Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image

Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8-84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments.

Automated summary

This study developed a method to effectively extract algal blooms and aquatic vegetation from turbid water bodies using high spatial resolution Sentinel 2 images. By combining the extraction results from multiple indices and utilizing image time series information, the study successfully distinguished algal blooms and aquatic vegetation in five typical lakes in China and mapped their spatial distributions.