Automated Extraction of Long-Term Cyanobacteria Blooming Series from Landsat Imagery Using Deep Learning

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious threats to inland water ecology and environmental security. Despite advances, accurately capturing their long-term dynamics for effective governance remains challenging due to limited datasets and segmentation model accuracy. This study addresses these gaps by developing a CyanoHABs dataset and a new model, Multi-scale Spatial Attention Network (MSA-Net), to automatically extract cyanobacterial blooms from Landsat imagery using deep learning. With enhanced multi-scale feature extraction and a Hybrid Attention Mechanism in an encoder–decoder framework, MSA-Net outperforms other models, providing robust methods and data support for remote sensing-based CyanoHAB monitoring in complex environments. The proposed MSA-Net achieved an F1-score of 88.34% and a Precision of 88.74%, outperforming all baseline methods. Ablation experiments further verified the effectiveness of the proposed Spatial Feature Enhancement Mechanism (SFEM) and Hybrid Attention Mechanism (HAM) in enhancing segmentation performance. The results highlight MSA-Net’s effectiveness in accurately identifying CyanoHABs, particularly in complex, multi-scale environments. This study provides a transferable deep learning framework and long-term remote sensing dataset for CyanoHAB monitoring, offering valuable insights for the large-scale assessment and management of eutrophic inland waters worldwide.