Identification of algal blooms in lakes in the Baltic states using Sentinel-2 data and artificial neural networks

Algal blooms are a common problem in inland waters, which raise growing awareness on monitoring lakes' conditions. The on site monitoring is expensive and requires large human resources efforts. This work proposes remote monitoring techniques using satellite images and machine learning algorithms to predict chlorophyll concentration in water bodies and identify algal blooms. The training and test dataset used in this study includes diverse range of lakes in Baltic countries. The lake spectral features obtained from Sentinel-2 satellite images are used as predictors for proposed deep neural network models. The prediction of chlorophyll concentration with MAE 7.97 mg/m 3 and bloom vs. non-bloom classification with 71.6 % accuracy was achieved. The use of Bèzier curves for smoothing the point-wise prediction is proposed for identification of algal bloom characteristics: the bloom start date, end date, and duration. The results showed lake type impact on the blooming time. The experimental data and code are released with paper.