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Updated: Jul 18, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
Comprehensive study of algal blooms variation in Jiaozhou Bay based on google earth engine and deep learning
Bin Guan1, Shaowei Ning2, Xu Ding2
1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China.
Abstract:
The Jiaozhou Bay ecosystem, a crucial marine ecosystem in China, has been plagued by frequent harmful algal blooms as due to deteriorating water quality and eutrophication. This study analyzed the temporal and spatial changes of harmful algal blooms in Jiaozhou Bay from 2000 to 2022 using the Floating Algae Index (FAI) calculated from MODIS (2000-2022) and Sentinel-2 (2015-2022) satellite image datasets. The calculation results of the image datasets were compared. The frequency of planktonic algal outbreaks was low and constant until 2017, but has increased annually since then. Algae blooms are most common in the summer and primarily concentrated along the bay's coast, middle, and mouth, with obvious seasonal and spatial distribution characteristics. Several factors influencing algal outbreaks were identified, including sea surface temperature, wind speed, air pressure, dissolved oxygen, nitrogen and phosphorus ratios, chemical oxygen demand, and petroleum pollutants. Algal bloom outbreaks in Jiaozhou Bay are expected to remain high in 2023. The findings provide crucial information for water quality management and future algal outbreak prediction and prevention in Jiaozhou Bay.
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