Satellite-based assessment of mining-related sediment influence on water quality in an extractive river basin

Extractive activities drive land transformation in many mineralised river basins. However, linking these changes to observable and attributable water-quality outcomes remains methodologically challenging. This study applies an integrated monitoring framework to examine how multi-decadal Land-use and land-cover change (LULC) translates into spatially differentiated river water quality in the Ankobra River basin, Ghana. Using harmonised Landsat and Sentinel imagery, LULC dynamics was reconstructed for 1986, 2002, 2016, and 2025. Field-based measurements of key physico-chemical water-quality parameters were collected to support the analysis. Spatial interpolation using Ordinary Kriging and redundancy analysis was then applied to assess the extent to which land-use composition explains the observed variation in water quality. The results showed a shift from forest-dominated land cover towards agriculture, settlement, and mining-related disturbance during the study period. Bareland/Mining expanded from less than 1% of the basin in 1986 to approximately 3.7% by 2025 (>100 km²), while combined forest cover declined overall throughout the study period. Water-quality patterns exhibited strong spatial gradients, with turbidity ranging from approximately 114 to more than 1000 NTU and total suspended solids (TSS) from around 100 to nearly 3000 mg L^-1. Redundancy analysis indicated that land-use composition explained approximately 47.5% of the variance in water quality, with the mining-related land cover exerting the strongest influence (F=13.66, p<0.001) and showing robust positive associations with turbidity and TSS. Closed forest cover displayed a significant buffering effect, while agricultural land use did not show significant association on the spatial scale examined. These findings demonstrate how integrated Earth observation and field data can move sustainability assessment beyond descriptive convergence towards diagnostic clarity. The analytical framework offers a transparent and scalable approach for prioritising regulatory attention and monitoring in extractive landscapes where environmental pressures are spatially uneven and governance capacity is constrained.