A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing

Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.