1203 scenes. To identify temporal changes in the boundaries of the tailings facilities, images covering the same spatial extent at different times were examined. Consequently, one cloud-free image per year acquired in June (or an adjacent month) was selected. Detailed information on the selected imagery and satellite characteristics is provided in Table 1. 4. EXPERIMENTAL STUDIES Applying a structured sequence of processing steps enables a reliable, precise, and comprehensive assessment of the investigated features. Drawing on a detailed understanding of remote sensing principles such as surface spectral behavior, water-related physical properties, and satellite image processing techniques, a dedicated workflow for analyzing tailings facility dynamics was developed and is illustrated in Figure 2. Satellite imagery was digitally processed and analyzed using the Sentinel Application Platform (SNAP), developed by the European Space Agency. The resulting outputs were subsequently imported into QGIS for spatial integration and map production. All images and software tools employed in this study are either open-source or freely accessible. Figure 2 – Workflow of the study In the context of remote sensing, a subset represents a selected, smaller portion extracted from a full satellite or aerial image. Because downloaded satellite scenes typically cover extensive areas and contain large volumes of data, subsetting is applied to limit the dataset to the specific study area. This approach concentrates the analysis on the defined area of interest and makes data handling, processing, and storage more efficient. In this study, subsetting was performed for all 51 images. After this step, the analysis focuses exclusively on the area of interest - The Benkovski tailings facilities, thereby optimizing computational resources and reducing overall processing time. Resampling in remote sensing refers to modifying the spatial resolution of raster data by changing the pixel size. This process transforms the original pixel grid into a new grid with a different spatial resolution (Figure 3). The primary objective of resampling is to standardize pixel dimensions, enabling direct comparison between images or spectral bands with varying resolutions. Since the selected image subsets have different spatial resolutions, harmonization is required prior to analysis. The images used have different spatial resolutions, which necessitates their harmonization. A common spatial resolution of 30 m was selected as the optimal compromise for harmonizing multi-sensor satellite data acquired between 1975 and 2025, as it corresponds to the native resolution of Landsat TM, ETM+, and OLI sensors and represents a widely accepted standard for long-term environmental monitoring. All datasets were resampled to this resolution to preserve temporal consistency and ensure reliable interannual comparison. Higher-resolution Sentinel-2 imagery (10–20 m) was downscaled to 30 m, thereby aggregating multiple measured pixels into a single representative value, reducing noise and mimicking the observation scale of historical sensors. Landsat MSS imagery, with a native instantaneous field of view of approximately 60 m and commonly distributed at 80 m pixel size, was upscaled to 30 m in order to ensure spatial alignment across the multi-sensor time series, without implying increased spatial detail. Importantly,
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