Reflectance Spectroscopy (Vis-NIR) for Assessing Soil Heavy Metals Concentrations Determined by two Different Analytical Protocols, Based on ISO 11466 and ISO 14869-1

Angelopoulou, Theodora; Dimitrakos, Agathoklis; Terzopoulou, Evangelia; Zalidis, George C.; Theocharis, John B.; Stafilov, Trajče; Zouboulis, Anastasios

doi:10.1007/s11270-017-3609-9

Cited by 19 publications

(13 citation statements)

References 32 publications

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“…Firstly, soil samples from the studied area are analyzed with conventional laboratory measurements. Even these standardized procedures could differ in each study [32,119], as it was observed that laboratory measurements could also entail errors [130] that affect the predictive ability of the models, since they are dependent upon these specific laboratory reference measurements [131]. Laboratory soil spectroscopy has been evaluated for more than twenty years, and despite the development of soil spectral libraries on a global scale, there is still not an agreed upon protocol for performing such measurements.…”

Section: Discussionmentioning

confidence: 99%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

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Section: Discussionmentioning

confidence: 99%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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“…Another challenge is the lack in comparability between different studies since the model evaluation and accuracy is not measured with the same methods and studies are deprived of certain information that is necessary for the comparison between them. In addition, different protocols for soil sampling and measurements together with different instrumentation are factors that also hinder the reliability of the results, while predictions are affected by the reference method the soil properties were measured [28,108].…”

Section: Future Of Soil Spectroscopy In Soc Estimationmentioning

confidence: 99%

“…The visible region of the electromagnetic spectrum could also provide valuable information for SOC estimation, considering that soil appears darker with increasing SOC content [27]. Figure 1 shows the spectral signature of a sandy loam soil with 5.43% OC content [28] and the important wavelength regions for SOC estimation according to several studies [29][30][31]. .…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

et al. 2019

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Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.

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“…The average value for the Daye area is lower than the first-class standard (the limit for soil quality that protects the natural ecology of the area and maintains the natural background), and the Daye area belongs to the unpolluted area category. The difference between the mean, standard deviation, and coefficient of variation of the calibration sets and verification sets is small [30,31]. Therefore, this division can be considered as reasonable and can be used for subsequent modeling.…”

Section: Calibration Set and Validation Setmentioning

confidence: 99%

An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing

et al. 2019

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Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils.

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Reflectance Spectroscopy (Vis-NIR) for Assessing Soil Heavy Metals Concentrations Determined by two Different Analytical Protocols, Based on ISO 11466 and ISO 14869-1

Cited by 19 publications

References 32 publications

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing

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