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 $\alpha$ 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 $\alpha$ concentration with MAE 7.97 mg/ $\text{m}^{3}$ 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

Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers

Climate change leads to more variable meteorological conditions. In many Northern Hemisphere temperate regions, cold seasons have become more variable and unpredictable, necessitating frequent river ice observations over long sections of rivers. Satellite SAR (Synthetic Aperture Radar)-based river ice detection models have been successfully applied and tested, but different hydrological, morphological and climatological conditions can affect their skill. In this study, we developed and tested Sentinel-1 SAR-based ice detection models in 525 km sections of the Nemunas and Neris Rivers. We analyzed three binary classification models based on VV, VH backscatter and logistic regression. The model sensitivity and specificity were used to determine the optimal threshold between ice and water classes. We used in situ observations and Sentinel-2 Sen2Cor ice mask to validate models in different ice conditions. In most cases, SAR-based ice detection models outperformed Sen2Cor classification because Sen2Cor misclassified pixels as ice in areas with translucent clouds, undetected by the scene classification algorithm, and misclassified pixels as water in cloud or river valley shadow. SAR models were less accurate in river sections where river flow and ice formation conditions were affected by large valley-dammed reservoirs. Sen2Cor and SAR models accurately detected border and consolidated ice but were less accurate in moving ice conditions. The skill of models depended on how dense the moving ice was. With a lowered classification threshold and increased model sensitivity, SAR models detected sparse frazil ice. In most cases, the VV polarization-based model was more accurate than the VH polarization-based model. The results of logistic and VV models were highly correlated, and the use of VV was more constructive due to its simpler algorithm