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.
Highlights
Ocean color remote sensing with daily–hourly sampling frequency and broad spatial coverage plays a critical role in the investigation of the bio-optical properties and the biogeochemical parameters of nearshore coastal waters
The criterion for Geostationary Ocean Color Imager (GOCI) cloud masking is different in GDPS than in SeaDAS [60]
The number of match-ups was too low, the match-up time-window was extended to ±3 h to ensure a suitable number of match-ups (Figures 3–8); (2) all methods overestimated Rrs and underestimated aerosol optical thickness (AOT) data in comparison with in situ values; (3) for visible bands, all algorithms performed well for Rrs and AOT at 555 nm, but larger uncertainties appeared at other wavelengths primarily because of incorrect estimation of the near infrared (NIR) ocean contributions [4]
Summary
Ocean color remote sensing with daily–hourly sampling frequency and broad spatial coverage plays a critical role in the investigation of the bio-optical properties and the biogeochemical parameters of nearshore coastal waters. While the past and the present primary observing platforms comprise polar orbiting satellites [i.e., the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectroradiometer (MERIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Ocean Land Colour Instrument (OLCI)], the world’s first Geostationary Ocean Color Imager (GOCI), launched by South Korea in 2010, represented a major breakthrough It was designed for oceanic applications over open and coastal waters, and it observes the same area with high-temporal frequency (an image every hour and up to eight per day) and spatial resolution of 500 m [3]
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