Yu-Hwan Ahn

Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS)

GOCI, the world’s first geostationary ocean color satellite, provides images with a spatial resolution of 500 m at hourly intervals up to 8 times a day, allowing observations of short-term changes in the Northeast Asian region. The GOCI Data Processing System (GDPS), a specialized data processing software for GOCI, was developed for real-time generation of various products. This paper describes GOCI characteristics and GDPS workflow/products, so as to enable the efficient utilization of GOCI. To provide quality images and data, atmospheric correction and data analysis algorithms must be improved through continuous Cal/Val. GOCI-II will be developed by 2018 to facilitate in-depth studies on geostationary ocean color satellites.

New atmospheric correction technique to retrieve the ocean colour from SeaWiFS imagery in complex coastal waters

There exists a large demand for an accurate atmospheric correction of satellite ocean colour data over highly turbid coastal waters, where the standard atmospheric correction (SAC) algorithms designed for open ocean water turn out to be unsuccessful because of eventual interference of elevated radiance from suspended materials and perhaps the shallow bottom with the corrections based on the two near-infrared bands at 765 and 865 nm in which the water-leaving radiances are discarded (or modelled) in order to estimate aerosol radiative properties and extrapolate these into the visible spectrum in the atmospheric correction of the imagery. Furthermore, in the presence of strongly absorbing aerosols (e.g. Asian dust and Sahara dust) the SAC algorithms often underestimate water-leaving radiance values in the violet and blue spectrum or completely fail to deliver the desired biogeochemical products for coastal regions. To make the satellite ocean colour data offer unrivaled utility in monitoring and quantifying the components of ecologically important coastal waters, this study presents a more realistic and cost-effective image-based atmospheric correction method to accurately retrieve water-leaving radiances and chlorophyll concentrations from SeaWiFS imagery in the presence of strongly absorbing aerosols over highly turbid Northwest Pacific coastal waters. This method is a modified version of the spectral shape matching method (SSMM) previously developed by Ahn and Shanmugam (2004 Korean J. Remote Sens. 20 289–305), re-treating the assumption of spatial homogeneity of the atmosphere using simple models for assessing the contributions of aerosol and molecular scattering. Because of the difficulties in making atmospheric measurements concurrently with each overpass of SeaWiFS the atmospheric diffuse transmittance values are dependent on a standard method with the SAC scheme designed for processing SeaWiFS ocean colour data. The new method is extensively tested under the presence of various atmospheric conditions using SeaWiFS imagery and the results are compared with in situ (ship-borne) measurements in highly turbid coastal waters of the Korean Southwest Sea (KSWS). Such comparison demonstrates the efficiency of SSMM in terms of removing the effects of strongly absorbing aerosols (Asian dust) and improving the accuracy of water-leaving radiance retrieval with an RMSE deviation of 0.076, in contrast with 0.326 for the SAC algorithm which masked most of the sediment-laden and aerosol-dominated coastal areas. Further comparison in the Yellow Sea waters representing a massive phytoplankton bloom on 27 March 2002 revealed that the SAC algorithm caused an excessive correction for the visible bands, with the 412 nm band being affected the most, leading to severe overestimation of chlorophyll concentrations in the bloom-contained waters. In contrast, the SSMM remained very effective in terms of reducing errors of both water-leaving radiance and chlorophyll concentration estimates.

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.

A New Inversion Model to Retrieve the Particulate Backscattering in Coastal/Ocean Waters

