Geostationary Ocean Color Imager Research Articles

A Study on Seasonal Dynamics of Suspended Particulate Matter in Korean Coastal Waters Using GOCI

Lee, Y.-K.; Choi, J.-K., and Lee, H.-J., 2020. A study on seasonal dynamics of suspended particulate matter in Korea coastal waters using GOCI. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 232-245. Coconut Creek (Florida), ISSN 0749-0208.Analysis of suspended particulate matter (SPM) is a key to understanding the turbulent sediment flow in the Heuksan mud belt (HMB), located along the southwestern coast of the Korean Peninsula. The purpose of this study was to investigate intra-annual variability in remotely sensed SPM data derived from the Geostationary Ocean Color Imager (GOCI) around the HMB over a period of 1 year (2013). Monthly averaged SPM images obtained by the GOCI showed pronounced seasonal changes in turbid water. Dominant environmental factors for intra-annual and diurnal variability of SPM, including river discharge, winds, and tides, were assessed. Monsoon wind dominated seasonal variation in SPM dispersion; the extent of turbid water increased during the winter and decreased during the summer months. Diurnal variation observed from 1-day composite images showed clearly that tidal current was the dominant factor affecting the short-term dispersal pattern of SPM. The gate-controlled river discharge showed the reverse seasonal pattern compared with SPM concentration. However, a turbid plume from the Geum River observed in GOCI data could support the identification of this river as the major source of the HMB. In conclusion, this study demonstrated that the spatial and temporal dynamics of the HMB, including intra-annual and diurnal variability, can be successfully detected using the GOCI.

Role of emissions and meteorology in the recent PM2.5 changes in China and South Korea from 2015 to 2018.

In this study, we examined the change rates of PM2.5 concentrations, aerosol optical depth (AOD), and the concentrations of PM2.5 precursors, such as SO2 and NO2, in China and South Korea using surface and satellite observations from 2015 to 2018. To quantify the impacts of the emissions and meteorology on the concentration changes, we performed a series of air quality simulations with year-specific meteorology and a fixed anthropogenic emissions inventory. The surface PM2.5 observations in China and South Korea decreased at rates of 9.1 and 4.3%/yr during the study period, respectively. The AODs from Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) also decreased faster over China than the AODs over South Korea. For the PM2.5 decrease in China, the emission impact was more significant (73%) than the meteorology impact (27%). On the contrary, in South Korea, the emissions and meteorology impacts on PM2.5 reductions were similar (51% vs 49%). The SO2 concentration over China in 2018 significantly reduced to approximately half of the level in 2015. In turn, the sulfate concentration in Baengnyeong (BN), located in a downwind pathway from China to South Korea, decreased at a rate of 0.79%/month. However, the nitrate concentration in BN showed an increasing trend due to the non-linear chemical reactions among sulfate-nitrate-ammonium. The increased nitrate compensated for the reduced PM2.5 concentration from the sulfate decrease at BN. Additionally, the number of high (>50μg/m3) PM2.5 concentration days continuously decreased in China, but the number in South Korea increased. It is noted that emission reductions in an upwind area do not guarantee corresponding air quality improvement in the downwind area when complex secondary aerosol formation processes, as well as spatiotemporal changes in meteorology, are involved in the transboundary transport of air pollutants.

Application of Parallel Processing Techniques to Satellite Ocean Color Data Processing

Recent advances demand that remote-sensing satellites efficiently process massive amounts of ocean color data. This paper compares the open multi-processing (OpenMP), the open computing language (OpenCL), the Message Passing Interface (MPI), the hybrid MPI/OpenMP, and the hybrid MPI/OpenCL in the parallel implementation of ocean color processing algorithms using data from the Geostationary Ocean Color Imager (GOCI), which is the first ocean color remote sensor operated in geostationary orbit. Since 2010, GOCI has observed ocean color around the Korean Peninsula and has generated hundreds of terabytes of big data. When any of the data-processing algorithms are updated, all preexisting data is required to be reprocessed, which can take hundreds of days because GOCI data are currently processed sequentially. Therefore, we attempted to develop an efficient parallel processing methodology for GOCI data. We tested well-known GOCI data-processing algorithms, like the chlorophyll (CHL) and total suspended solid (TSS) concentration estimation algorithms, using a cluster system. This cluster uses the Red Hat Linux operating system with two Intel Xeon 8-core processors (CPU), an AMD Radeon HD 7970 (GPU), and InfiniBand 4x QDR (network). As a result of this study we were able to improve the GOCI ocean color algorithms

