A satellite remote-sensing multi-index approach to discriminate pelagic Sargassum in the waters of the Yucatan Peninsula, Mexico

Recently, the need for quantitative information on the spatiotemporal distribution of floating macroalgae, particularly the two species of genus Sargassum, has grown because of blooms of these species in the Gulf of Mexico and Caribbean Sea. Remote sensing is one of the most frequently used tools to assess pelagic Sargassum distribution. The purpose of this study was to implement a methodological approach to detect floating algae in an efficient and replicable manner at a moderate cost. We analyzed Landsat 8 imagery, from which we calculated four vegetation indices and one floating-algae index to implement a supervised classification, together with the bands 2 and 5, using the Random Forest algorithm. The analysis was performed monthly from 2014 to 2015 for the northeastern Yucatan Peninsula, Mexico, with a total of 91 analyzed images. The quantitative performance metrics of the classifier (overall, Kappa and Tau) were greater than 80%, whereas bands 2 and 5 as well as the atmospherically resistant vegetation index made the greatest contributions to the classifications. During summer 2015, more than 4,000ha of Sargassum coverage per image were observed, which was substantially greater than that over the rest of the period. This approach constitutes a transferable alternative for the systematic detection of Sargassum, which enables a quantitative semi-automated time series comparison.