Lake Cyanobacterial Bloom Color Recognition and Spatiotemporal Monitoring with Google Earth Engine and the Forel-Ule Index

Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these have altered freshwater worldwide during recent decades. Although cyanobacterial are typically caused blue-green cyanobacteria, which derive their color from phycocyanin pigment, other pigmented been frequently observed in bodies. These pose serious threat to inland waters, endangering public health ecosystems. Therefore, comprehending mechanism variation is crucial for revealing outbreak implementing effective prevention control measures. This study developed human–machine interactive workflow extracting recognizing colors based on Forel-Ule index Sentinel-2 MultiSpectral Instrument data. Using this workflow, authors conducted spatiotemporal analysis statistical bloom four typical eutrophic lakes 2019 2022. The findings indicated declining trend across studied over years, among Hulun Lake experienced an annual increase emerged as lake with most severe such yellowing status varied lakes, Taihu Dianchi exhibiting relatively high proportion green-yellow yellow blooms, followed Chaohu Lake, whereas had lowest occurrence. was implemented Google Earth Engine provided automated, integrated, rapid monitoring solution long-term recognition blooms.