An Analysis on the Distribution of Floating Seaweed in the East China Sea and Southern Yellow Sea in 2015–the Case of Sargassum observed by the Geostationary Ocean Color Imager

In early 2015, a large amount of brown seaweed, known as Sargassum horneri macroalgae, was piled up along the shore of Jeju Island and the southwest islands of the Korean Peninsula. This event was associated with a huge bloom of floating Sargassum in the East China Sea (ECS) and southern Yellow Sea (SYS). Ship surveys or aerial surveys can only cover a limited space and are time consuming and expensive. This study aims to capture temporal variation in the geographical distribution of floating S. horneri using satellite imagery obtained from the Geostationary Ocean Color Imager (GOCI). The GOCI acquires eight images a day with a 500-m spatial resolution, a high signal-to-noise ratio and a constant viewing angle, providing imagery suitable for monitoring a large-scale floating algae event and its temporal evolution. Semi-monthly aggregated images were generated to determine fractional coverage area per pixel or density of the floating algae from January to June, 2015. The results are consistent with previous field-survey-based studies, but also reveal a number of new findings. Unexpectedly, S. horneri patches were detected as early as January over a broad area of the ECS continental shelf. The floating algae were detected not only near the outer continental shelf area along the Kuroshio front and Eastern Kuroshio Branch current as previously reported in literature but also along the western inner continental shelf. The floating seaweed patches along the eastern outer shelf proliferated during the second half of March, then moved north, entering the Korea-Tsushima Straits in the west of Kyushu to the north. The algae patches in the western shelf moved north in April and May and separated, one entering the Jeju Strait and the Korea-Tsushima Straits, and the other entering the SYS. Overall, S. horneri density peaked in late April, then decreased in May and June before disappearing from the ECS in July.

Estimating Water Reflectance at Near-Infrared Wavelengths for Turbid Water Atmospheric Correction: A Preliminary Study for GOCI-II

Atmospheric correction is a fundamental process to remove the atmospheric effect from the top-of-atmosphere level. The atmospheric correction algorithm developed by the Korea Institute of Ocean Science and Technology employs a near-infrared (NIR) water reflectance model to deal with non-negligible NIR water reflectance over turbid waters. This paper describes the NIR water reflectance models using visible bands of the Second Geostationary Ocean Color Imager (GOCI-II). Whereas the previous GOCI uses the 660 nm band to estimate NIR water reflectance (SR660), GOCI-II uses additional 620 and 709 nm bands, which improves estimation of NIR water reflectance. We developed two reflectance models with the additional bands based on a spectral relationship of water reflectance (SR709) and a spectral relationship of inherent optical properties (SRIOP) from red to NIR wavelengths. A preliminary validation of these two reflectance models was performed using both simulations and an in situ dataset. The validation result showed that the mean absolute percentage error of the SR709 model compared with SR660 was reduced by approximately 6% and 10% at 745 and 865 nm, respectively. Moreover, the mean absolute percentage error of the SRIOP model compared with SR660 was reduced by approximately 12% and 16% at 745 and 865 nm, respectively. Note that SR709 produces the most accurate result when there is only one sediment type, and SRIOP shows the most accurate result when various sediment types exist. Users will be able to optionally select the appropriate NIR water reflectance models in the GOCI-II atmospheric correction process to enhance the accuracy of aerosol reflectance correction over turbid waters.

