Geostationary Ocean Color Satellite Research Articles

Using geostationary satellite ocean color data and superpixel to map the diurnal dynamics of water transparency in the eastern China seas

Polar-orbiting ocean color satellites can monitor daily to interannual variations in water transparency (or Secchi disk depth, SDD) from regional to global oceans. However, diurnal variations in SDD of coastal oceans remain poorly understood. Based on the bio-optical SDD algorithm, we retrieved the SDD products of hourly observations using the Geostationary Ocean Color Imager (GOCI) from 2011 to 2020 in the eastern China seas. The determination coefficient (R2) between the SDD product and the in situ dataset is 0.93, with a root mean squared error (RMSE) of 0.86 m. Based on the pixel-level tempo-spatial analysis, superpixel image segmentation retrieved by a simple linear iterative clustering algorithm (SLIC) was applied to classify the SDD product. The reconstruction SDD superpixel products not only match the spatial distribution well but can also more clearly express the spatial gradient. The percentage of the diurnal change in transparency was high in the nearshore (∼10 %), medium in transitional waters (∼5%), and low in offshore waters (∼3%). Finally, we found a significant negative correlation between SDD and wind speed (R2 = 0.65) and a significantly positive correlation between diurnal change range (DCR) and wind speed (R2 = 0.62). In contrast, sea surface temperature (SST) was positively correlated with SDD (R2 = 0.72) but significantly negatively correlated with DCR (R2 = 0.80). These results provide a basis for studying diurnal SDD changes in highly dynamic waters.

Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends.

High-frequency and tidal period observations of suspended particulate matter in coastal waters by AHI/Himawari-8.

Driven by tidal forcing and terrestrial inputs, suspended particulate matter (SPM) in shallow coastal waters usually shows high-frequency dynamics. Although specific geostationary satellite ocean color sensors such as the geostationary ocean color imager (GOCI) can observe SPM hourly eight times in a day from morning to afternoon, it cannot cover the whole semi-diurnal tidal period (∼12 h), and an hourly frequency may be insufficient to witness rapid changes in SPM in highly dynamic coastal waters. In this study, taking the Yangtze River Estuary as an example, we examined the ability of the geostationary meteorological satellite sensor AHI/Himawari-8 to monitor tidal period SPM dynamics with 10-min frequency. Results showed that the normalized water-leaving radiance (Lwn) retrieved by the AHI was consistent with the in-situ data from both cruise- and tower-based measurements. Specifically, AHI-retrieved Lwn was consistent with the in-situ cruise values, with mean relative errors (MREs) of 19.58%, 16.43%, 18.74%, and 26.64% for the 460, 510, 640, and 860 nm bands, respectively, and determination coefficients (R2) larger than 0.89. Both AHI-retrieved and tower-measured Lwn also showed good agreement, with R2 values larger than 0.75 and MERs of 14.38%, 12.42%, 18.16%, and 18.89% for 460, 510, 640, and 860 nm, respectively. Moreover, AHI-retrieved Lwn values were consistent with the GOCI hourly results in both magnitude and spatial distribution patterns, indicating that the AHI can monitor ocean color in coastal waters, despite not being a dedicated ocean color sensor. Compared to the 8 h of SPM observations by the GOCI, the AHI was able to monitor SPM dynamics for up to 12 h from early morning to late afternoon covering the whole semi-diurnal tidal period. In addition, the high-frequency 10-min monitoring by the AHI revealed the minute-level dynamics of SPM in the Yangtze River Estuary (with SPM variation amplitude found to double over 1 h), which were impossible to capture based on the hourly GOCI observations.

Estimation of spatial distribution of coastal ocean primary production in Hiroshima Bay, Japan, with a geostationary ocean color satellite

