Geostationary Ocean Color Imager Satellite Research Articles

Short‐Term Sea Fog Area Forecast: A New Data Set and Deep Learning Approach

AbstractPrompt and precise forecast of sea fog regions ensures maritime navigational safety. This paper establishes a prompt and precise forecast of sea fog regions that ensure maritime navigational safety. This paper establishes a multivariable sea fog forecast (MV‐SFF) data set and proposes a deep learning‐based forecast method named rich‐element aggregated (REA) for short‐term forecasts. The MV‐SFF data set contains 122 sea fog events from 2010 to 2020. Each event in the MV‐SFF data set includes meteorological variables from reanalysis data and geostationary ocean color imager satellite images captured hourly on the day of the sea fog occurrence. This data set aims to comprehensively utilize meteorological elements and remote sensing observations to predict the spatial changes of sea fog areas in the next 7 hours. The proposed REA model can extract and integrate features from historical meteorological elements of different types, times, and spatial locations. In addition, REA utilizes a position‐aware edge detection mechanism to locate the exact position of sea fog. We perform a seven‐hour ahead forecast starting from 09:16 local time and show promising forecasting results, which demonstrate the fog area forecast ability of our model. Compared with existing advanced deep learning networks, the REA model is superior in performance and stability.

Horizontal and vertical migration of cyanobacterial blooms in two eutrophic lakes observed from the GOCI satellite

Under the variations of natural conditions (temperature, wind speed, light, et al.) and self-regulation of buoyancy, cyanobacterial blooms can change rapidly in a short time. The Geostationary Ocean Color Imager (GOCI) can provide hourly monitoring of the dynamics of algal blooms (eight times per day), and has potential in observing the horizontal and vertical movement of cyanobacterial blooms. Based on the fractional floating algae cover (FAC), the diurnal dynamics and migration of floating algal blooms were evaluated, and the horizontal and vertical migration speed of phytoplankton was estimated from the proposed algorithm in two eutrophic lakes, Lake Taihu and Lake Chaohu in China. The locations, number, and area of algal bloom patches showed the hotspots and horizontal movement of bloom patches. The spatial and seasonal variations of the vertical velocities indicated that both the rising and sinking speed were higher in summer and autumn than those in spring and winter. The factors affecting diurnal horizontal and vertical migrations of phytoplankton were analyzed. Diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and temperature had significant positive relationships with FAC in the morning. Wind speed contributed 18.3 and 15.1% to the horizontal movement speed in Lake Taihu and Lake Chaohu, respectively. The rising speed was more related to DNI and DHI in Lake Taihu and Lake Chaohu with contribution of 18.1 and 16.6%. The horizontal and vertical movement of algae provide important information for understanding phytoplankton dynamics and the prediction and warning of algal blooms in lake management.

The Ocean Surface Current in the East China Sea Computed by the Geostationary Ocean Color Imager Satellite

High-frequency observations of surface current field data over large areas and long time series are imperative for comprehending sea-air interaction and ocean dynamics. Nonetheless, neither in situ observations nor polar-orbiting satellites can fulfill the requirements necessary for such observations. In recent years, geostationary satellite data with ultra-high temporal resolution have been increasingly utilized for the computation of surface flow fields. In this paper, the surface flow field in the East China Sea is estimated using maximum cross-correlation, which is the most widely used flow field computation algorithm, based on the total suspended solids (TSS) data acquired from the Geostationary Ocean Color Imager satellite. The inversion results were compared with the modeled tidal current data and the measured tidal elevation data for verification. The results of the verification demonstrated that the mean deviation of the long semiaxis of the tidal ellipse of the inverted M2 tide is 0.0335 m/s, the mean deviation of the short semiaxis is 0.0276 m/s, and the mean deviation of the tilt angle is 6.89°. Moreover, the spatially averaged flow velocity corresponds with the observed pattern of tidal elevation changes, thus showcasing the field’s significant reliability. Afterward, we calculated the sea surface current fields in the East China Sea for the years 2013 to 2019 and created distribution maps for both climatology and seasonality. The resulting current charts provide an intuitive display of the spatial structure and seasonal variations in the East China Sea circulation. Lastly, we performed a diagnostic analysis on the surface TSS variation mechanism in the frontal zone along the Zhejiang coast, utilizing inverted flow data collected on 3 August 2013, which had a high spatial coverage and complete time series. Our analysis revealed that the intraday variation in TSS in the local surface layer was primarily influenced by tide-induced vertical mixing. The research findings of this article not only provide valuable data support for the study of local ocean dynamics but also verify the reliability of short-period surface flow inversion of high-turbidity waters near the coast using geostationary satellites.

