Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat

Atzberger, Clement; Guérif, Martine; Baret, Frédéric; Werner, Willy

doi:10.1016/j.compag.2010.05.006

Cited by 214 publications

(197 citation statements)

References 83 publications

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“…Our results (Figure 7) show that MLR is more sensitive to noise than PLSR, which is consistent with the findings of Zhao et al [91]. The results of Atzberger et al [7] indicate that the noise immunity of PCR, PLSR, and SMLR is ranked as PCR > PLSR > MLR, which is exactly the same ranking as obtained in the present work ( Figure 7). The present noise immunity results ( Figure 7) are important because repeated observations by remote-sensing techniques occur at different times; so techniques with poor noise immunity may lead to low accuracy because of data errors [26,78,79] (e.g., due to atmosphere, clouds, observation times, sensor noise).…”

Section: Analysis Of Noise Immunitysupporting

confidence: 93%

“…In the To evaluate how sensor noise and other uncertainty sources affect these data-analysis techniques, we simulate the internal noise (dark current, random noise) of a CCD used for remote sensing. We add random noise to the validation VIs to analyze how remote-sensing noise affects the stability of these techniques with the help of white Gaussian noise [7]. These models to estimate the AGB use the original VIs and are validated by using VIs with noise added.…”

Section: Modeling Parameters and Sampling Methodsmentioning

confidence: 99%

“…PCA often reduces the dimensionality of data sets. This method can reduce the dimensionality of hyperspectral data, thus avoiding the problem of collinear variables that can occur in PLSR and MLR regression [7].…”

Section: Conventional Regression Techniquesmentioning

confidence: 99%

“…Traditional AGB estimates are based on destructive measurements, which are not only time and labor consuming, but more importantly, are difficult to apply over large areas [6]. In recent years, Hyperspectral remote-sensing data acquired from the ground [7,8], unmanned aerial vehicles [9], airborne platforms [10][11][12], and satellite platforms [13] have been able to capture crop canopy spectra in narrow bands and thereby provide information on the biochemical composition of the canopy. Crop physiology research shows that spectral absorption by plant leaves is mainly due to the leaf pigments, especially chlorophyll content (Chl) [14].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has used many powerful empirical regression techniques that make full use of the narrow hyperspectral bands, VIs, and even different types of sensor data [21]. These techniques essentially fall into two categories: (i) machine-learning techniques such as artificial neural network (ANN) [22], decision tree regression (DT) [23], boosted binary regression tree (BBRT) [24], random forest regression (RF) [25], support vector machine regression (SVM) [26], and (ii) conventional regression techniques such as multivariable linear regression (MLR) [26,27], partial least squares regression (PLSR) [7,8,22], and principal component regression (PCR) [7]. Many studies have obtained promising results by using these techniques [8][9][10][11]26].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

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Section: Analysis Of Noise Immunitysupporting

confidence: 93%

Section: Modeling Parameters and Sampling Methodsmentioning

confidence: 99%

Section: Conventional Regression Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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Remote Detection and Monitoring of Plant Traits: Theory and Practice

Angel

Shiklomanov

2022

Annual Plant Reviews Online

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A means for tracking global plant biodiversity and understanding ecological processes is to identify general functioning principles and quantify underpinning common traits across species. Cumulative knowledge from applied spectroscopy in plant functioning has demonstrated the use of hyperspectral remote sensing in quantifying leaf and canopy biochemical and physiological traits. This article aims to provide an overview of the existing theory, research, and applications of remote sensing of plant functioning traits and wraps up future directions and challenges for ecologists and remote sensing users in assessing global biodiversity and ecological functions. It covers the physics, chemistry, and physiological ecology underlying the relationships between leaf and canopy optical properties with functional traits. Within the scope of the article, descriptions and commonly used physical and empirical trait retrieval approaches are included for both leaf and canopy scales. The last section considers the advancing of retrieving frameworks through emerging hyperspectral remote sensing technology and data‐driven analytics. The article concludes by calling for a greater infusion of biological and ecological principles into remote trait retrieval techniques to ultimately improve understanding of ecosystem function and dynamics across different spatial and temporal scenarios.

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Predicting maize leaf area index by partial least square combined with wavelet transform

et al. 2022

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The leaf area index (LAI) is crucial for assessing and monitoring maize (Zea mays L.) vegetation status and photosynthetic ability. Predicting maize LAI by hyperspectral remote sensing technology is significant for managing agricultural production. In this study, three N rates (0, 120, and 240 kg N ha −1 ) and four drought stress treatments (60-70%, 45-55%, 30-40%, and 15-25% of field capacity), were imposed to provide the different environments for maize. The canopy spectral reflectance and LAI of maize were measured at the V6 and V12 stages. In this study, the main objectives were to investigate the performance of a new statistical method for monitoring LAI from canopy spectral reflectance. We used the canopy spectral reflectance to estimate the LAI and compared several methods of spectral analysis, including vegetation indices, wavelet functions, and the combination of continuous wavelet transform (CWT)-uninformative variable elimination (UVE)-partial least squares (PLS) (i.e., CWT-UVE-PLS), for spectral reflectance data analysis and model construction of maize LAI. Results showed that the model using the combination of db3-UVE-PLS achieved the best performance (with the highest coefficient of determination [R 2 ] and lowest RMSE for the calibration [R 2 = .979, RMSEC = .172] and validation [R 2 = .861, RMSEP = .533] datasets, respectively) in estimating the maize LAI. The CWT-UVE-PLS exhibited a considerable advantage in avoiding redundant or noise information interaction and achieving excellent correlations with maize LAI.

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Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat

Cited by 214 publications

References 83 publications

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Remote Detection and Monitoring of Plant Traits: Theory and Practice

Predicting maize leaf area index by partial least square combined with wavelet transform

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