Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach
Toxic heavy metals in soil negatively impact soil’s physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliabil...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Soil Systems |
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| Online Access: | https://www.mdpi.com/2571-8789/6/1/30 |
| _version_ | 1797442118101237760 |
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| author | Arnab Saha Bhaskar Sen Gupta Sandhya Patidar Nadia Martínez-Villegas |
| author_facet | Arnab Saha Bhaskar Sen Gupta Sandhya Patidar Nadia Martínez-Villegas |
| author_sort | Arnab Saha |
| collection | DOAJ |
| description | Toxic heavy metals in soil negatively impact soil’s physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliability, and rapidity at a high temporal and spatial resolution, hyperspectral remote sensing within the Vis-NIR region is an indispensable and widely used approach in today’s world for monitoring broad regions and controlling soil arsenic (As) pollution in agricultural land. This study investigates the effectiveness of hyperspectral reflectance approaches in different regions for assessing soil As pollutants, as well as a basic review of space-borne earth observation hyperspectral sensors. Multivariate and various regression models were developed to avoid collinearity and improve prediction capabilities using spectral bands with the perfect correlation coefficients to access the soil As contamination in previous studies. This review highlights some of the most significant factors to consider when developing a remote sensing approach for soil As contamination in the future, as well as the potential limits of employing spectroscopy data. |
| first_indexed | 2024-03-09T12:37:11Z |
| format | Article |
| id | doaj.art-b68a45840c5345b884a6ad6ab71b3e33 |
| institution | Directory Open Access Journal |
| issn | 2571-8789 |
| language | English |
| last_indexed | 2024-03-09T12:37:11Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Soil Systems |
| spelling | doaj.art-b68a45840c5345b884a6ad6ab71b3e332023-11-30T22:23:30ZengMDPI AGSoil Systems2571-87892022-03-01613010.3390/soilsystems6010030Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance ApproachArnab Saha0Bhaskar Sen Gupta1Sandhya Patidar2Nadia Martínez-Villegas3Institute of Infrastructure and Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UKInstitute of Infrastructure and Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UKInstitute of Infrastructure and Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UKApplied Geosciences Department, IPICyT, San Luis Potosi 78216, MexicoToxic heavy metals in soil negatively impact soil’s physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliability, and rapidity at a high temporal and spatial resolution, hyperspectral remote sensing within the Vis-NIR region is an indispensable and widely used approach in today’s world for monitoring broad regions and controlling soil arsenic (As) pollution in agricultural land. This study investigates the effectiveness of hyperspectral reflectance approaches in different regions for assessing soil As pollutants, as well as a basic review of space-borne earth observation hyperspectral sensors. Multivariate and various regression models were developed to avoid collinearity and improve prediction capabilities using spectral bands with the perfect correlation coefficients to access the soil As contamination in previous studies. This review highlights some of the most significant factors to consider when developing a remote sensing approach for soil As contamination in the future, as well as the potential limits of employing spectroscopy data.https://www.mdpi.com/2571-8789/6/1/30hyperspectral remote sensingsoil As contaminationrice paddyspectral analysis |
| spellingShingle | Arnab Saha Bhaskar Sen Gupta Sandhya Patidar Nadia Martínez-Villegas Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach Soil Systems hyperspectral remote sensing soil As contamination rice paddy spectral analysis |
| title | Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach |
| title_full | Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach |
| title_fullStr | Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach |
| title_full_unstemmed | Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach |
| title_short | Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach |
| title_sort | identification of soil arsenic contamination in rice paddy field based on hyperspectral reflectance approach |
| topic | hyperspectral remote sensing soil As contamination rice paddy spectral analysis |
| url | https://www.mdpi.com/2571-8789/6/1/30 |
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