The Suomi National Polar-orbiting Partnership (SNPP) was successfully launched on October 28, 2011. The Visible
Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP, which has 22 spectral bands (from visible to
infrared) similar to the NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), is a multi-disciplinary
sensor providing observations for the Earth’s atmosphere, land, and ocean properties. In this paper, we provide some
evaluations and assessments of VIIRS ocean color data products, or ocean color Environmental Data Records (EDR),
including normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a (Chl-a)
concentration, and water diffuse attenuation coefficient at the wavelength of 490 nm Kd(490). Specifically, VIIRS ocean
color products derived from the NOAA Multi-Sensor Level-1 to Level-2 (NOAA-MSL12) ocean color data processing
system are evaluated and compared with MODIS ocean color products and in situ measurements. MSL12 is now
NOAA’s official ocean color data processing system for VIIRS. In addition, VIIRS Sensor Data Records (SDR or Level-
1B data) have been evaluated. In particular, VIIRS SDR and ocean color EDR have been compared with a series of in
situ data from the Marine Optical Buoy (MOBY) in the waters off Hawaii. A notable discrepancy of global deep water
Chl-a derived from MODIS and VIIRS between 2012 and 2013 is observed. This discrepancy is attributed to the SDR
(or Level-1B data) calibration issue and particularly related to VIIRS green band at 551 nm. To resolve this calibration
issue, we have worked on our own sensor calibration by combining the lunar calibration effect into the current
calibration method. The ocean color products derived from our new calibrated SDR in the South Pacific Gyre show that
the Chl-a differences between 2012 and 2013 are significantly reduced. Although there are still some issues, our results
show that VIIRS is capable of providing high-quality global ocean color products in support of science research and
operational applications. The VIIRS evaluation and monitoring results can be found at the website:
http://www.star.nesdis.noaa.gov/sod/mecb/color/index.html.
The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological Satellite (COMS), which was launched in June of 2010 and has eight spectral bands from the blue to the near-infrared (NIR) wavelengths in 412–865 nm, can monitor and measure ocean phenomenon over a local area of the western Pacific region centered at 36°N and 130°E and covering ~2500 × 2500 km2. Hourly measurements during daytime (i.e., eight images per day from local 9:00 to 16:00) are a unique capability of GOCI to be used for the short- and long-term regional ocean environmental monitoring.
A recent study from a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korean Institute of Ocean Science and Technology (KIOST) showed that the GOCI ocean color products such as normalized water-leaving radiance spectra, nLw(λ), for GOCI coverage region derived using an iterative NIR-corrected atmospheric correction algorithm (Wang et al., Opt. Express, vol. 20, 741–753, 2012) were significantly improved compared with the original GOCI data products and have a comparable data quality as from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua in this region (Wang et al., Opt. Express, vol. 21, 3835–3849, 2013). It is also shown that the GOCI-derived ocean color data can be used to effectively monitor ocean phenomenon in the region such as tide-induced re-suspension of sediments, diurnal variations of ocean optical and biogeochemical properties, and horizontal advection of river discharge.
In this paper, we show some more recent results of GOCI-measured ocean diurnal variations in various coastal regions of the Bohai Sea, Yellow Sea, and East China Sea. With possibly eight-time measurements daily, GOCI provides a unique capability to monitor the ocean environments in near real-time, and GOCI data can be used to address the diurnal variability in the ecosystem of the GOCI coverage region. In addition, more in situ data measured around the Korean coastal regions are used to validate the GOCI ocean color data quality, including evaluation of ocean diurnal variations in the region. The GOCI results demonstrate that GOCI can effectively provide real-time monitoring of water optical, biological, and biogeochemical variability of the ocean ecosystem in the region.
During the winter in later 2009 and early 2010, the Bohai Sea experienced its worst sea ice event in four decades.
Sea ice optical properties are derived from MODIS-Aqua measurements using the SWIR atmospheric correction
algorithm. The radiance feature of the sea ice in the Bohai Sea shows a strong dependence on ice types. For months
of December, January, and February during the winter of 2009-2010, the average sea ice albedo in the Bohai Sea
reached about 9.3%, 13.4%, and 12.6%, respectively.
A regional sea ice detection algorithm has been developed for monitoring sea ice in the Bohai Sea. During the 2009-
2010 winter, the sea ice covered about 5427, 27414, and 21156 km2 for the three winter months, while average
values of sea ice coverage between 2002-2010 are about 2735, 11119, and 10287 km2, respectively. Anomalously
large sea ice event in the Bohai Sea during 2009-2010 winter is attributed to the dominance of a high air pressure
system in the northern China and widespread air temperature drops in January and early February of 2010.
Turbidity is one of the important factors that can be used for measuring water quality in the Chesapeake Bay. The
Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua derived diffuse attenuation coefficient at the
wavelength 490 nm (Kd(490)) can be used to relate the Chesapeake Bay water turbidity. In this presentation, we use the
recently developed shortwave infrared (SWIR)-based atmospheric correction algorithm for deriving MODIS-Aqua ocean
color products in the Chesapeake Bay. It has been demonstrated that the SWIR-based data processing produces better
quality ocean color products over the turbid coastal waters. We use the Kd(490) data derived from MODIS-Aqua with
SWIR-based algorithm to study the turbidity in the Chesapeake Bay. Spatial distribution and seasonal variations of
turbidity are analyzed. In addition, simulations from the Regional Ocean Modeling System (ROMS) coupled with a
sediment model have been carried out to investigate the mechanisms of sediment transport, deposition, and resuspension
processes in the Chesapeake Bay. Factors that contribute to the turbidity variations, such as wind, current, tide, and
sediment settling velocity are simulated in the model. The satellite observations combined with the model simulations
are used to study and understand the turbidity variation and its impact on the water quality in the Chesapeake Bay.
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