Monitoring water quality of surface drinking water sources on typical islands with remote sensing time series dataset

Surface drinking water is critical for island residents, significantly impacting drinking water safety. Utilizing remote sensing imagery to estimate water quality parameters holds profound theoretical significance for monitoring and preserving surface drinking water quality. In this investigation, the FUI water color index model was effectively harnessed via the Google Earth Engine (GEE) platform for the purpose of monitoring the temporal and spatial variations in the water color of Songtao Reservoir. A longitudinal analysis spanning from 2013 to 2023 has revealed that while the FUI values have largely adhered to a stable trend, a nuanced yet persistent annual decrement has been observed. This trend is hypothesized to stem from the aggregate effects of water quality stewardship measures implemented within the reservoir, the vicissitudes of climate, or the impress of other environmental factors. Spatially, the distribution of FUI values has been noted to be predominantly stable across the reservoir. Notably, regions with heightened FUI values and more pronounced volatility are chiefly situated in the northern quadrant. In contrast, the inlet channels and their immediate vicinities manifest reduced FUI values, a phenomenon potentially attributable to the influence of disparate water source inputs. The FUI index evinces a modest negative correlation with both precipitation and turbidity, and a nominal positive correlation with chlorophyll-a. Although the correlation analysis between the FUI index and these environmental parameters has provided significant perspectives, the strength of these correlations remains temperate, chiefly bounded by the constraints of a limited sample size. It is anticipated that forthcoming research will require the compilation of more comprehensive datasets to augment the robustness of statistical analyses and to further scrutinize the intricate relationship between the FUI index and a spectrum of environmental variables.