Mapping the Changjiang Diluted Water in the East China Sea during summer over a 10-year period using GOCI satellite sensor data

The Changjiang River loads freshwater into the East China Sea (ECS) and the Yellow Sea (YS). A low-salinity plume known as Changjiang Diluted Water (CDW) is formed by mixing with saline ambient waters; it disperses toward Jeju Island and the East/Japan Sea due to the dominant southerly wind and tide during summer. To map the offshore surface CDW in the ECS, a proxy was developed using surface water beam attenuation (c) and salinity from 18 annual summer cruises matched with synchronous Geostationary Ocean Color Imager (GOCI) satellite data. We followed a two-step empirical approach. First, a relationship between in situ salinity and c was obtained from hydrographic cruises (1998, 2003–2018, and 2021). Second, in situ c was matched with GOCI remote-sensing reflectance (Rrs) band ratios of all available blue-to-green wavelengths (2011–2018). Finally, satellite-derived surface salinity was determined directly by combining the two empirical relationships, providing a robust estimate over a range of salinities (22–34 psu). Our algorithm was then compared and validated with five previous satellite-derived salinity algorithms based on colored dissolved organic matter and particle concentrations. In the first step, surface salinity was matched and well correlated with c using an 18-year full conductivity–temperature–depth (CTD) dataset (N = 1,345, R2 = 0.93). In the second step, c was synchronously matched and well correlated with GOCI Rrs band ratios using an 8-year summer CTD dataset (N = 391, R2 = 0.93). The satellite-derived surface salinity based on the GOCI was compared with five other approaches and validated using an 8-year summer CTD dataset (2011–2018, N = 707, RMSE = 0.43, bias = 0.053, mean ratio = 1.002). We mapped satellite-derived surface salinity using monthly GOCI images during the summer from 2011 to 2020. The spatial patterns of the CDW were connected to the Changjiang River mouth and extended to the east–northeast during summer. Saline water was confined to the warm current and the upper slope in the eastern part of the study area. Anomalous dispersion of the CDW occurred in August 2016, 2017, and 2020, and limited dispersion occurred in August 2014 and 2019.

The spatial data of paddy rice classification over Northeast Asia using COMS geostationary satellite

The Korea Aerospace Research Institute (KARI) estimated paddy rice classification maps over Northeast Asia using the Cheonian geostationary orbiting satellite (COMS: Communication, Ocean and Meteorological Satellite) data. In the case of classification map of rice paddy, it is not only used as input data for estimating rice yield, but also for various fields such as agriculture, weather, climate change, bio energy, and ecology. The spatial resolution of the classified rice map is 500 m, and the classification map was estimated yearly temporal resolution from 2011 to 2017. The spatial coverage of the classification map was the Northeast Asia with the latitude 25 ° N ~ 47 ° N and the longitude 115 ° E ~ 145 ° E as shown in Fig. 1 including Heilongjiang Sheng, Jilin Sheng, and Liaoning Sheng. In this classification map of paddy rice, it was calculated by applying geostationary orbiting satellites based on value-added products from Geostationary Ocean Color Imager (GOCI). In this study, we additionally used MODIS Land Surface Water Indices (LSWI) to support rice classification by considering the physical characteristics of rice cultivation area in the transplanting season. Basically, the radiance value of the top of atmosphere (TOP) observed in GOCI satellite was corrected to the surface reflectance at the top of the canopy through radiative transfer model. After that, NDVI, which can reflect the time series growth characteristics of rice, was estimated first. In addition, the MODIS LSWI index was used to determine the rice cultivation area with the NDVI in Northeast Asia by reflecting the water characteristics of the rice cultivation area during the transplanting period. More details of validation results for this algorithm can be found in previous studies.

A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bp → cp → j, bbp and bp → Bp, and j and Bp → np. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate np in other coastal optically complex water bodies.

Diurnal variation of aerosol optical depth and PM<sub>2.5</sub> in South Korea: a synthesis from AERONET, satellite (GOCI), KORUS-AQ observation, and the WRF-Chem model

