Auther: Dr. Anil Verma, Ms. Priya Joshi
Abstract: Soil contamination caused by industrial activities poses severe risks to environmental health and human safety. Traditional soil sampling methods are often labor-intensive, costly, and spatially limited. Hyperspectral remote sensing (HRS) has emerged as a powerful technique for detecting and mapping soil contaminants over large areas with high spectral resolution. This paper reviews the principles of hyperspectral remote sensing and its application in identifying soil contamination from heavy metals, hydrocarbons, and other pollutants linked to industrial sources. The integration of HRS data with advanced image processing and machine learning techniques enhances detection accuracy and spatial mapping. Two tables summarize major hyperspectral sensors used in soil contamination studies and compare various spectral indices and classification algorithms for contaminant detection. Challenges such as spectral variability, atmospheric interference, and data processing complexity are discussed.
Keywords: Hyperspectral remote sensing, Soil contamination, Industrial pollution, Spectral indices, Machine learning, Environmental monitoring
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