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Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach

dc.contributor.authorSaha, Arnab
dc.contributor.authorSenGupta, Bhaskar
dc.contributor.authorPatidar, Sandhya
dc.contributor.authorMartínez Villegas, Nadia Valentina
dc.date.accessioned2023-06-14T16:12:34Z
dc.date.available2023-06-14T16:12:34Z
dc.date.issued2022
dc.identifier.citationSaha, A.; Sen Gupta, B.; Patidar, S.; Martínez-Villegas, N. Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach. Soil Syst. 2022, 6, 30. https://doi.org/10.3390/soilsystems6010030
dc.identifier.urihttp://hdl.handle.net/11627/6364
dc.description.abstract"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."
dc.publisherMDPI
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHyperspectral remote sensing
dc.subjectSoil As contamination
dc.subjectRice paddy
dc.subjectSpectral analysis
dc.subject.classificationCIENCIAS DEL SUELO (EDAFOLOGÍA)
dc.titleIdentification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach
dc.typearticle
dc.identifier.doihttps://doi.org/10.3390/soilsystems6010030
dc.rights.accessAcceso Abierto


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional