Abstract:

In the remote sensing monitoring for cyanobacteria blooms, the bloom area is a critical indicator for assessing the severity of the bloom and is crucial for relevant authorities in selecting preventive measures and determining emergency response levels. Traditional methods using medium-to-low-resolution imagery has limited precision in estimating bloom area. To address this issue, the cyanobacteria bloom areas of Taihu Lake as the study area extracted from Sentinel-2 and Sentinel-3 data were compared. Furthermore, the relationship between the NDVI from Sentinel-3 imagery and the cyanobacteria bloom area proportion within mixed pixels were analyzed. Based on these analyses, a corrected model for estimating bloom area was established using the NDVI density segmentation method to refine the bloom area extracted from Sentinel-3 images. The statistical results of bloom areas derived from Sentinel-3 with correction, Sentinel-3 without correction, and Sentinel-2 were compared and analyzed. The findings demonstrate that the corrected model significantly improves the accuracy and reliability of bloom area estimation using Sentinel-3 imagery compared to traditional methods, thereby enhancing its practical application value in cyanobacteria bloom monitoring.

Key words: NDVI density segmentation, cyanobacteria bloom area, Sentinel-3 OLCI image, Taihu Lake