Scientific implications and practical applications of spectral particulate backscattering (bbp(λ)) in oceanography are wide ranging, particularly in optical remote sensing as the light backscattered from various seawater constituents provides possibility to derive information about the particle properties of the water under investigation. Several inversion models have been previously developed for use with remote sensing reflectance (Rrs) data over open ocean waters; however, when applied to coastal waters, these models return bbp having large differences with in situ bbp values primarily because of the improper definitions of parameters of the functions describing the spectra of bbp(λ). The present study is aimed to develop a new inversion model with appropriate definitions of parameters of the functions to provide reliable retrievals of bbp in a variety of waters covering both the coastal and ocean environments. The new model is tested using large independent in situ data sets (NOMAD and Carder data sets) and simulated data provided by the IOCCG working group. When applied to these data sets, the new model outperformed the currently existing inversion models (e.g., GSM, LM and QAA models). The percent mean relative error (MRE) and root mean square error (RMSE) were found to be MRE -5.16 ~ 0.35% and RMSE 0.114 ~ 0.146 (in the 412-555 nm range) for the simulated data, MRE -0.14 ~ 2.42% and RMSE 0.125 ~ 0.157 for the NOMAD data, MRE -0.77% ~ 2.23% and RMSE 0.124 ~ 0.15 for the Korean regional data, and MRE 6.88% and RMSE 0.218 for the Carder data (for 490 nm only). Slopes close to unity, high R2 and low intercept values also indicated that the new model provides better performance over other models. The results further suggest that the new model is more robust and can be effectively applied to satellite ocean color data to retrieve the particulate backscattering coefficients in a variety of waters commonly found in the coastal and offshore domains.

Retrieval of ocean colour from high resolution multi-spectral imagery for monitoring highly dynamic ocean features

Retrieval of ocean colour information from a space borne Multi‐spectral Camera (MSC) on KOMPSAT‐2 is investigated to study and characterize small‐scale biogeophysical features that are very rich and dynamic in nature in the coastal oceans rather than the interior. Prior to the derivation of this information from space‐borne ocean colour observations, the path radiance largely from the atmospheric path and air–sea interface should be removed from the total signal recorded at the top of the atmosphere (TTOA ). In this study, the ‘path extraction’ method is introduced for the atmospheric correction of ocean colour images. The potential use of path extraction was demonstrated on Landsat TM and SeaWiFS images of highly turbid coastal waters of Korea. The path‐extracted water‐leaving radiance was then compared with the water‐leaving radiance spectra derived from the standard SeaWiFS atmospheric correction algorithm. It was noticed that the path‐extracted water‐leaving radiance resembled in situ spectra while the same was found highly degraded throughout the visible wavebands by adopting the standard SeaWiFS atmospheric correction algorithm. Algorithms for the retrieval of ocean colour information are explored from remotely sensed reflectance (Rrs ) in the visible wavelength bands of a Multi‐spectral Camera. A large set of remote sensing reflectances are generated by random number functions using an Rrs model, which relates bb /(a+bb ) to Rrs as functions of inherent optical properties, such as absorption and backscattering coefficients of six water components including water, phytoplankton (chl), dissolved organic matter (DOM), suspended sediment (SS) concentration, heterotropic organisms (he) and an unknown component, possibly represented by bubbles or other particulates unrelated to the first five components. Since the Kompsat‐2 MSC and Landsat‐5 TM bands are spectrally similar, these Rrs values are then modelled to the equivalent remote sensing reflectances at MSC and Landsat TM bands using a spectral band model. The empirical relationships between the spectral ratios of modelled Rrs (e.g. Rrs (MSC band1)/Rrs (MSC band2) and Rrs (MSC band1‐centre)/Rrs (MSC band2‐centre)) and chlorophyll concentrations are established in order to derive ⟨chl⟩ algorithms for both Landsat TM and MSC bands. Similarly, ⟨SS⟩ algorithms are obtained by relating a single band reflectance (e.g. Rrs (MSC band2) and Rrs (MSC Band2‐centre)) to the suspended sediment concentrations. Finally, a comparative analysis is made between the Landsat TM and MSC bands as well as narrow (centre‐wavelength) and broad band (full bandwidth) width of algorithms. From this study, it was observed that the Rrs spectra of three MSC spectral bands are found to be slightly superior to the Landsat TM bands in terms of spectral sensitivity to varying constituent concentrations. A small discrepancy between the reflectance ratios of broad and narrow bands was noticed in MSC and Landsat TM. The coefficient of determination (R 2) for log‐transformed data [⟨chl⟩ N = 500] was interestingly found to be R 2 = 0.90 for both Landsat TM and MSC. Similarly, the R 2 value for log‐transformed data [⟨SS⟩ N = 500] was 0.93 and 0.92 for Landsat TM and MSC, respectively. The modelled Rrs spectra were in good agreement with our in situ spectra obtained from the southern coastal Sea of Korea during 1998 and 1999. The algorithms presented are expected to explore the fine details of the complex coastal oceanic features from the ocean colour images of Multi‐spectral Camera and Landsat TM.

