Comparison of multi-source satellite data for quantifying water quality parameters in a mining environment

Population growth and associated anthropogenic activities such as mining are posing a major threat to water resources. Remote sensing provides valuable information for assessing and monitoring water quality and quantity, and thus supports sustainable water management. Several studies have demonstrated the utility of various remote sensing techniques for quantifying water quality parameters, however, more needs to be done to exploit the growing number of satellite sensors available to the public. The main objective of this study was to evaluate the performances of individual and combined bands of Landsat 8 OLI, Sentinel-2 MSI, ASTER and SPOT 6 data as predictors of water quality parameters of open water bodies in a mining area. We applied the all-subsets regression approach to explore all possible candidate models from which selection was made based on Akaike's Information Criterion (AIC), coefficient of determination (adjR²) and Root-Mean-Squared-Error (RMSE). Two secondary objectives that make use of the products of the all-subsets regression approach were added in the study. This included comparison of a parsimonious model containing few predictors with the model that was ranked the best but containing more predictors; and comparison of models consisting of similar bands across sensors per water quality parameter. In general, all sensors yielded promising estimation accuracies, particularly for SAR, permanent hardness and cations (RMSE < 50% of observed means). Sentinel-2 bands produced the best estimations followed by Landsat 8 bands for most water quality parameters, chiefly due to the greater number of bands these sensors possess compared to ASTER and SPOT 6 sensors. All sensors provided alternative models that used fewer predicting bands than the best-ranked models (lowest AIC) and still showed comparable accuracies with the lowest AIC models for most variables (Pearson's correlation, r up to 0.9). Sentinel-2, in particular, had the most accurate, parsimonious models compared to the other sensors. Pairwise cross-sensor comparison of estimation models using similar bands revealed the best matches when a model consisted of blue band only (r > 0.95) for most parameters while the similarity decreased as the number of predicting bands increased. Furthermore, the best similarities were obtained between Sentinel-2 and Landsat 8 sensors compared to the other pairs, indicating the potential of replacing one in the absence of the other. The findings of this study showed the advantage of commonly used remotely-sensed data for estimating, and by extension monitoring, water quality parameters in open water bodies. In addition, the results revealed that few, specific bands carry information relevant to characterise most of the water quality parameters considered in the study. We recommend similar studies for different seasons, considering the need to monitor water quality throughout the year.