Quantification and Mapping of Water Clarity for Freshwater Lakes Using Sentinel-2 Data and Random Forest Regression Model: Application on Finger Lakes, New York

The growing effects of climate change and urbanization necessitates continuous monitoring of the freshwater resources in terms of water quality. Although remote sensing techniques have been successful in estimating water quality, its applicability over small oligotrophic lakes still remains a challenge due to the lower contribution of constituents to the water-leaving radiance. As such, this study leverages the availability of citizen science data and the synergistic use of Sentinel-2 imagery with Random Forest (RF) regression to estimate Secchi Disk Depth (SDD) over Canandaigua Lake. The results indicate an R
²
of 0.74, RMSE of about 0.72 m, MAE and Bias of 1.11 and 0.98, respectively. The feature importance for RF was also calculated, and the results indicate high value for visible bands. The model can be replicated for similar study areas and the findings can be used for efficient freshwater monitoring.