Turbidity Estimation from GOCI Satellite Data in the Turbid Estuaries of China’s Coast

Knowledge of the distribution and variation of water turbidity directly represent important information related to the marine ecology and multiple biogeochemical processes, including sediment transport and resuspension and heat transfer in the upper water layer. In this study, a neural network (NN) approach was applied to derive the water turbidity using the geostationary ocean color imager (GOCI) data in turbid estuaries of the Yellow River and the Yangtze River. The results showed a good agreement between the GOCI-derived turbidity and in situ measured data with a determination coefficient (R2) of 0.84, root mean squared error (RMSE) of 58.8 nephelometric turbidity unit (NTU), mean absolute error of 25.1 NTU, and mean relative error of 34.4%, showing a better performance than existing empirical algorithms. The hourly spatial distributions of water turbidity in April 2018 suggested that high turbidity regions were distributed in the Yellow River estuary, Yangtze River estuary, Hangzhou Bay, and coastal waters of Zhejiang Province. Furthermore, the relationship between water turbidity and tide were estimated. A defined turbid zone was defined to evaluate the diurnal variations of turbidity, which has subtle changes at different times. Our results showed an inverse relationship between turbidity and tide over six selected stations, i.e., when the value of turbidity is high, then the corresponding tidal height is usually low, and vice versa. The combined effects of tidal height and tidal currents could explain the phenomena, and other factors such as winds also contribute to the turbidity distributions.

Tracking flood debris using satellite-derived ocean color and particle-tracking modeling

Flood debris associated with Typhoon Lionrock from the Tumen River at the border between Russia and North Korea was traced using ocean color and a Lagrangian particle-tracking model. As debris is transported along with discharged water during floods, a means of tracing floodwater should also allow any associated debris to be tracked. By analyzing the anomalous distribution of colored dissolved organic matter (CDOM) and total suspended sediments (TSS) from the Geostationary Ocean Color Imager (GOCI), the southward movement of the floodwater was tracked along the eastern coast of the Korean Peninsula. This movement was driven by the North Korean Cold Current and was consistent with model results. The similarity between the satellite-derived and modeled datasets shows that CDOM and TSS can be used to track flood-derived debris for several hundreds of kilometers and locate hotspots of debris accumulation.

Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations.

To better understand air quality issues in South Korea, it is essential to identify the main contributors of air pollution and to quantify the effects of transboundary transport. In this study, geostationary satellite measurements were used to assess the effects of aerosol transport on air quality in South Korea. This study proposes a method to define the long-range transport (LRT) of aerosols into the Korean Peninsula using remote sensing obervations and back-trajectories and estimates the LRT effects on air quality in Seoul using in-situ particulate matter (PM) measurements. Aerosol optical depths (AODs) are obtained from the Geostationary Ocean Color Imager (GOCI), and the back-trajectories are from the National Ocean and Atmospheric Administration (NOAA) HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. For LRT events, satellite observations showed high AOD plumes over the Yellow Sea, a pathway between Eastern China and South Korea, and the movements of aerosol plumes transported to South Korea were also detected. PM2.5 concentrations, PM10 concentrations, and AOD during LRT increased by 52%, 49%, and 81%, respectively, relative to their average values for 2015-2018. To quantitatively characterize the LRT of aerosols, the effects of LRT on PM2.5 concentrations were estimated for each PM concentration category. The contribution of LRT to PM2.5 concentrations was estimated to be 33% during 2015-2018. When high concentrations of PM2.5 were observed in Seoul, they were likely to be associated with LRT events.

Optical and chemical properties of long-range transported aerosols using satellite and ground-based observations over seoul, South Korea

