Estimation of soil copper content based on fractional-order derivative spectroscopy and spectral characteristic band selection

Cui, Shichao; Zhou, Kefa; Ding, Ronggui; Cheng, Yinyi; Jiang, Guoshun

doi:10.1016/j.saa.2022.121190

Cited by 22 publications

(8 citation statements)

References 62 publications

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“…After the introduction of FOD preprocessing, the prediction accuracy has been dramatically improved. The results in this study were similar to other soil-related content estimation models with FOD preprocessing [19,21]. Chen L et al confirmed that FOD could better dig out the correlation spectrum of heavy metals in soil than integer derivative, and improve the model estimation ability [36].…”

Section: Discussionsupporting

confidence: 84%

“…However, some researchers have shown that fractional order derivatives (FOD) have more potential than integer derivatives [17,18]. By exploring the potential of FOD for the prediction of soil copper content, Cui S et al proved that the FOD is better than the integer-order, as it can effectively eliminate noise and highlight the spectral sensitivity of copper [19]. Lao C et al used FOD with an interval of 0.05 and a range of 0-2 to pretreat soil spectra to predict soil salinity and soluble ion content.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative

Tang

et al. 2022

Electronics

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Soil total nitrogen (TN) is a vital nutrient element that affects the growth and rubber production of rubber trees. Especially in the coastal environment, soil nutrients will show significant differences. Using hyperspectral technology to detect soil nitrogen ion content in the offshore environment can provide technical support for nutrient management. Preprocessing hyperspectral data is a crucial step in accurate spectral model estimation. At the same time, it is considered that the traditional first-order and second-order derivatives are easily unbalanced between the signal-to-noise ratio, resulting in the loss of adequate information. Therefore, this work focuses on the feasibility of fractional order derivative (FOD) combined with partial least squares regression (PLSR) to estimate its TN content. By collecting soil samples from rubber plantations, the TN content of the soil samples was determined, and the spectral reflectance was measured. The FOD of the original spectrum was preprocessed with an interval of 0.2, and 11 spectral curves were obtained. Then, successive projections algorithm (SPA) was used to extract spectral features, and partial least squares regression (PLSR) models of soil TN content were established. The research results show that compared with the traditional integer derivative, FOD has a tremendous advantage in balancing spectral information and noise and can provide more abundant characteristic variables, which helps establish a more robust estimation model. In the range of orders 0–2, the model established by the 1.8-order is the best. Under that circumstance, the determination coefficients of validation (R2v) is 0.649, and the ratio of the performance to deviation (RPD) is 1.72. Combined with FOD, it is feasible and practical to establish an accurate and rapid estimation model of soil TN content, which can provide an important reference for large-scale detection of soil TN content in rubber plantations.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative

Tang

et al. 2022

Electronics

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“…The findings of our research are consistent with the previous research conclusions to a certain extent. Cui et al [ 53 ] investigated the potential of using the FOD for estimating the soil copper content and found that the model using the 0.8-order FOD spectra performed the best, and the R 2 and RPD of the validation set were 0.6416 and 1.63, respectively. Jin and Wang [ 54 ] created hyperspectral indices using FOD spectra to retrieve the leaf mass per area (LMA), and results showed that the 0.3-order FOD indices provided the highest accuracies to trace LMA and at the same time had the least sensitivity to random noise.…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms

Hasan

Jia

Wang

et al. 2023

Plants

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The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 μg∙cm−2). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40 μg∙cm−2), root mean square error (RMSE = 4.23 μg∙cm−2), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite.

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“…A portable spectroradiometer, the PSR-3500 manufactured by Spectral Evolution, USA, was used to measure the canopy spectra of 69 sample points (trees). The spectral reflectance data were obtained from May 19th to June 1st, 2020, during the flowering stage of jujube trees, between 11:00 and 17:00 Beijing time, under clear, windless, and cloudless conditions ( Cui et al., 2022 ). The spectral range covered 350-2500 nm with a 1 nm interval, resulting in 2150 wavebands.…”

Section: Methodsmentioning

confidence: 99%

“…FD and/or the second derivatives (SD) are commonly used spectral data preprocessing techniques ( Wang et al., 2018b ; Wang et al., 2021 ; Wang et al., 2022b ). They are widely employed to mitigate noise, baseline effects, overlap problems, enhance spectral features, capture subtle details of spectral curves, and improve the accuracy of land surface parameter extractions ( Li et al., 1993 ; Cui et al., 2022 ; Jin and Wang, 2022 ). However, to the best of our knowledge, there has been no research combining these two derivative processing techniques with LASSO and EN dimensionality reduction algorithms for predicting hyperspectral vegetation LCC.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Tuerxun,

Zheng,

Wang

et al. 2023

Front. Plant Sci.

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The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

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Estimation of soil copper content based on fractional-order derivative spectroscopy and spectral characteristic band selection

Cited by 22 publications

References 62 publications

Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative

Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative

Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

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