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

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.

[1]  K. Stamnes,et al.  OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors , 2021 .

[2]  Domenico Cimini,et al.  Coastal Water Remote Sensing From Sentinel-2 Satellite Data Using Physical, Statistical, and Neural Network Retrieval Approach , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Chaofei Ma,et al.  Performance of COCTS in Global Ocean Color Remote Sensing , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[4]  P. Shanmugam,et al.  Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans , 2020 .

[5]  Yan Bai,et al.  Radiometric Sensitivity and Signal Detectability of Ocean Color Satellite Sensor Under High Solar Zenith Angles , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[6]  P. Shanmugam,et al.  Semi-analytical algorithms of ocean color remote sensing under high solar zenith angles. , 2019, Optics express.

[7]  Z. Qiu,et al.  Improving ocean color data coverage through machine learning , 2019, Remote Sensing of Environment.

[8]  V. Guinder,et al.  Validation of MODIS-Aqua bio-optical algorithms for phytoplankton absorption coefficient measurement in optically complex waters of El Rincón (Argentina) , 2019, Continental Shelf Research.

[9]  Yan Bai,et al.  Effects of Earth curvature on atmospheric correction for ocean color remote sensing , 2018 .

[10]  P Jeremy Werdell,et al.  Performance metrics for the assessment of satellite data products: an ocean color case study. , 2018, Optics express.

[11]  Delu Pan,et al.  Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm , 2017 .

[12]  K. Stamnes,et al.  Atmospheric correction over coastal waters using multilayer neural networks , 2017 .

[13]  Yan Bai,et al.  A Practical Method for On-Orbit Estimation of Polarization Response of Satellite Ocean Color Sensor , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  T. Cui,et al.  Remote sensing of absorption and scattering coefficient using neural network model: Development, validation, and application , 2014 .

[15]  T. Cui,et al.  A neural network model for remote sensing of diffuse attenuation coefficient in global oceanic and coastal waters: Exemplifying the applicability of the model to the coastal regions in Eastern China Seas , 2014 .

[16]  P. Shanmugam CAAS: an atmospheric correction algorithm for the remote sensing of complex waters , 2012 .

[17]  Bryan A. Franz,et al.  Corrections to the Calibration of MODIS Aqua Ocean Color Bands Derived From SeaWiFS Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Yan Bai,et al.  A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface , 2010 .

[19]  Luis Guanter,et al.  Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes , 2010 .

[20]  Menghua Wang,et al.  Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data , 2009 .

[21]  Menghua Wang,et al.  The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. , 2007, Optics express.

[22]  Guo Mao-hua Atmospheric Correction over Case 2 Waters Using Neutral Network , 2007 .

[23]  P. J. Werdell,et al.  A multi-sensor approach for the on-orbit validation of ocean color satellite data products , 2006 .

[24]  K. Ruddick,et al.  Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. , 2000, Applied optics.

[25]  Sylvie Thiria,et al.  Applying artificial neural network methodology to ocean color remote sensing , 1999 .

[26]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.