Continuous Sargassum monitoring across the Caribbean Sea and Central Atlantic using multi-sensor satellite observations

Abstract

Recurrent transnational Sargassum blooms across the Caribbean Sea and Atlantic Ocean have received growing attention. Different multispectral sensors, including Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imager Radiometer Suite (VIIRS), and Ocean and Land Color Instrument (OLCI), have been used to map their distributions. However, the synergistic use of multi-sensor observations for high temporal resolution Sargassum monitoring is lacking. Here, by combining MODIS (on Aqua and Terra), VIIRS (on JPSS1 and SNPP), and OLCI (on Sentinel-3A and -3B) observations, 3-day mean Sargassum distributions were mapped across the Caribbean Sea and Central Atlantic. The Sargassum biomass densities were derived using the sensor-specific Alternative Floating Algae Index (AFAI)-biomass model, and the consistency between the six sensors was examined using MODIS Aqua as the reference sensor. Comparison of the Sargassum biomass derived from different sensors shows that they have strong linear correlations (R2 ≥ 0.95), demonstrating high consistency and continuity between the six-sensor observations. On average, the combined six-sensor datasets provide ∼1.6 times more valid observations compared to the MODIS-only dataset in 2021, enabling the generation of the 0.5° 3-day mean products over ∼90% of the study region. Such 0.5° 3-day mean products detected ∼10-20% more biomass in the bloom peak month (June 2021) compared to the monthly mean counterpart. Increasing the spatial resolution to 0.1°, the 3-day mean products can continuously monitor Sargassum dynamics with eddies and tropical cyclones, which cannot be well captured by single sensors. This study highlights that combining multiple polar-orbiting satellite observations can achieve 3-day gap-free monitoring of floating macroalgae dynamics in the Caribbean Sea and tropical Atlantic, thus facilitating the analyses of the bloom response to different environmental conditions and the prediction of future bloom events.