Surveillance of waste disposal activity at sea using satellite ocean color imagers: GOCI and MODIS

Korean Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations of the variation in ocean color at the sea surface were utilized to monitor the impact of nutrient-rich sewage sludge disposal in the oligotrophic area of the Yellow Sea. MODIS revealed that algal blooms persisted in the spring annually at the dump site in the Yellow Sea since year 2000 to the present. A number of implications of using products of the satellite ocean color imagers were exploited here based on the measurements in the Yellow Sea. GOCI observes almost every hour during the daylight period, every day since June 2011. Therefore, GOCI provides a powerful tool to monitor waste disposal at sea in real time. Tracking of disposal activity from a large tanker was possible hour by hour from the GOCI timeseries images compared to MODIS. Smaller changes in the color of the ocean surface can be easily observed, as GOCI resolves images at smaller scales in space and time in comparison to polar orbiting satellites, e.g., MODIS. GOCI may be widely used to monitor various marine activities in the sea, including waste disposal activity from ships.

Initial On-Orbit Modulation Transfer Function Performance Analysis for Geostationary Ocean Color Imager

The world`s first geostationary ocean color imager (GOCI) is a three-mirror anastigmat optical system 140 mm in diameter. Designed for 500 m ground sampling distance, this paper deals with on-orbit modulation transfer function (MTF)measurement and analysis for GOCI. First, the knife-edge and point source methods were applied to the 8th band (865 nm) image measured April 5th, 2011. The target details used are the coastlines of the Korean peninsula and of Japan, and an island 400 meters in diameter. The resulting MTFs are 0.35 and 0.34 for the Korean East Coastline and Japanese West Coastline edge targets, respectively, and 0.38 for the island target. The daily and seasonal MTF variations at the Nyquist frequency were also checked, and the result is on average. From these results, we confirm that the GOCI on-orbit MTF performance satisfies the design requirements of 0.32 for 865 nm wavelength.

Modeling the underwater light field fluctuations in coastal oceanic waters: Validation with experimental data

Modeling of the wave-induced underwater light fluctuations at near-surface depths in coastal oceanic waters is challenging because of the surface roughness and strong anisotropic effects of the light field. In the present work, a simple and computationally efficient radiative transfer model is used for the wind-driven sea surface for simulating underwater light fields such as downwelling irradiance (Ed), upwelling irradiance (Eu), and upwelling radiance (Lu) in a spatial domain. It is an extension of our previous work that essentially combines the air–sea interface of the wind-driven sea surface with transmittance and reflectance along with the diffuse and direct components of the homogenous and inhomogeneous water column. The present model simulates underwater light fields for any possible values of absorption and backscattering coefficients. To assess the performance of the model, the Ed, Eu, and Lu profiles predicted by the model are compared with experimental data from relatively clear and turbid coastal waters. Statistical results show significantly low mean relative differences regardless of the wavelength. Comparison of the simulated and in-situ time series data measured over rough sea surfaces demonstrates that model-observation agreement is good for the present model. The Hydrolight model when implemented with the modified bottom reflectance and phase function provides significantly better results than the original Hydrolight model without consideration of the bottom slope and vertically varying phase function. However, these results are non-spatial and have errors fluctuating at different wavelengths. To further demonstrate the efficiency of the present model, spatial distribution patterns of the underwater light fields are simulated based on the measured data from a coastal station for different solar zenith angles (under sunny condition). Simulated wave-induced fluctuations of the underwater lights fields show a good consistency with in-situ data for a few near-surface depths. The present model also provides a reasonable approximation for simulating wave-induced effects on the downward irradiance field and its anisotropic conditions caused by the surface roughness, wavelength and angle of incidence.

