Abstract
A sudden change in global and regional environmental conditions has triggered the invasion of Sargassum algae in parts of the Caribbean coasts since 2014. To date, it has not been possible to revert the trend of seasonal Sargassum invasion, but some public institutions of subtropical countries are in the process of building monitoring systems based on satellite earth observation. Algorithms applied on high spatial resolution (but low revisiting frequency) data have been reported successful for Sargassum detection. GOES-16, MODIS (Aqua & Terra) and VIIRS imagery are acquired daily at the reception station of the National Laboratory for Earth Observation (LANOT), hosted in the Geography Institute, National Autonomous University of Mexico (UNAM). In this research, we implement a near real time Sargassum monitoring platform off the shore of Honduras, Belize and Mexico, based on the above-mentioned satellite imagery. The system design of this platform is first described, including the Big Data infrastructure for image acquisition and storage. Then, Sargassum potential presence is mapped from each of the three sensors, using Python-based processing tools and Sargassum detection algorithms. The coarse spatial resolution products obtained could complement higher spatial resolution studies by providing inputs for temporal modelling of propagation and onshore accumulation of Sargassum.
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