The Inversion of HY-1C-COCTS Ocean Color Remote Sensing Products from High-Latitude Seas

• Published in: Remote Sensing
• Main Authors: Hao Li, Xianqiang He, Jing Ding, Yan Bai, Difeng Wang, Fang Gong, Teng Li
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2022
• Subjects: ocean color; HY-1C-COCTS; atmospheric correction; polar zone; diurnal change; large solar zenith angle; Arctic; Arctic Ocean
• Online Access: https://doi.org/10.3390/rs14225722

China’s first operational ocean color satellite sensor, named the Chinese Ocean Color and Temperature Scanner (HY-1C-COCTS), was launched in September 2018 and began to provide operational data in June 2019. However, as a polar orbiting ocean color satellite sensor, HY-1C-COCTS would inevitably encounter regions impacted by large solar zenith angles when observing the high-latitude seas, especially during the winter. The current atmospheric correction algorithm used by ocean color satellite data processing software cannot effectively process observation data with solar zenith angles greater than 70°. This results in a serious lack of effective ocean color product data from high-latitude seas in winter. To solve this problem, this study developed an atmospheric correction algorithm based on a neural network model for use with HY-1C-COCTS data. The new algorithm used HY-1C-COCTS satellite data collected from latitudes greater than 50°N and between April 2020 and April 2021 to establish a direct relationship between the total radiance received by the satellite and the remote sensing reflectance products. The evaluation using the test dataset shows that the inversion accuracy of the new algorithm is relatively high under different solar zenith angles and different HY-1C-COCTS bands (the relative deviation is 3.37%, 7.05%, 5.10%, 5.29%, and 10.06% at 412 nm, 443 nm, 490 nm, 520 nm, and 565 nm, respectively; the relative deviation is 1.07% when the solar zenith angle is large (70~90°)). Cross comparison with MODIS Aqua satellite products shows that the inversion results are consistent. After verifying the accuracy and stability of the algorithm, we reconstructed the remote sensing reflectance dataset from the Arctic Ocean and surrounding high-latitude seas (latitude greater than 50°N) and successfully retrieved chlorophyll-a data and information on other marine ecological parameters.