Aerosols that are transported long distances to the Korean Peninsula due to seasonal weather conditions can affect the local air quality of South Korea. In general, the long-range transported aerosols are widely distributed and variable with respect to time and space. In this aspect, geostationary satellites can be used as a powerful tool for providing temporal and spatial variation of aerosols by sequentially observing a target area. In this study, select events of high-concentration aerosols transported to Seoul, the capital of South Korea, were identified and analyzed using aerosol optical properties retrieved from geostationary ocean color imager (GOCI) for the cold season (October–March) from 2015 to 2018. In addition to the satellite data, ground-based observations of particulate matter (PM) and backward trajectory models were used. Based on the trajectories of air masses simulated with the backward trajectory models, events of the long-range transported high-concentration aerosols (hereafter referred to as “LRT events”) were selected and classified into three types: yellow dust, PM traversing over the Yellow Sea (PM-YS), and PM passing through North Korea (PM-NK). Optical and chemical properties of the three LRT events were analyzed, and significantly different features were found. Yellow dust showed a relatively high mean depolarization ratio at 1020 nm (0.26), a low Ångström exponent between 440 nm and 870 nm (0.27), fine mode fraction at 500 nm (0.26), single scattering albedo at 440 nm (0.93), and lidar ratio at 675 nm (37 sr) compared with those during the other PM events. Moreover, the proportions of Ca+ concentrations to the water-soluble ions in PM2.5 and concentrations of Ca and Fe among inorganic elements in PM2.5 significantly increased during the yellow dust event. On the contrary, the PM events (PM-YS and PM-NK) had relatively high mean Ångström exponent (1.28 and 1.38), fine mode fraction (0.94 and 0.81), single scattering albedo (0.96 and 0.94), lidar ratio (66 sr and 44 sr), and low depolarization ratios (0.035 and 0.066). The ratios of NO3− and SO42− to water-soluble ions in PM2.5 were significantly increased during the high PM events. By comparing the two PM events, the volume median radius of fine mode particles and fine mode fraction of PM-YS were found to be considerably higher compared with those of PM-NK. Such different characteristics found in this study for the three LRT events indicate that optical and chemical analyses of aerosols using satellite and ground-based observations can give us the possibility of roughly identifying aerosol types (yellow dust or PM) and trajectories (passing through the Yellow Sea or North Korea).

Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling

Quantitative estimates of the flux, transportation, and burial of riverine particulate organic carbon (POC) in estuaries and shelf areas are crucial for understanding the carbon cycle in marginal seas. The complexity of the hydrodynamic environment and estuarine processes in the Changjiang River estuary (CRE) results in a POC flux with high spatiotemporal variability. In this study, we proposed a new method for time series monitoring of riverine POC flux by combining satellite ocean color and numerical modeling data. We used 500-m resolution geostationary satellite data from the Geostationary Ocean Color Imager (GOCI) to retrieve surface POC concentrations in the CRE and used the numerical Finite Volume Community Ocean Model (FVCOM) to simulate three-dimensional (3D) current and sediment distribution. Diurnal, seasonal, and annual variations in POC flux through the lower reaches (Datong Hydrological Station), upper estuary (Xuliujing Hydrological Station), and outlets of the CRE, as well as transportation and deposition outside the CRE, were examined from July 2011 to June 2018. The annual mean POC fluxes through the Datong, Xuliujing, and outlet sections were 1.16 ± 0.16 Tg C/yr, 1.29 ± 0.11 Tg C/yr, and 1.17 ± 0.11 Tg C/yr, respectively. After passing through the estuary, 35.2% of POC was deposited in the sandbar outside the outlets, 52.9% was transported to the south, and a small amount entered the eastern shelf. Thus, these results indicated that POC flux through the river and estuary sections differed from the effective riverine POC flux into the sea. The proposed method combining high spatiotemporal-resolution satellite data and numerical modeling not only makes full use of their advantages, but also reduces the uncertainty of their individual estimates. This approach also supports long-term monitoring of riverine fluxes to the sea and helps clarify the effects of terrestrial inputs on marginal seas under multiple stresses from human activities and climate change.

Drift path of green tide and the impact of typhoon “Chan-hom” in the Chinese Yellow Sea based on GOCI images in 2015

Since 2007, green tide disasters have occurred in Chinese Yellow Sea for 11 consecutive years. The continuous outbreak of green tides has caused various degrees of economic loss and ecological damage to coastal areas. In this study, we use GF1-WFV (GaoFen No.1-Wide Field of View) high resolution images and GOCI (Geostationary Ocean Color Imager) images comprehensively. NDVI (Normalized vegetation index), linear decomposition model of mixed pixels (LDMMP) and artificially assisted interpretation methods were used to monitor the green tide eruptions in the Yellow Sea in 2015. The impact of the typhoon “Chan-hom” on the green tide growth process and drift path were analyzed for the first time. The results showed that: The accuracy of extracting green tide area by LDMMP method is higher than that of NDVI. Based on GOCI images, the distribution of green tides in the study area in 2015 was obtained using the LDMMP method; From May to August, the green tide in the Yellow Sea has undergone the process of “emergence-development-explosion-decline-disappearance”; Typhoon “Chan-hom” changed the path of the green tide continuing to the north to a certain extent and drifted to the southeast, causing the center of the green tide to move to Lianyungang Eastern waters.

