Phytoplankton blooms in coastal areas from space and in situ data

Phytoplankton, microscopic algae floating in water, is the foundation of the marine food chain and plays a crucial role in Earth's carbon system by fixing CO2 and producing oxygen. Phytoplankton productivity is especially high in coastal areas, typically supplied with abundant nutrients near estuaries. However, phytoplankton blooms can have detrimental effects, as some can be toxic and potentially create hypoxic zones. Harmful algal blooms, often called "red tides," can persist from hours to months and are challenging to predict due to their dependence on food chain relationships and environmental conditions. Monitoring these blooms is important for local management, as they affect coastal recreational activities, fishing, aquaculture, and overall human and ecosystem health. To address these challenges, various projects were recently supported in Europe, one of them is the LandSeaLot project, aiming to integrate and enhance existing observation efforts in the land-sea interface area [1]. This initiative combines in-situ observations, satellite data, numerical modeling, and citizen science to better understand this complex transitional zone and to create a unified observation strategy for European coastal areas [1, 2]. On the smaller scale, there is a local French Phenomer project, launched in 2013 by Ifremer and partners, that engages citizens in reporting colored seawater events caused by phytoplankton blooms [3]. This citizen science initiative helps scientists detect and study bloom events that might otherwise go unnoticed, particularly in areas not covered by regular monitoring programs [3]. At the same time, in the frame of this project, the scientific community is planning to propose the analysis of harmful coastal bloom situations based on satellite data.Being part of the mentioned initiatives, we propose to discuss the latest technological advancements that have revolutionized our ability to study phytoplankton populations. In particular, recently we have developed an algorithm to detect various optical types of phytoplankton blooms using high-resolution Sentinel-2 satellite data provided by ESA. This innovation builds upon improvements in optical imagery algorithms, enabling the identification of specific bloom types based on the unique spectral signatures of different plankton species, attributed to their distinct pigment compositions, and is a part of CNES project TOSCA. We also review the advantages and limits of state-of-art atmospheric corrections and cloud and land masks algorithms, as well as retrieval of temperature and optical properties and ocean surface dynamical conditions from high resolution satellite data.The combination of large-scale projects like LandSeaLot, citizen science initiatives such as Phenomer, and advanced satellite-based detection methods provides a comprehensive approach to monitoring and understanding phytoplankton dynamics. These efforts are crucial for predicting harmful algal blooms and assessing the impacts of climate change on marine environments. This approach to studying the land-sea interface will contribute to more effective coastal management strategies and support the sustainable use of marine resources in the face of increasing environmental pressures. Citations:[1] https://landsealot.eu[2] https://dyneco.ifremer.fr/en/Who-are-we/Hydro-sedimentary-dynamics-DHYSED/DHYSED/Recherche/Projects/LANDSEALOT[3]  https://www.phenomer.org/