<p>This paper presents a fresh approach aimed at defining and delimiting sampling areas in abandoned mining sites by combining satellite imagery with geospatial analysis. The proposed methodology involves the overlay of three Web Map Service (WMS) layers within a geographic information system (GIS) environment, which facilitates the spatial understanding of the study area. To validate this approach, factorial analysis techniques were applied, with principal component analysis (PCA) being the primary statistical tool used. The Pintor mine, located in mainland Portugal, was selected as the setting for a comprehensive case study due to its historical significance and the environmental problems associated with abandoned mining activities. The use of freely accessible satellite imagery in this context demonstrates the potential of remote sensing technologies to support environmental monitoring and management in post-mining landscapes. By combining these geospatial datasets with robust multivariate statistical methods, the study was able to accurately identify, define, and delineate the extent of contamination and areas of interest within the abandoned mining site. The PCA validation confirmed that the spatial patterns derived from the satellite image overlays were consistent with the geochemical data obtained from field sampling campaigns. This integrated methodology resulted in a substantial refinement of the initially proposed sampling area, reducing it by approximately 10 times. Such optimization not only decreases the logistical and financial burdens associated with extensive sampling but also enhances the precision of environmental assessments. The findings underscore the effectiveness of combining remote sensing with statistical analysis to improve the characterization of abandoned mining areas, thereby supporting better-informed decision-making for environmental remediation and land management.</p>