Initial validation of GOCI water products against in situ data collected around Korean peninsula for 2010–2011

This paper provides initial validation results for GOCI-derived water products using match-ups between the satellite and ship-borne in situ data for the period of 2010–2011, with a focus on remote-sensing reflectance (Rrs). Match-up data were constructed through systematic quality control of both in situ and GOCI data, and a manual inspection of associated GOCI images to identify pixels contaminated by cloud, land and inter-slot radiometric discrepancy. Efforts were made to process and quality check the in situ Rrs data. This selection process yielded 32 optimal match-ups for the Rrs spectra, chlorophyll a concentration (Chl_a) and colored dissolved organic matter (CDOM), and with 20 match-ups for suspended particulate matter concentration (SPM). Most of the match-ups are located close to shore and thus the validation should be interpreted limiting to near-shore coastal waters. The Rrs match-ups showed the mean relative errors of 18–33% for the visible bands with the lowest 18–19% for the 490 nm and 555 nm bands and 33% for the 412 nm band. Correlation for the Rrs match-ups was high in the 490–865 nm bands (R2=0.72–0.84) and lower in the 412 nm band (R2=0.43) and 443 nm band (R2=0.66). The match-ups for Chl_a showed a low correlation (<0.41) although the mean absolute percentage error was 35% for the GOCI standard Chl_a. The CDOM match-ups showed an even worse comparison with R2<0.2. These match-up comparison for Chl_a and CDOM would imply the difficulty to estimate Chl_a and CDOM in near-shore waters where the variability in SPM would dominate the variability in Rrs. Clearly, the match-up statistics for SPM was better with R2=0.73 and 0.87 for two evaluated algorithms, although GOCI-derived SPM overestimated low concentration and underestimated high concentration. Based on this initial match-up analysis, we made several recommendations -1) to collect more offshore under-water measurements of the Rrs data, 2) to include quality flags in level-2 products, 3) to introduce an ISRD correction in the GOCI processing chain, 4) to investigate other types of in-water algorithms such as semianalytical ones, and 5) to investigate vicarious calibration for GOCI data and to maintain accurate and consistent calibration of field radiometric instruments.

[1]  Kevin Ruddick,et al.  Validation of MERIS water products for Belgian coastal waters: 2002-2003 , 2004 .

[2]  Kazuhiro Tanaka,et al.  Vicarious calibration of ADEOS-2 GLI visible to shortwave infrared bands using global datasets , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Menghua Wang,et al.  Seawifs Postlaunch Calibration and Validation Analyses , 2013 .

[4]  F. D’Ortenzio,et al.  Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS-A) at an offshore site in the Mediterranean Sea (BOUSSOLE project) , 2008 .

[5]  G. F. Humphrey,et al.  New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton , 1975 .

[6]  C. Mobley,et al.  Removal of surface-reflected light for the measurement of remote-sensing reflectance from an above-surface platform. , 2010, Optics express.

[7]  E. Fry,et al.  Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. , 1997, Applied optics.

[8]  B. Franz,et al.  Sources and assumptions for the vicarious calibration of ocean color satellite observations. , 2008, Applied optics.

[9]  Giuseppe Zibordi,et al.  Assessment of satellite ocean color products at a coastal site , 2007 .

[10]  C. McClain,et al.  The calibration and validation of SeaWiFS data , 2000 .

[11]  Collin S. Roesler Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique , 1998 .

[12]  Robert A Arnone,et al.  Uniqueness in remote sensing of the inherent optical properties of ocean water. , 2004, Applied optics.

[13]  S. Saitoh,et al.  Validation of ADEOS-II GLI ocean color products using in-situ observations , 2006 .

[14]  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.

[15]  L. Kou,et al.  Refractive indices of water and ice in the 0.65- to 2.5 micrometer spectral range , 1993 .

[16]  Sonia Gallegos,et al.  Development of Suspended Particulate Matter Algorithms for Ocean Color Remote Sensing , 2001 .

[17]  K. Ruddick,et al.  Model of remote-sensing reflectance including bidirectional effects for case 1 and case 2 waters. , 2005, Applied optics.

[18]  C. Mobley,et al.  Estimation of the remote-sensing reflectance from above-surface measurements. , 1999, Applied optics.

[19]  K. Baker,et al.  Optical properties of the clearest natural waters (200-800 nm). , 1981, Applied optics.

[20]  C. McClain,et al.  A Comprehensive Plan for the Long-Term Calibration and Validation of Oceanic Biogeochemical Satellite Data , 2007 .

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

[22]  B. Gentili,et al.  Diffuse reflectance of oceanic waters. III. Implication of bidirectionality for the remote-sensing problem. , 1996, Applied optics.

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

[24]  Davide D'Alimonte,et al.  Validation of satellite ocean color primary products at optically complex coastal sites: Northern Adriatic Sea, Northern Baltic Proper and Gulf of Finland , 2009 .

[25]  K. Ruddick,et al.  Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters , 2006 .

[26]  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 .

[27]  Sang-Woo Kim,et al.  Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas , 2011 .

[28]  S. Tassan Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. , 1994, Applied optics.

[29]  Hui Feng,et al.  AERONET-OC: A Network for the Validation of Ocean Color Primary Products , 2009 .

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

[31]  Tingwei Cui,et al.  Validation of MERIS ocean-color products in the Bohai Sea: A case study for turbid coastal waters , 2010 .

[32]  Motoaki Kishino,et al.  Estimation of the spectral absorption coefficients of phytoplankton in the sea , 1985 .