研究目的
To assess the accuracy of bio-optical models and infer the best suitable model for Ocean Colour Monitoring (OCM-2) and for the upcoming OCM-3 sensors of Indian Space Research Organization in case 1 waters of the Bay of Bengal.
研究成果
The optimization technique performs best for MODIS-A and OCM-2 sensors with minimal bias and RMSE, while GIOP is better for VIIRS. It is recommended to use the optimization technique for estimating satellite-based backscattering coefficients in Indian waters, but further evaluation in other water types is needed.
研究不足
The study is limited to case 1 waters of the Bay of Bengal; performance in coastal waters and global oceans needs further evaluation. Cloud-free satellite data were only available for three stations, limiting the dataset size.
1：Experimental Design and Method Selection:
The study uses multi-approach techniques including quasi-analytical algorithm (QAA), QAA version 5 (QAAv5), Generalized Inherent Optical Properties (GIOP), and optimization of semi-analytical algorithms to retrieve backscattering coefficients from remote sensing reflectance (Rrs). The models are based on radiative transfer equations and bio-optical relationships.
2：Sample Selection and Data Sources:
In situ data were acquired during the INS Sagardhwani cruise in the Bay of Bengal, covering 18 stations with bio-optical measurements. Satellite datasets from MODIS-Aqua, VIIRS, and OCM-2 sensors were used, with atmospheric corrections applied using SeaDAS.
3：List of Experimental Equipment and Materials:
Instruments include WET Labs ECO-BB9 for measuring volume scattering, calibrated by the manufacturer. Software includes SeaDAS version
4：4 for data processing. Experimental Procedures and Operational Workflow:
In situ backscattering coefficients were measured and interpolated to satellite wavelengths. Satellite Rrs data were processed with atmospheric corrections, and models were applied to retrieve bb, which were compared with in situ data for accuracy assessment.
5：Data Analysis Methods:
Statistical analysis was performed using bias and root-mean-square error (RMSE) to evaluate model performance.
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