Publications

Zhang, CX; Tao, BY; Li, YZ; Ai, LB; Zhu, YX; Liang, LS; Huang, HQ; Li, CP (2024). Evaluation of Rayleigh-Corrected Reflectance on Remote Detection of Algal Blooms in Optically Complex Coasts of East China Sea. REMOTE SENSING, 16(13), 2304.

Abstract
This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (R-rc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (R-rs), R-rc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from R-rc and R-rs showed good consistency (R-2 > 0.98). The effectiveness of five R-rc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of R-rc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to R-rs based bloom detection algorithms, synthetical R-rc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing R-rc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

DOI:
10.3390/rs16132304

ISSN:
2072-4292