved six irrigation levels of winter wheat in the harvest years 2019 and 2020 at Luancheng, North China Plain on seasonal and diurnal timescales. Additionally, WDI derived from several vegetation indices (VIs) were compared: near-infrared-, red edge-, and RGB-based. The WDIs derived from different VIs were highly correlated with each other and had similar performances. The WDI had a consistently high correlation to stomatal conductance during the whole season (R2 between 0.63–0.99) and the correlation was the highest in the middle of the growing season. On the contrary, the correlation between WDI and leaf water potential increased as the season progressed with R2 up to 0.99. Additionally, WDI and ETa had a strong connection to soil water status with R2 up to 0.93 to the fraction of transpirable soil water and 0.94 to the soil water change at 2 m depth at the hourly rate. The results indicated that WDI derived from multispectral and thermal sensors was a reliable factor in assessing the water status of the crop for irrigation scheduling.

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

Cited by 25 publications

References 56 publications

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

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

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

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

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