Prelaunch characterization of the Geostationary Ocean Color Imager

The instrument level ground test of the Geostationary Ocean Color Imager(GOCI) has been completed and integrated onto the Communication, Ocean and Meteorological Satellite(COMS) which is scheduled for launch in late 2009. In order to monitor the short-term biophysical phenomena with better temporal and spatial resolution, The GOCI has developed with eight VNIR bands, 500m GSD, and 2500km×2500km coverage centered at 36°N and 130°E. The GOCI planned to observe the full coverage region by every hour in daytime, and provide 8 images in daytime during single day. The GOCI ground test campaign for characterization and calibration has been performed by Korea Aerospace Research Institute(KARI), Korea and EADS Astrium, France. Korea Ocean Research & Development Institute(KORDI) has verified that test results satisfy all the GOCI performance requirements(Ex. MTF, SNR, Polarization, etc.) requested by KORDI. The GOCI has been sufficiently characterized under both of ambient and thermal-vacuum environments in order to develop the on-orbit radiometric calibration algorithm. GOCI radiometric model has been finalized with 3rd order polynomial. Because solar calibration is the on-orbit radiometric calibration method of the GOCI, Solar Diffuser made of fused silica and Diffuser Aging Monitoring Device(DAMD) are implemented as on-board calibration system. Diffusion factor of the Solar Diffuser and DAMD with respect to the solar incident angle, wavelength, and pixel location has been successfully characterized. Diffuser aging factor has been calculated for the compensation of the diffuser degradation by space environment. Diffusion factor of Solar Diffuser and DAMD, and diffuser aging factor characterized during prelaunch ground test are implemented into the GOCI radiometric calibration S/W developed by KORDI.

Application of optical remote sensing imagery for detection of red tide algal blooms in Korean waters

The present study involves analyzing chlorophyll-a (Chl-a) from SeaWiFS data collected over the period 1998-2002 to better understand the spatial and temporal aspects of red tide algal blooms created by Cochlodinium polykrikoides species in the enclosed and semi-enclosed bays of the South Sea of Korea. NOAA-AVHRR data is analyzed for sea surface temperature (SST) to elucidate physical factors affecting these aspects and abundance of Cochlodinium.p blooms. The time series Chl-a give an impression that recent red tide events with higher concentrations appear to have spanned more than 8 weeks in summer and fall seasons and were widespread in most of the South Sea coastal bays and neighboring ocean waters. The Chl-a estimates from SeaWiFS data appeared to be useful in demonstrating spatial and temporal aspects of these blooms, but uniquely identification of Cochlodinium.p from non-bloom and sediment dominated waters remains ineffective with these data. Thus the classical techniques such as Forward Principal Component Analysis (FPCA) and Minimum Spectral Distance (MSD) are attempted on both low spatial resolution SeaWiFS ocean color image data and high spatial resolution Landsat-7 ETM+ data. In September 2000, when an exchange of water masses took place off the southeastern coast, dense and mixed phases of the Cochlodinium.p blooms were conspicuous in the spectrally transformed SeaWiFS image. The existence of these phases inferred from SeaWiFS imagery was verified with both the atmospherically corrected and in-situ radiance spectra collected from these regions. Findings show that the application of SeaWiFS to uniquely identify Cochlodinium.p bloom from mixed and turbid plume was not feasible, but it was very effective with Landsat-7 ETM+ imagery detecting intricate and filament-like patterns of the Cochlodinium.p blooms and its dynamics owing to numerous physical mechanisms as determined by sea surface temperature from AVHRR infrared data.