Due to heavy industrialization pollution is inevitable phenomena around the world and most pollutants are accumulated by soil, air and water. Specially soil pollution directly influencing soil eco-diversity and crop production system. Is it possible to detect soil pollution using GEE machine learning technique?
Yes, it is possible to detect soil pollution using Google Earth Engine (GEE) machine learning techniques. GEE, a cloud-based platform, facilitates access to vast repositories of satellite imagery, ideal for monitoring soil pollution. Techniques like Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) can be deployed on GEE data to discern and categorize instances of soil pollution. Through training on labeled datasets, these algorithms can recognize deviations such as alterations in vegetation health, soil color, and other pollution indicators within the imagery.
Moreover, GEE offers integrated functionalities for data filtering, composite creation, and the application of bespoke algorithms for atmospheric corrections. These features empower the analysis of extensive datasets, enabling the identification of trends and patterns in soil pollution progression over time.
An illustrative instance of GEE's efficacy in soil pollution detection is showcased in the research by Kazemi Garajeh et al. (2024). Their study leverages GEE to monitor the spatio-temporal distribution of soil salinity, particularly focusing on identifying saline regions prone to salt storm occurrences. This research underscores GEE's potential in soil pollution surveillance, holding substantial implications for soil biodiversity preservation and agricultural productivity enhancement.
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