Enhancement of Cyanobacterial Bloom Monitoring in Lake Taihu Using Dual Red-Edge Bands of GF-6/WFV: Multi-Dimensional Feature Combination and Extraction Accuracy Analysis

Abstract

Highlights: What are the main findings? The dual red-edge bands of GF-6/WFV significantly enhance cyanobacterial bloom identification accuracy in Lake Taihu, with red-edge indices contributing most to accuracy gains. The red-edge 710 nm band outperforms the 750 nm band in spectral separability and feature utility, while their combined use further improves model robustness and spatial characterization of bloom heterogeneity. What are the implications of the main findings? This study systematically developed and validated an extraction framework that comprehensively utilizes the multi-dimensional features of the GF-6/WFV red-edge bands, including spectral, textural, and index-based characteristics, thereby providing a practical and operational solution for cyanobacterial bloom monitoring using this satellite data. The findings clarify the contributions of different red-edge features in distinguishing cyanobacterial blooms from water, offering an experimental basis for extending the red-edge feature analysis framework to other inland waters or sensors equipped with red-edge bands. Cyanobacterial blooms pose a serious threat to freshwater ecosystems, necessitating accurate remote sensing monitoring. Although red-edge bands show potential in terrestrial monitoring, their multi-dimensional features (i.e., spectral, textural, and index-based characteristics) remain underutilized for aquatic blooms. This study leverages the dual red-edge bands (710 nm and 750 nm) of GF-6/WFV to enhance cyanobacterial bloom identification in Lake Taihu. Multi-temporal images from 2019–2023 were used to construct red-edge features in three dimensions: spectral (evaluated via adaptive band selection method) and Jeffries–Matusita–Bhattacharyya distance), texture (based on Gray Level Co-occurrence Matrix and principal component analysis), and indices (nine vegetation indices ranked by Random Forest importance). Twelve feature-combination schemes were designed and implemented with a Random Forest classifier. Results show that red-edge features consistently improve identification accuracy. Quantitatively, compared to the basic four-band (RGBN) combination, the 710 nm band improved spectral separability by an average of 9.63%, whereas the 750 nm band yielded a lower average improvement of 5.69%. Red-edge indices, especially the modified chlorophyll absorption reflectance index 1 (MCARI1) and normalized difference red-edge index (NDRE), exhibited higher importance than non-red-edge indices. All schemes incorporating red-edge features achieved mean overall accuracies of 92.8–94.9% and Kappa coefficients of 0.86–0.94, surpassing the basic four-band scheme. Among these features, red-edge indices contributed most significantly to accuracy gains, increasing the overall accuracy by an average of 0.36–6.06% and the Kappa coefficient by up to 0.06. The enhancement effect of the red-edge 710 nm band features was superior to that of the 750 nm band. This study demonstrates that multi-dimensional red-edge features effectively enhance the identification accuracy of cyanobacterial blooms and provides a methodological reference for operational GF-6 applications in water quality monitoring.