![Sample our Environment & Agriculture journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5934/TOC-Subject-Sample-2021-Environment-Agriculture.jpg"})
ABSTRACT
Frequent and extensive sun glint is a serious obstacle to real-time monitoring of ocean colour anomalies. We semi-analytically adjust an inherent optical properties (IOPs) data processing system for the open ocean to correct for sun glint (called ‘IDAS-SGC’) and for remote sensing reflectance (Rrs) and IOPs retrievals. Tests with synthetic data validated the effectiveness of our algorithm in deriving ocean colour data from severely glint-contaminated images to produce high quality images. Evaluating results from single mission images suggested that our approach provides spatially smooth and consistent ocean colour products from both severe glint and glint-free regions for Visible Infrared Imaging Radiometer Suite and for Medium Resolution Spectral Imager II instruments. Specifically, complete coverage of circulation-caused ocean colour anomalies can be recovered from a single severe sun glint image. Comparing multi-mission images found that the inter-mission consistency for IDAS-SGC Rrs in sun glint regions is comparable with the inter-mission consistency in glint-free regions. Furthermore, we evaluate the performance of the Cox-Munk algorithm for sun glint estimation, and we find that our IDAS-SGC algorithm is more effective than the Cox-Munk algorithm in deriving Rrs products from severe sun glint regions due to the absence of accurate real-time wind data. Our results suggest that the IDAS-SGC algorithm obtains meaningful ocean colour products from sun glint-contaminated images of the open oceans.
Acknowledgements
Financial support for this study was provided by the National Key R&D Program of China2018YFB0504800 (2018YFB0504802; 2018YFB0504901), Shaanxi Key Research and Development Program (2018ZDXM-GY-023), Fundamental Research Funds for the Central Universities (60020302), and National Natural Science Foundation of China (41676171). We also thank NASA, NOAA, and the National Satellite Meteorological Centre of China for providing the ocean color and meteorological data.
Disclosure statement
The authors declare no conflict of interest.