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Inland water bodies in the Colombian Andes face growing threats from human activity and climate variability, intensifying concerns over regional water security (WS). This issue is critical in the Upper Cauca River Basin, where pollution, hydrological changes, and limited in situ data hinder effective water management. Challenges are amplified in low- and middle-income countries with restricted access to high-performance computing and field monitoring, especially in remote or insecure areas. This study addresses these gaps through a remote sensing-based framework for multitemporal water monitoring using freely available satellite imagery. The methodology integrates four automatic segmentation techniques; applies the C2RCC processor to estimate chlorophyll-a (Chl-a), total suspended matter (TSM) and coloured dissolved organic matter (CDOM); and extracts time series data on surface area, NDWI, and water quality. Data from Landsat-8 (2014–2021), Sentinel-2 (2020–2021), and PlanetScope (2016–2021) support consistent monitoring. The approach provides a cost-effective, scalable solution to support water quality assessment and decision-making in data-scarce, high-risk Andean regions.
Sánchez-Guevara et al. (Mon,) studied this question.
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