Machine Learning Methods for Water Quality Monitoring over Finger Lakes Using Sentinel-2

Monitoring freshwater quality is a global concern because of increasing harmful algal blooms (HABs). Therefore, it is important to detect HABs especially in small lakes as they hold great socioeconomic value. This study estimates the potential of using Sentinel-2 for estimating chlorophyll-a value in small inland lakes. In particular, this study uses support vector regression (SVR), random forest (RF) and adaptive boosting (AB) for Seneca lake. The processing power of Google Earth Engine (GEE) was used to extract the input features. The results indicate the superior performance of machine learning models in comparison to linear regression. Furthermore, AB provided the best results (R2=0.85) as compared to RF and SVR. In terms of ensemble, the combination of all three models performed best in terms of R2 (0.76), RMSE (0.633 µg/L) and MAE (0.728 µg/L).