Atmospheric Correction of GOCI Using Quasi-Synchronous VIIRS Data in Highly Turbid Coastal Waters

The Geostationary Ocean Color Imager (GOCI) sensor, with high temporal and spatial resolution (eight images per day at an interval of 1 hour, 500 m), is the world’s first geostationary ocean color satellite sensor. GOCI provides good data for ocean color remote sensing in the Western Pacific, among the most turbid waters in the world. However, GOCI has no shortwave infrared (SWIR) bands making atmospheric correction (AC) challenging in highly turbid coastal regions. In this paper, we have developed a new AC algorithm for GOCI in turbid coastal waters by using quasi-synchronous Visible Infrared Imaging Radiometer Suite (VIIRS) data. This new algorithm estimates and removes the aerosol scattering reflectance according to the contributing aerosol models and the aerosol optical thickness estimated by VIIRS’s near-infrared (NIR) and SWIR bands. Comparisons with other AC algorithms showed that the new algorithm provides a simple, effective, AC approach for GOCI to obtain reasonable results in highly turbid coastal waters.

[1]  K. Ruddick,et al.  Spectral relationships for atmospheric correction. II. Improving NASA's standard and MUMM near infra-red modeling schemes. , 2013, Optics express.

[2]  Menghua Wang,et al.  A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure , 2005 .

[3]  K. Ruddick,et al.  Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8 , 2015 .

[4]  Wei Shi,et al.  Sensor Noise Effects of the SWIR Bands on MODIS-Derived Ocean Color Products , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[5]  K. Ruddick,et al.  Spectral relationships for atmospheric correction. I. Validation of red and near infra-red marine reflectance relationships. , 2013, Optics express.

[6]  박영제,et al.  GOCI영상의 탁한 해역 대기보정: MUMM 알고리즘 개선 , 2013 .

[7]  Courtney Kearney,et al.  Impact of Aerosol Model Selection on Water-Leaving Radiance Retrievals from Satellite Ocean Color Imagery , 2012, Remote. Sens..

[8]  Su Jilan,et al.  Changjiang river plume and suspended sediment transport in Hangzhou Bay , 1989 .

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

[10]  Jie Zhang,et al.  Hourly changes in sea surface salinity in coastal waters recorded by Geostationary Ocean Color Imager , 2017 .

[11]  Menghua Wang.  Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function. , 2016, Optics express.

[12]  P Jeremy Werdell,et al.  Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. , 2010, Optics express.

[13]  J. Milliman,et al.  Suspended matter regime in the Yellow Sea , 1986 .

[14]  Tongji Li,et al.  Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea , 2019, Remote. Sens..

[15]  Chuqun Chen,et al.  Atmospheric Correction of Landsat-8/OLI Imagery in Turbid Estuarine Waters: A Case Study for the Pearl River Estuary , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Jaehun Park,et al.  Harmful algal bloom (HAB) in the East Sea identified by the Geostationary Ocean Color Imager (GOCI) , 2014 .

[17]  Delu Pan,et al.  Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing , 2016, Remote. Sens..

[18]  Seasonal variations of transport time of freshwater exchanges between Changjiang Estuary and its adjacent regions , 2015 .

[19]  Xiangming Zeng,et al.  Seasonal distribution of suspended sediment in the Bohai Sea, China , 2014 .

[20]  Jong-Kuk Choi,et al.  GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity , 2012 .

[21]  Wei Li,et al.  Distributions of suspended sediment concentration in the Yellow Sea and the East China Sea based on field surveys during the four seasons of 2011 , 2013 .

[22]  Chuqun Chen,et al.  A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary , 2014 .

[23]  Menghua Wang,et al.  VIIRS-derived chlorophyll-a using the ocean color index method , 2016 .

[24]  T. Pohlmann,et al.  Hydrography-Physical Description of the Bohai Sea , 2016 .

[25]  H. Yang,et al.  Remote sensing of ocean color: assessment of water-leaving radiance bidirectional effects on atmospheric diffuse transmittance. , 1997, Applied optics.

[26]  Xi Shao,et al.  Suomi NPP VIIRS sensor data record verification, validation, and long‐term performance monitoring , 2013 .

[27]  Menghua Wang,et al.  Estimation of ocean contribution at the MODIS near‐infrared wavelengths along the east coast of the U.S.: Two case studies , 2005 .

[28]  Ziauddin Ahmad,et al.  New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans. , 2010, Applied optics.

[29]  H. Gordon,et al.  Influence of oceanic whitecaps on atmospheric correction of ocean-color sensors. , 1994, Applied optics.

[30]  Gérard Thuillier,et al.  The Visible Solar Spectral Irradiance from 350 to 850 nm As Measured by the SOLSPEC Spectrometer During the ATLAS I Mission , 1998 .

[31]  Andrea Cavallaro,et al.  Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing , 2015, Remote. Sens..

[32]  Menghua Wang,et al.  Improved near-infrared ocean reflectance correction algorithm for satellite ocean color data processing. , 2014, Optics express.

[33]  F. Muller‐Karger,et al.  Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method , 2000 .

[34]  Menghua Wang.  A Sensitivity Study of the SeaWiFS Atmospheric Correction Algorithm: Effects of Spectral Band Variations , 1999 .

[35]  D. Doxaran,et al.  Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations , 2002 .

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

[37]  R. Greatbatch,et al.  An exploratory model study of sediment transport sources and deposits in the Bohai Sea, Yellow Sea, and East China Sea , 2013 .

[38]  J. Ryu,et al.  Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS) , 2012, Ocean Science Journal.

[39]  W. Guan,et al.  Sediment transport in the Yellow Sea and East China Sea , 2011 .

[40]  J. Ryu,et al.  Monitoring changes in suspended sediment concentration on the southwestern coast of Korea , 2014 .

[41]  J. Ryu,et al.  Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI) , 2012, Ocean Science Journal.

[42]  S. Bailey,et al.  Correction of Sun glint Contamination on the SeaWiFS Ocean and Atmosphere Products. , 2001, Applied optics.

[43]  S. Piketh,et al.  Retrieval of aerosol optical thickness and size distribution from the CIMEL Sun photometer over Inhaca Island, Mozambique , 2003 .

[44]  D. Tanré,et al.  Spatial and temporal variability of aerosol: size distribution and optical properties , 2003 .

[45]  Maycira Costa,et al.  Evaluation of MODIS-Aqua Atmospheric Correction and Chlorophyll Products of Western North American Coastal Waters Based on 13 Years of Data , 2017, Remote. Sens..

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

[47]  Wei Shi,et al.  Deriving Total Suspended Matter Concentration from the Near-Infrared-Based Inherent Optical Properties over Turbid Waters: A Case Study in Lake Taihu , 2018, Remote. Sens..

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

[49]  Cédric Jamet,et al.  Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters , 2013 .

[50]  Xiaoxiong Xiong,et al.  Early On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite , 2014, IEEE Transactions on Geoscience and Remote Sensing.