Automatic detection of Cyanobacterial blooms using multi-source optical satellite imagery: method development and application

Abstract

Cyanobacterial blooms (CyanoBloom) are a widespread environmental concern in lakes, and optical satellite imagery has been widely used to monitor their spatiotemporal dynamics. Many spectral-based CyanoBloom detection methods rely on Rayleigh- or atmosphere-corrected surface reflectance. However, these methods require ancillary atmospheric data and preprocessing procedures, thereby limiting their operational efficiency and scalability. The potential of multi-source satellite imagery remains underexploited due to the lake of robust methods for automatic CyanoBloom detection across various satellite platforms. To address this challenge, we proposed a novel automatic CyanoBloom detection (ACD) method that directly utilizes satellite top-of-atmosphere reflectance (RTOA) data. Cross-index and cross-sensor analyses confirmed the high detection accuracy and robustness of the ACD method against atmospheric and observational variations. The method was successfully applied to various inland lakes, including Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun in China, Lake Okeechobee in the United States, and Lough Neagh in Northern Ireland. Requiring only for four spectral bands (blue, green, red, and near-infrared), the ACD method was compatible with various optical sensors, including HY1C/D-CZI, GF-WFV, GF4-PMS, HJ-CCD, Sentinel2-MSI, Landsat9-OLI, and Terra-MODIS. This study presents a novel automatic and rapid approach for detecting CyanoBloom in lakes, providing valuable technical support for water quality monitoring and bloom management.

Keywords

• Cyanobacterial bloom
• Automatic detecting
• Satellite RTOA
• Optical satellite imagery