Advancing process-based sargassum forecasts with Earth Observation and Machine Learning

Since 2011, free drifting (pelagic) sargassum has proliferated across the tropical Atlantic, consequential for coastal communities in the eastern Caribbean.

Accumulation and decomposition on beaches threaten the vital tourism sector, yet sargassum may prove to be a 'golden tide', with commercial uses demonstrated in extraction of fuel, fertilizers, and pharmaceuticals.

To optimize coastal management and commercial activities, it is imperative to develop accurate monitoring and forecasting of sargassum influxes. Earth Observation (EO) technologies bring step changes in monitoring and process-level understanding of sargassum.

Forecasting open ocean drift of sargassum has extensively developed over the last decade, but two major challenges remain: (i) to incorporate near-shore processes into the offshore forecasts, to better predict the timing, location and quantities of major sargassum beaching; (ii) to better incorporate biological functionality into forecasts, accounting for differences between three dominant morphotypes of sargassum.

These challenges will be tackled with the aid of machine learning.