Remote sensing to detect harmful algal blooms in inland waterbodies.

Harmful algal blooms (HABs) are an issue of concern for water management worldwide. As such, effective monitoring strategies of HAB spatio-temporal variability in waterbodies are needed. Remote sensing has become an increasingly important tool for HAB detection and monitoring in large lakes. However, accurate HAB detection in small-medium waterbodies via satellite data remains a challenge. Current barriers include the waterbody size, the limited freely available high resolution satellite data, and the lack of field calibration data. To test the applicability of remote sensing for detecting HABs in small-medium waterbodies, three satellites (Planetscope, Sentinel-2 and Landsat-8) were used to understand how spatial resolution, the availability of spectral bands, and the waterbody size itself effect HAB detection skill. Different algorithms and a non-parametric method, Self-Organizing Map (SOM), were tested. Curvature Around Red and NIR minus Red had the best HAB detection skill of the 20 existing algorithms that were tested. Landsat 8 and Sentinel 2 were the best satellites for HAB detection in small to medium waterbodies. The most critical attribute for detecting HABs were the available satellite bands, which determine the detection algorithms that can be used. Importantly, algorithm performance was mostly unrelated to waterbody size. However, there remain some barriers in utilizing satellite data for HAB detection, including algae dynamics, macrophyte cover within the waterbody, weather effects, and the correction models for satellite data. Moreover, it is important to consider the match time between satellite overpass and sampling activities for calibration. Given these challenges, integrating regular sampling activities and remote sensing is recommended for monitoring and managing small-medium waterbodies.