Ocean primary production measured by conventional methods using carbon isotope are mostly calculated from point sampling, which is not appropriate for estimating the temporal and spatial variations involved in marine ecosystems. Ocean color remote sensing can estimate spatial variation of ocean primary production at the global scale. However, the spatial resolution of the ocean color remote sensing is not adequate for estimating coastal ocean primary production. This study sought to establish a novel methodology for estimating coastal ocean primary production in Hiroshima Bay, Japan using high-resolution ocean color data (~500 m) and higher temporal resolution (hourly during the day) obtained by a geostationary ocean color satellite, GOCI-COMS. Estimated values of primary production derived from the ocean color satellite based on the Kameda & Ishizaka model did not correlate with observed primary production. We estimated primary production in Hiroshima Bay with a modified Kameda & Ishizaka model. The coefficient of determination between observed and estimated primary production by the ocean color satellite was 0.750. The slope of the linear regression was 1.00, and root mean square error was 165 mg-C m2 d−1. Our proposed method was thus sufficient for discussions of the changes in spatial distribution of primary production in Hiroshima Bay. Finally, spatial distribution of primary production in Hiroshima Bay was estimated using our proposed method. In January, the primary production of the Hiroshima Bay ranged <50-1000 mg-C m−2 d−1, and increased to 1000–1600< mg-C m−2 d−1 during seasonal blooms in spring and autumn. The primary production was relatively high at the innermost and western parts of the bay which affected by terrigeneous loads compared to other parts of the bay. In July 2016, highest primary production was observed at the innermost of the bay, ranging from 1200 to 1600< mg-C m−2 d−1. The high primary production was considered to be due to the supply of nutrients from terrigeneous load. Hence, our proposed method based on the high-resolution (approx. 500 m) ocean-color product has been enabled us to estimate primary production in coastal seas including complicated topographic areas such as enclosed seas or channels of less than 1 km wide.

Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China’s Seas: Impacts of Cloud Coverage and Sediment Variations

Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013–2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China’s seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (Pbias) at 14.1% (±2.6%), 7.6% (±2.3%) and 12.2% (±4.3%) for TSS of the ECS, BS and YS, respectively. Cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly Pbias ranging from 6.5% (±7.4%) to 20% (±13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant “missing trends” effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated the effects of cloud coverage and times for sampling on the remote sensing monitoring of ocean color constituents, which would suggest the most appropriate timeframe for ocean color sensor scans, as well as in situ data collection, and can provide design specification guidance for future satellite sensor systems.

Assessment of the Number of Valid Observations and Diurnal Changes in Chl-a for GOCI: Highlights for Geostationary Ocean Color Missions.

The first geostationary ocean color satellite mission (geostationary ocean color imager, or GOCI) has provided eight hourly observations per day over the western Pacific region since June 2010. GOCI imagery has been widely used to track the short-term dynamics of coastal and inland waters. Few studies have been performed to comprehensively assess the advantages of GOCI images in obtaining valid observations and estimating diurnal changes within the water column. Using the entire mission dataset between 2011 and 2017, these knowledge gaps were filled by comparing the daily percentages of valid observations (DPVOs) between GOCI and MODIS Aqua (MODISA) and by examining the diurnal changes in Chl-a over the East China Sea. The mean DPVOs of GOCI was 152.6% over the clear open ocean, suggesting that a daily valid coverage could be expected with GOCI. The GOCI DPVOs were ~26 times greater than the MODISA DPVOs; this pronounced difference was caused by the combined effects of their different observational frequencies and the more conservative quality flag system for MODISA. Diurnal changes in the GOCI-derived Chl-a were also found, with generally higher Chl-a in the afternoon than the morning and pronounced heterogeneities in the temporal and spatial domains. However, whether such diurnal changes are due to the real dynamics of the oceanic waters or artifacts of the satellite retrievals remains to be determined. This study provides the first comprehensive quantification of the unparalleled advantages of geostationary ocean color missions over polar orbiters, and the results highlights the importance of geostationary ocean color missions in studying coastal and inland waters.

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

The Geostationary Ocean Color Imager (GOCI) sensor, with high temporal and spatial resolution (eight images per day at an interval of 1 hour, 500 m), is the world’s first geostationary ocean color satellite sensor. GOCI provides good data for ocean color remote sensing in the Western Pacific, among the most turbid waters in the world. However, GOCI has no shortwave infrared (SWIR) bands making atmospheric correction (AC) challenging in highly turbid coastal regions. In this paper, we have developed a new AC algorithm for GOCI in turbid coastal waters by using quasi-synchronous Visible Infrared Imaging Radiometer Suite (VIIRS) data. This new algorithm estimates and removes the aerosol scattering reflectance according to the contributing aerosol models and the aerosol optical thickness estimated by VIIRS’s near-infrared (NIR) and SWIR bands. Comparisons with other AC algorithms showed that the new algorithm provides a simple, effective, AC approach for GOCI to obtain reasonable results in highly turbid coastal waters.

