Estimation of the Relative Chlorophyll Content in Spring Wheat Based on an Optimized Spectral Index

Kasim, Nijat; Sawut, Rukeya; Abliz, Abdugheni; Shi, Qingdong; Maihmuti, Balati; Yalkun, Ahunaji; Kahaer, Yasenjiang

doi:10.14358/pers.84.12.801

Cited by 14 publications

(14 citation statements)

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“…There are still image factors, such as mixed pixels, water content, and spectral resolution in remote-sensing data, that must be taken into consideration for soil organic matter analysis. Therefore, the theoretical underpinning of using existing remote-sensing data for SOM mapping involves extracting adequate information from soil spectral data and generating a soil spectral index [14]. Researchers have made some progress in related fields.…”

Section: Introductionmentioning

confidence: 99%

Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Tang

Nie

2022

Land

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Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.

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Section: Introductionmentioning

confidence: 99%

Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Tang

Nie

2022

Land

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“…Red edge position, sensitive band and vegetation index were effective means to retrieve crop chlorophyll content from the spectral curves ( Dou et al, 2018 ; Kasim et al, 2018 ; Wang et al, 2019 ). Previous studies had shown that the position and reflectance of red edge were highly correlated with chlorophyll content of plant leaves and could be used as an indicator of chlorophyll content ( Filella and Penuelas, 1994 ; Gitelson et al, 1996 ).…”

Section: Discussionmentioning

confidence: 99%

“…Inversion of chlorophyll content by hyperspectral remote sensing was of great significance for crop growth status monitoring, yield estimation and agricultural planning ( Liang et al, 2012 ; Flores-De-Santiago et al, 2013 ). Hyperspectral remote sensing had been used to monitor winter wheat chlorophyll content ( He et al, 2018 ; Kasim et al, 2018 ). However, the application had been limited to specific test conditions ( Serbin et al, 2012 ; Zhou et al, 2016 ) and there were few studies investigating hyperspectral remote sensing applications on winter wheat under elevated CO 2 conditions.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Estimation Models of Winter Wheat Chlorophyll Content Under Elevated CO2

Cai

Miao

et al. 2021

Front. Plant Sci.

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Chlorophyll content is an important indicator of winter wheat health status. It is valuable to investigate whether the relationship between spectral reflectance and the chlorophyll content differs under elevated CO2 condition. In this open-top chamber experiment, the CO2 treatments were categorized into ambient (aCO2; about 400 μmol⋅mol–1) or elevated (eCO2; ambient + 200 μmol⋅mol–1) levels. The correlation between the spectral reflectance and the chlorophyll content of the winter wheat were analyzed by constructing the estimation model based on red edge position, sensitive band and spectral index methods, respectively. The results showed that there was a close relationship between chlorophyll content and the canopy spectral curve characteristics of winter wheat. Chlorophyll content was better estimated based on sensitive spectral bands and difference vegetation index (DVI) under both aCO2 and eCO2 conditions, though the accuracy of the models varied under different CO2 conditions. The results suggested that the hyperspectral measurement can be effectively used to estimate the chlorophyll content under both aCO2 and eCO2 conditionsand could provide a useful tool for monitoring plants physiology and growth.

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“…HSI can describe the interaction between alfalfa physicochemical traits and the environment in more detail through its fine spectral superiority and spatial information, and shows a strong advantage in the extraction of alfalfa computational phenotypic traits ( El-Hendawy et al., 2019a ; Xiaofeng et al., 2020 ; Jin et al., 2021b ). The hyperspectral narrow band vegetation index can more comprehensively characterize the content of crop stress resistance components through band combination ( Post et al., 2007 ; Wu et al., 2008 ; Ullah et al., 2012 ; Thenkabail et al., 2013 ; El-Hendawy et al., 2019b ), which provides an effective means for the study of alfalfa stress resistance phenotypic traits, breeding screening and implementation of precision agriculture ( Hunt et al., 2013 ; Kasim et al., 2017 ). With the cumulative change of physical and chemical parameters, the growth, development and structural parameters of alfalfa were also affected and changed ( Hanin et al., 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data

Zhang,

Liu

et al. 2023

Front. Plant Sci.

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Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa.

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Estimation of the Relative Chlorophyll Content in Spring Wheat Based on an Optimized Spectral Index

Cited by 14 publications

References 0 publications

Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Hyperspectral Estimation Models of Winter Wheat Chlorophyll Content Under Elevated CO2

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data

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