On the Synergistic Use of SAR and Optical Imagery to Monitor Cyanobacteria Scum in Inland Waters

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science. Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover. We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.