Dynamics of euphotic zone depth in the Bohai Sea and Yellow Sea

Euphotic zone depth (Zeu) plays an important role in studies of marine biogeochemical processes and ecosystems. Remote sensing techniques are ideal tools to investigate Zeu distributions because of their advanced observation ability with broad spatial coverage and frequent observation intervals. This study aims to develop a new approach that derives Zeu directly from remote sensing reflectance (Rrs(λ)) values rather than by using other intermediate variables and then reveals the dynamic characteristics of Zeu in the Bohai Sea (BS) and Yellow Sea (YS). To do this, in situ data collected from various seasons were first used to assess the ability of several spectral indicators of Rrs(λ) for deriving Zeu and the optimal spectral indicator was determined to build a Zeu retrieval model. This model was further applied to Geostationary Ocean Color Imager (GOCI) data to study the spatial and temporal variations in Zeu. The results showed that the new Zeu retrieval model performed well with R2, RMSE and MAPE values of 0.843, 4.42 m and 17.9%, respectively. High Zeu levels were generally observed during summer for both coastal and offshore waters while the lowest Zeu values were observed during winter. Changing concentrations of total suspended matter, which are often modulated by sediment resuspension and transportation, are probably the main factor responsible for the spatial and temporal variability of Zeu. These findings provide crucial information for modeling primary production, carbon flux, and heat transfer, etc., in the BS and YS, as well as contribute a useful alternative approach that will be easily implemented to study Zeu from satellite data for other water environments.

Diurnal to Seasonal Variations in Ocean Chlorophyll and Ocean Currents in the North of Taiwan Observed by Geostationary Ocean Color Imager and Coastal Radar

The waters in the north of Taiwan are located at the southern end of the East China Sea (ECS), adjacent to the Taiwan Strait (TS), and the Kuroshio region. To understand the physical dynamic process of ocean currents and the temporal and spatial distribution of the ocean chlorophyll concentration in the north of Taiwan, hourly coastal ocean dynamics applications radar (CODAR) flow field data and geostationary ocean color imager (GOCI) data are analyzed here. According to data from December 2014 to May 2020, the water in the TS flows along the northern coast of Taiwan into the Kuroshio region with a velocity of 0.13 m/s in spring and summer through the ECS. In winter, the Kuroshio invades the ECS shelf, where the water flows into the TS through the ECS with a velocity of 0.08 m/s. The seasonal variation of ocean chlorophyll concentration along the northwestern coast of Taiwan is obvious, where the average chlorophyll concentration from November to January exceeds 2.0 mg/m3, and the lowest concentration in spring is 1.4 mg/m3. It is apparent that the tidal currents in the north of Taiwan flow eastward and westward during ebb and flood periods, respectively. Affected by the background currents, the flow velocity exhibits significant seasonal changes, namely, 0.43 m/s in summer and 0.27 m/s in winter during the ebb period and is 0.26 m/s in summer and 0.45 m/s in winter during the flood period. The chlorophyll concentration near the shore is also significantly affected by the tidal currents. Based on CODAR data, virtual drifter experiments, and GOCI data, this research provides novel and important knowledge of ocean current movement process in the north of Taiwan and indicates diurnal to seasonal variations in the ocean chlorophyll concentration, facilitating future research on the interaction between the TS, ECS, and Kuroshio.

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.

Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans

With a revisit time of 1 h, spatial resolution of 500 m, and high radiometric sensitivity, the Geostationary Ocean Color Imager (GOCI) is widely used to monitor diurnal dynamics of oceanic phenomena. However, atmospheric correction (AC) of GOCI data with high solar zenith angle (>70°) is still a challenge for traditional algorithms. Here, we propose a novel neural network (NN) AC algorithm for GOCI data under high solar zenith angles. Unlike traditional NN AC algorithms trained by radiative transfer-simulated dataset, our new AC algorithm was trained by a large number of matchups between GOCI-observed Rayleigh-corrected radiance in the morning and evening and GOCI-retrieved high-quality noontime remote-sensing reflectance (Rrs). When validated using hourly GOCI data, the new NN AC algorithm yielded diurnally stable Rrs in open ocean waters from the morning to evening. Furthermore, when validated by in-situ data from three Aerosol Robotic Network-Ocean Color (AERONET-OC) stations (Socheongcho, Gageocho and Ieodo), the GOCI-retrieved Rrs at visible bands obtained using the new AC algorithm agreed well with the in-situ values, even under high solar zenith angles. Practical application of the new algorithm was further examined using diurnal GOCI observation data acquired in clear open ocean waters. Results showed that the new algorithm successfully retrieved Rrs for the morning and evening GOCI data. Moreover, the amount of Rrs data retrieved by the new algorithm was much higher than that retrieved by the standard AC algorithm in SeaDAS. Our proposed NN AC algorithm can not only be applied to process GOCI data acquired in the morning and evening, but also has the potential to be applied to process polar-orbiting satellite ocean color data at high-latitude ocean that also include satellite observation with high solar zenith angles.

