Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Abstract

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

“…Many studies did assess the performance of existing AC algorithms in moderately turbid waters for different satellite sensors such as SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) [28], MODIS-Aqua [29], GOCI (Geostationary Ocean Color Imager) [30], L8-OLI [31][32][33][34][35][36], S2-MSI [36][37][38][39][40][41] and S3-OLCI [42,43]. None of these studies considered the case of highly turbid waters for S3-OLCI.…”

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

“…Although the GDPS algorithm and SeaDAS algorithm are both based on the atmospheric correction scheme developed by Gordon and Wang [30], their actual atmospheric correction processors are with different aerosol models, different near-infrared (NIR) waterleaving radiance corrections, and different vicarious calibration gains [18][19][20][31][32][33]37]; thus, as Section 3.2 indicated, the nLw_GDPS and nLw_SeaDAS show some deviations. When comparing with nLw(λ) data of four AERONET-OC sites in terms of clear and turbid water, nLw_GDPS shows better accuracy than nLw_SeaDAS.…”

“…The atmospheric correction for GOCI data has been mainly performed using two operational algorithms: the Korea Ocean Satellite Center standard atmospheric correction algorithm, which can be achieved in the GOCI Data Processing System (GDPS) (hereafter, GDPS algorithm) [6], and the NASA standard atmospheric correction algorithm, which can be realized in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package) (hereafter, SeaDAS algorithm) [17]. Huang et al [18] found that the GDPS algorithm shows better performance in retrieving Rrs and aerosol optical information over the Yellow Sea region than the SeaDAS algorithm, although low accuracies were discovered at blue and near-infrared (NIR) bands, which is consistent with the research results of Concha et al [19,20]. Kim et al [19,21] evaluated the chlorophyll concentration derived from GOCI radiometric data acquired from the GDPS algorithm using 130 matchups between GOCI data and field data and concluded that the surface radiometric outcome needs to be improved primarily for clear waters and for the blue bands (412, 443, and 490 nm).…”

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

“…The algorithms mentioned above can improve the data quality in turbid waters, but the empirical relationships are highly reliant on in situ datasets; even the empirical relationship used in L2013 is derived from the nearest non-turbid AC algorithm [39]. These algorithms are, therefore, only regionally applicable [40]. For example, the B2010 method only works properly in low to moderately turbid waters [41,42], as the chlorophyll-a-based relationship used in the bio-optical model might not be appropriate to extrapolate the ρ w (NIR) in waters whose optical properties are dominated by non-algal particles [41,42].…”