Radiometric Sensitivity and Signal Detectability of Ocean Color Satellite Sensor Under High Solar Zenith Angles

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean–atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio ( $\Delta $ SNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL $= 1\,\,\mu \text{g}$ /L, TSM = 1 mg/L, CDOM = 0.15 m−1), with the typical observation zenith angle (OZA) = 30°, changes on the order of $\Delta $ CHL $= 0.024\,\,\mu \text{g}$ /L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal $\Delta $ CHL increased to $0.77~\mu \text{g}$ /L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes ( $\Delta $ CDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), $\Delta $ CDOM = 0.007 m−1 which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), $\Delta $ TSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.

정지궤도 해양위성 2호 지상시스템의 설계 및 구현

정지궤도 해양위성 2호 지상시스템의 설계 및 구현

Semi-analytical algorithms of ocean color remote sensing under high solar zenith angles.

With the increasing interest in ocean color remote sensing in polar oceans and geostationary ocean color satellite with diurnal observations, it is unavoidable to encounter ocean color retrievals under high solar zenith angles. Under these scenarios, the capability of current remote sensing algorithms is poorly known. In this study, the performance of the two widely used semi-analytical algorithms for the water inherent optical properties (QAA and GSM01) under high solar zenith angle conditions were firstly evaluated based on global in situ data set (SeaBASS-NOMAD). The results showed that the performances of both QAA and GSM01 degraded significantly with the increasing in solar zenith angle (SZA), and the biases increased about 1.3-fold when SZA varied from 30° to 80°. The high uncertainties at high SZA was mainly induced by the systematic overestimation of the key parameter u (ratio of backscattering coefficient to the sum of absorption and backscattering coefficients) at high solar zenith angles. Based on the Hydrolight-simulated data set, a new model (NN-algorithm) for retrieving u from remote sensing reflectance was developed for high solar zenith angle conditions using the neural network method. The validation results revealed that the NN-algorithm could improve the estimation of parameter u and further ocean color products. In addition, our results indicate that a more accurate atmosphere correction is needed to deal with ocean color remote sensing data acquired under large solar zenith angle conditions.

Simulation of Sedimentation in Lake Taihu with Geostationary Satellite Ocean Color Data

In this study, the goal is to estimate the sedimentation on the bottom bed of Lake Taihu using numerical simulation combined with geostationary satellite ocean color data. A two-dimensional (2D) model that couples the dynamics of shallow water and sediment transport is presented. The shallow water equations are solved using a semi-implicit finite difference method with an Alternating Direction Implicit (ADI) method. Suspended sediment transport is simulated by solving the general convection-diffusion equation with resuspension and deposition terms using a second-order explicit central difference method in space and two-step Adams–Bashforth method in time. Moreover, the total suspended particulate matter (TSM) is retrieved by the world’s first geostationary satellite ocean color sensor Geostationary Ocean Color Imager (GOCI) using atmospheric correction algorithm for turbid waters using ultraviolet wavelengths (UV-AC) and regional empirical TSM algorithm. The 2D model and GOCI-retrieved TSM are applied to study the sediment transport and sedimentation in Lake Taihu. Validation results show rationale TSM concentration retrieved by GOCI, and the simulated TSM concentrations are consistent with GOCI observations. In addition, simulated sedimentation results reveal the dangerous locations that must be observed and desilted.

Diurnal Changes Monitoring and Analysis of the Total Suspended Matters in Bohai Sea Using Geostationary Ocean Color Imager

Since the launch of the world’s first geostationary satellite ocean color sensor, the Geostationary Ocean Color Imager (GOCI), it is possible to realize the hourly observations and trace the diurnal changes of color matters in target waters. In this paper, we first developed an empirical TSM retrieval algorithm of Bohai Sea based on in situ spectral data. Then we applied the algorithm to the GOCI images on September 13, 2015. Results showed that TSM concentration in Bohai Sea had a good relationship with band five of GOCI. The concentrations of TSM were high along the coast and low in the offshore areas. The concentrations were extremely high around Huanghe Reiver Delta, such as Bohai Bay and Laizhou Bay. The deviation analysis also showed a great diurnal changes around the north part of Bohai Bay and Laizhou Bay. This paper highlight the feasibility of using geostationary ocean color remote sensing to monitor short-term and regional color matters in coastal waters.

