Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy

Zhang, Xia; Sun, Wendong; Yi, Chuixiang; Zhang, Lifu; Wang, Nan

doi:10.1016/j.scitotenv.2018.08.442

Cited by 72 publications

(40 citation statements)

References 41 publications

(56 reference statements)

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“…After that, a sieve with a pore size of 2 mm was used to screen the crushed soil. Next, the sieved sample was compacted and screened again with a sieve with a pore size of 0.15 mm [27]. Finally, nitric acid, hydrochloric acid, and perchloric acid were used to decompose the arsenic in various forms in the soil sample, which converted them into soluble arsenic ions in the solution and avoided the interference of sulfur and phosphorus on the measurement.…”

Section: Soil Sample Collection and Processingmentioning

confidence: 99%

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Wei

Wang

et al. 2020

Sensors

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With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

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Section: Soil Sample Collection and Processingmentioning

confidence: 99%

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Wei

Wang

et al. 2020

Sensors

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“…They were taken from the upper soil layer (0-20 cm) in 2018. After removal of the stones and plant roots, then they were sifted through a 200-mesh sieve and then ground into fine particles, approximate particle size after grinding ≤74 µm [32]. Each soil sample was then divided into two parts.…”

Section: Sample Preparation and Soil Chemical Analysismentioning

confidence: 99%

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

Wei

Zhang

et al. 2020

Applied Sciences

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Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.

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“…Moreover, the MTFA value of each band after the restoration was higher than that of the original tilting image, which showed that the quality of each band was better after restoration. To analyze the DN-value change of the restored tilting image, the index, the ratio of prediction to deviation (RPD) [ 30 ], which was commonly used in remote sensing inversion, was introduced in this study. The value can be expressed as follows:

where SD means the standard deviation, RMSEP is the root mean square error of prediction and RPD was usually used to evaluate the predicted value in quantitative remote sensing restored image and the original image.…”

Section: Restoration Of Tilting Hyperspectral Imagerymentioning

confidence: 99%

Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image

Zhang

et al. 2020

Sensors

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Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.

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Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy

Cited by 72 publications

References 41 publications

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image

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