Estimation of ground-level dry PM2.5 concentrations at 3 km resolution over Beijing using Geostationary Ocean Colour Imager

ABSTRACT In this study, aerosol optical depth (AOD) and fine-mode fraction (FMF) with a 3 km resolution are retrieved from Geostationary Ocean Colour Imager (GOCI) data using a multi-temporal method. The retrieved results are input into a physical model for estimating the fine particulate matter (PM2.5) based on instantaneous satellite-based measurements. The other two input parameters of the model are planetary boundary layer height (PBLH) and atmospheric relative humidity (RH), which are simulated by the Weather Research and Forecasting (WRF) model and have the same spatial resolution as AOD and FMF. For better time-matching, the time of the simulated PBLH and RH is almost the same as that of the retrieved AOD and FMF, and is selected when uniform mixing of aerosols occurs within boundary layer in the afternoon. Finally, the ground-level dry PM2.5 concentrations at a 3 km resolution are estimated over Beijing. The GOCI-estimated PM2.5 results are validated by in situ measurements in the study area. The means of GOCI-estimated and in situ results are very close (131.2 μg m−3 versus 123.0 μg m−3) and the correlation coefficient is about 0.84 with a linear slope of 0.81 and intercept of 31.1 μg m−3, which shows good performance of GOCI in air-quality monitoring.

Dynamic Diurnal Changes in Green Algae Biomass in the Southern Yellow Sea Based on GOCI Images

Macroalgae blooms of Ulva prolifera have occurred every summer in the southern Yellow Sea since 2007, inducing severe ecological problems and huge economic losses. Genesis and secular movement of green algae blooms have been well monitored by using remote sensing and other methods. In this study, green algae were detected and traced by using Geostationary Ocean Color Imager (GOCI), and a novel biomass estimation model was developed from the relationship between biomass measurements and previously published satellite-derived biomass indexes. The results show that the green algae biomass can be determined most accurately with the biomass index of green algae for GOCI (BIGAG), which is calculated from the Rsurf data that had been atmospherically corrected by ENVI/QUAC method. For the first time, dynamic changes in green algae biomass were studied over an hourly scale. Short-term biomass changes were highly influenced by Photosynthetically Available Radiation (PAR) and tidal phases, but less by sea surface temperature variations on a daily timescale. A new parameter of biomass changes (PBC), calculated by the ratio of the biomass growth rate to movement velocity, could provide an effective way to assess and forecast green tide in the southern Yellow Sea and similar areas.

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.

Tidal variation of total suspended solids over the Yangtze Bank based on the geostationary ocean color imager

Hourly mapping by a Geostationary Ocean Color Imager was used to reveal the spatial pattern and tidal variation of total suspended solids (TSS) over the Yangtze Bank in the Yellow and East China Seas during the winter. The TSS form a tongue-shaped structure, which decreases further offshore in a stepwise manner. The stepwise change is separated by two fronts of TSS, which are located near the 20-m and 50-m isobaths. The tidal variation of TSS concentration during the study period is evident and can be divided into three stages: decay, maintenance, and growth. Compared with the relatively stationary TSS during the maintenance stage, drastic changes exist during the decay and growth stages. In terms of tide-induced mixing, the dynamic analysis shows that both the topography and the tidal currents play an important role in the spatio-temporal variation of TSS during the tidal period. In particular, spatial distribution is primarily determined by the topography, whereas the temporal variations in tidal scale are determined by the tidal currents.

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.