Phytoplankton, as a major primary producer in the oceans, is of greatsignificance to the structure and function of marine ecosystems. However,excessive accumulation of phytoplankton biomass in the water columnresults in the formation of harmful algal blooms, also known as red tides.In recent years, the outbreaks of phytoplankton blooms have becomeincreasingly serious in the global coastal ocean due to human activitiesand natural factors, causing a series of negative impacts on the marine environment and human health. For a long time, the monitoring of algal blooms has been limited by traditional observation methods and existing remote sensing algorithms, which makes it difficult to analyze the changeprocess and driving factors of coastal algal blooms on a global scale.China's coastal zone area spans the tropical, subtropical and temperate zones, is rich in fishery resources, and has important ecological functions,but also faces the challenges of ocean warming and frequent algal blooms. And the intricate relationship between climate warming and the frequency of algal blooms in the coastal area of China is also an important knowledge gap. In addition, the competition and succession of dominant phytoplankton species may also have an important impact on the marine ecosystem and the marine food web. In this context, this study established an automated remote sensing identification algorithm for phytoplankton blooms based on thechlorophyll a fluorescence signal of phytoplankton blooms and CIE colorspace, and traced the spatial and temporal dynamics of phytoplankton blooms in the offshore of the globe over the past 20 years using a total of 760,000 views of day-scale MODIS satellite remote sensing data from2003 to 2020. Meanwhile, this study also reveals the potential impacts of natural factors such as sea surface temperature, ocean mesoscalecirculation, El Niño/Southern Oscillation (ENSO), wind speed andprecipitation, and human activities such as fertilizer use and fishery farming on the frequency of algal blooms. In addition, we constructed a classification algorithm for global phytoplankton bloom species, and conducted a preliminary analysis of the variation of the dominant algal species of global coastal zone blooms and their relationship with environmental changes.
 

Phytoplankton blooms; Remote sensing; Sea surface temperature; Ocean mesoscale circulation