VIRS based detection in combination with machine learning for mapping soil pollution

Jia, Xiyue, O'Connor, David, Shi, Zhou and Hou, Deyi (2020) VIRS based detection in combination with machine learning for mapping soil pollution. Environmental Pollution, 268 (Part A). p. 115845. ISSN 0269-7491

Jia. 2020. VIRS based detection.pdf - Accepted Version

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Widespread soil contamination threatens living standards and weakens global efforts towards the Sustainable Development Goals (SDGs). Detailed soil mapping is needed to guide effective countermeasures and sustainable remediation operations. Here, we review visible and infrared reflectance spectroscopy (VIRS) based detection methods in combination with machine learning. To date, proximal, airborne and spaceborne carrier devices have been employed for soil contamination detection, allowing large areas to be covered at low cost and with minimal secondary environmental impact. In this way, soil contaminants can be monitored remotely, either directly or through correlation with soil components (e.g. Fe oxides, soil organic matter, clay minerals). Observed vegetation reflectance spectra has also been proven an effective indicator for mapping soil pollution. Calibration models based on machine learning are used to interpret spectral data and predict soil contamination levels. The algorithms used for this include partial least squares regression, neural networks, and random forest. The processes underlying each of these approaches are outlined in this review. Finally, current challenges and future research directions are explored and discussed

Item Type: Article
Keywords: Reflectance spectroscopy, Machine learning, Soil mapping, heavy metals, Soil pollution
Divisions: Real Estate and Land Management
Depositing User: Dr David O'Connor
Date Deposited: 30 Nov 2020 13:22
Last Modified: 13 Oct 2022 04:20

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