Characterizing surface circulation in the Taiwan Strait during NE monsoon from Geostationary Ocean Color Imager

The Taiwan Strait is the only channel connecting the South China Sea (SCS) and the East China Sea (ECS), and plays a key role in the material exchanges between SCS and ECS. The region however is poorly sampled during the northeast (NE) monsoon when severe weather hinders shipboard measurements. In this study, the surface circulations in the Taiwan Strait and the adjacent East China Sea are derived from the Geostationary Ocean Color Imager (GOCI) with a high spatiotemporal resolution. The satellite observations are limited by cloud over. Nevertheless, they provide the only surface flow observations useful in hypothesis testing. It is demonstrated that the transient surface currents can be classified into three regimes: a southwestward flow for strong winds, a northeastward flow for weak winds, and a cross-strait flow for moderate winds. The satellite observations are also valuable in validating model currents. Two high-resolution global ocean reanalysis products, the Hybrid Coordinate Ocean Model (HYCOM) and the Copernicus Marine Environment Monitoring Service (CMEMS), are examined. The reanalysis products are generally consistent with the satellite observations. The CMEMS moreover shows a superior performance. This study illustrates the unique capability of the geostationary ocean color satellite in providing spatially extensive surface flow observations in broad shelf regions where current meter data are sparse or non-existent.

Diurnal Dynamics and Seasonal Variations of Total Suspended Particulate Matter in Highly Turbid Hangzhou Bay Waters Based on the Geostationary Ocean Color Imager

Characterized by high concentrations and significant diurnal dynamics, total suspended particulate matter (TSM) in Hangzhou Bay (HZB) can influence the water quality. Both cruise sampling and polar-orbiting satellite remote sensing make it difficult to synoptically delineate such matter in coastal regions due to insufficient spatiotemporal resolution. However, near-real-time monitoring from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary satellite ocean color sensor, now allows hourly observations of target areas. Here, we developed an empirical TSM retrieval algorithm for the GOCI, based on the spectral absorption index (SAI). Additionally, validations of the atmospheric correction algorithm and SAI TSM algorithm were conducted, with the GOCI-retrieved normalized water-leaving radiance (Lwn) and TSM well-matched with in situ values. Moreover, the diurnal dynamics corresponded to tidal phases, with a maximum of 5000 mg/L in central HZB. The seasonal variations in TSM were also determined by freshwater inputs and sediment loads, with correlation coefficients of −0.77 and 0.84, respectively. The increased wind speed in winter enhanced vertical mixing and favored the resuspension of sediment to the upper layer, and therefore TSM was higher in winter than in summer. These results highlight the feasibility of using geostationary ocean color remote sensing to monitor short-term and regional oceanic phenomena in coastal waters. Furthermore, this study contributes to our understanding of related sediment dynamics and physical biogeochemical processes.

Using Geostationary Ocean Color Imager to Map the Diurnal Dynamics of Suspended Sediment Concentration in Estuary Area

ABSTRACT Kang, Y.Y.; Ding, X.R., and Ge, X.P., 2018. Using Geostationary Ocean Color Imager to map the diurnal dynamics of suspended sediment concentration in estuary area. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 101–105. Coconut Creek (Florida), ISSN 0749-0208. Geostationary ocean color imager (GOCI), the first in-orbit operation of the geostationary satellite ocean color sensor, provides hourly observations of the covered area. Quick atmospheric correction (QUAC) algorithm was used for GOCI data to remove the atmospheric effects and an artificial neural network (ANN) inversion model (multi-layer feed forward neural network) was proposed to derive suspended sediment concentration (SSC) via GOCI in coastal waters of Yongjiang estuary, China. This model has three parts those are the input layer with eight nodes, two hidden layers with seventeen nodes in...

Hourly changes in sea surface salinity in coastal waters recorded by Geostationary Ocean Color Imager

In this study, we monitored hourly changes in sea surface salinity (SSS) in turbid coastal waters from geostationary satellite ocean color images for the first time, using the Bohai Sea as a case study. We developed a simple multi-linear statistical regression model to retrieve SSS data from Geostationary Ocean Color Imager (GOCI) based on an in situ satellite matched-up dataset (R2 = 0.795; N = 41; Range: 26.4 to 31.9 psμ). The model was then validated using independent continuous SSS measurements from buoys, with the average percentage difference of 0.65%. The model was applied to GOCI images from the dry season during an astronomical tide to characterize hourly changes in SSS in the Bohai Sea. We found that the model provided reasonable estimates of the hourly changes in SSS and that trends in the modeled and measured data were similar in magnitude and direction (0.43 vs 0.33 psμ, R2 = 0.51). There were clear diurnal variations in the SSS of the Bohai Sea, with a regional average of 0.455 ± 0.079 psμ (0.02–3.77 psμ). The magnitude of the diurnal variations in SSS varied spatially, with large diurnal variability in the nearshore, particularly in the estuary, and small variability in the offshore area. The model for the riverine area was based on the inverse correlation between SSS and CDOM absorption. In the offshore area, the water mass of the North Yellow Sea, characterized by high SSS and low CDOM concentrations, dominated. Analysis of the driving mechanisms showed that the tidal current was the main control on hourly changes in SSS in the Bohai Sea.

Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions

Numerous empirical algorithms have been operationally used to retrieve global ocean chlorophyll-a (Chla) concentrations from ocean color satellite data, for example, the OC4V4 algorithm for sea-viewing wide field-of-view sensor and the OC3M algorithm for moderate-resolution imaging spectroradiometer. However, the algorithms have been established and validated based on in situ data mainly measured under low to moderate solar zenith angle (SZA) (<70 deg). Currently, with the development of geostationary satellite ocean color remote sensing, enabling observations from early morning to late afternoon, it has become necessary to know whether the empirical Chla algorithms can be applied to high SZA. The performances of seven widely used Chla algorithms (i.e., OC2, OC3M, OC3V, OC4V4, Clark, ocean-color index, and Yellow Sea Large Marine Ecosystem Ocean Color Work Group) under high SZAs were evaluated using the global in situ ocean color dataset (NASA bio-optical marine algorithm dataset). The results show that the performances of all seven algorithms decreased significantly under high SZAs compared with those under low to moderate SZAs. For instance, for the OC4V4 algorithm, the relative percent difference (RPD) and root-mean-square error (RMSE) were 13.78% and 1.66 μg/L for the whole dataset and 3.95% and 1.49 μg/L for SZAs ranging from 30 deg to 40 deg, respectively. However, RPD and RMSE values increased to 30.45% and 6.10 μg/L for SZAs larger than 70 deg. Adjusting the coefficients of the algorithms using the in situ dataset with high SZA can only slightly improve the performance. The bidirectional remote sensing reflectance can explain the underestimation of the retrieved Chla under high SZA, and the low sensitivity of the blue–green ratio is responsible for the relatively larger scatter between the retrieved and the in situ Chla under high SZA.

Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing

Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes.

Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands

The Geostationary Ocean Color imager (GOCI) is the first geostationary ocean color satellite sensor that collects hourly images eight times per day during daylight. This high frequency image acquisition makes it possible to study more detailed dynamics of red tide blooms, sediment plumes, and colored dissolved organic matter plumes, and can aid in the prediction of biophysical phenomena. We apply the red band difference and the fluorescence line height algorithms to GOCI imagery to separate waters with high algal and nonalgal particles and validate the results with the MODIS imagery. We also track optical features using hourly GOCI imagery and assess their movement through comparisons with predicted ocean currents derived from the navy coastal ocean model and tidal data.

Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI

Harmful algal blooms (HABs) in the East China Sea (ECS) have been reported every year in the last decade, and satellite remote sensing has often been used to study the bloom size and duration. Yet satellite remote sensing suffered from lack of reliable algorithms to detect HABs in optically complex coastal waters and from frequent cloud cover. Thus, it has not been possible to document short-term changes of HABs in synoptic scales. Here, using measurements from the Geo-stationary Ocean Color Imager (GOCI), we studied diurnal changes of a HAB of the dinoflagellate Prorocentrum donghaiense in the ECS in May 2011. The standard remote sensing reflectance (Rrs) products from the GDPS processing algorithms showed promise in delineating HABs in turbid coastal waters, yet the cloud-masking is often too tight to reveal valid cloud-free data. An alternative approach was developed to circumvent this difficulty by using the Rayleigh-corrected reflectance (Rrc) and a normalization technique. A modified red tide index (RI) was developed from the normalized Rrc data at 443, 490 and 555nm, and proven effective in delineating the P. donghaiense bloom in sediment-rich waters. The hourly RI images on 29 and 30 May 2011 (8images per day from 8:30 to 15:30) showed consistent bloom evolution through the course of a day, with physical locations driven by tides while its surface expression increased significantly from early morning to early afternoon (maximum around 14:30 local time, a 124% and 163% increase from 9:30, respectively). The maximum coverage of the HAB at 14:30 on 29 May 2011, when no cloud was observed, reached 6620km2. While the short-term changes in the surface expression could be a result of the horizontal dilution due to tides, vertical migration of the dinoflagellate from early morning to afternoon, as reported elsewhere, may be a dominant reason. The case study here demonstrates the unique value of a geostationary satellite ocean color sensor in revealing short-term dynamics of HABs.