Abstract. Spatial distribution of diurnal variations of aerosol properties in South Korea, both long term and short term, is studied by using 9 AERONET (AErosol RObotic NETwork) sites from 1999 to 2017 and an additional 10 sites during the KORUS-AQ (Korea–United States Air Quality) field campaign in May and June of 2016. The extent to which the WRF-Chem (Weather Research and Forecasting coupled with Chemistry) model and the GOCI (Geostationary Ocean Color Imager) satellite retrieval can describe these variations is also analyzed. On a daily average, aerosol optical depth (AOD) at 550 nm is 0.386 and shows a diurnal variation of 20 to −30 % in inland sites, which is larger than the AOD of 0.308 and diurnal variation of ±20 % seen in coastal sites. For all the inland and coastal sites, AERONET, GOCI, and WRF-Chem, and observed PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) data generally show dual peaks for both AOD and PM2.5, one in the morning (often at ∼08:00–10:00 KST, Korea Standard Time, especially for PM2.5) and another in the early afternoon (∼14:00 KST, albeit for PM2.5 this peak is smaller and sometimes insignificant). In contrast, Ångström exponent values in all sites are between 1.2 and 1.4 with the exception of the inland rural sites having smaller values near 1.0 during the early morning hours. All inland sites experience a pronounced increase in the Ångström exponent from morning to evening, reflecting an overall decrease in particle size in daytime. To statistically obtain the climatology of diurnal variation of AOD, a minimum requirement of ∼2 years of observation is needed in coastal rural sites, twice as long as that required for the urban sites, which suggests that the diurnal variation of AOD in an urban setting is more distinct and persistent. While Korean GOCI satellite retrievals are able to consistently capture the diurnal variation of AOD (although it has a systematically low bias of 0.04 on average and up to 0.09 in later afternoon hours), WRF-Chem clearly has a deficiency in describing the relative change of peaks and variations between the morning and afternoon, suggesting further studies for the diurnal profile of emissions. Furthermore, the ratio between PM2.5 and AOD in WRF-Chem is persistently larger than the observed counterparts by 30 %–50 % in different sites, but spatially no consistent diurnal variation pattern of this ratio can be found. Overall, the relatively small diurnal variation of PM2.5 is in high contrast with large AOD diurnal variation, which suggests the large diurnal variation of AOD–PM2.5 relationships (with the PM2.5 ∕ AOD ratio being largest in the early morning, decreasing around noon, and increasing in late afternoon) and, therefore, the need to use AOD from geostationary satellites to constrain either modeling or estimate of surface PM2.5 for air quality application.

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity eight times a day and is equipped with spectral bands suitable for monitoring crop yield in the national scale with a spatial resolution of 500 m. The objectives of this study were to classify nationwide paddy fields and to project rice (Oryza sativa) yield and production using the grid-based GRAMI-rice model and GOCI satellite products over South Korea from 2011 to 2014. Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction systems for model inputs, respectively. The paddy fields and transplanting dates were estimated by using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products. The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea. The overall accuracies of the estimated paddy fields using MODIS data ranged from 89.5% to 90.2%. The simulated rice yields statistically agreed with the observed yields with mean errors of −0.07 to +0.10 ton ha−1, root-mean-square errors of 0.219 to 0.451 ton ha−1, and Nash–Sutcliffe efficiencies of 0.241 to 0.733 in four years, respectively. According to paired t-tests (α = 0.05), the simulated and observed rice yields were not significantly different. These results demonstrate the possible development of a crop information delivery system that can classify land cover, simulate crop yield, and monitor regional crop production on a national scale.

Episodic surface intrusions in the Yellow Sea during relaxation of northerly winds

AbstractThe surface currents over the Yellow and East China Seas are mapped from the Geostationary Ocean Color Imager (GOCI). Based on a composite of six intrusion events in January–April, the strong northward surface current in the Yellow Sea is shown to be concentrated along the deep trough, accompanied by a broad northward surface current over the East China Sea. From the corresponding surface winds, the episodic northward surface flow bursts appear to be associated with abrupt changes from the strong northerly winds to weak southerly winds during cold front passages. A three‐dimensional model driven with observed surface winds is used to simulate the observed shelf‐wide response to northerly winds. There is an outstanding agreement between the simulated and observed surface currents. The surface intrusion in the Yellow Sea is shown to be driven primarily by a barotropic longitudinal surface slope, while the strong northward current in the East China Sea is associated with a coastal trapped wave. Moreover, the surface intrusion is associated with a large volume transport, suggesting that the transient intrusions could be important in the northward heat transport. The unprecedented capability of GOCI satellite in providing a regional circulation pattern, in conjunction with complementary model simulations, could contribute greatly to understanding of the dynamics of the Yellow and East China Seas.

Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations

In an image from the Geostationary Ocean Color Imager (GOCI) taken on 13 June 2011, a recurrent floating green algae bloom was detected around Qingdao. To detect the bloom an index based on multi-spectral band ratios using three wavelengths (555, 660, 745nm) was utilized. One month later, the GOCI images detected widespread floating green algae patches (east–west elongated) across the Yellow Sea and East China Sea (YS, ECS). The presence of the patches was confirmed from various oceanic observation cruises.Lagrangian particle tracking experiments were conducted to understand the pathway of the floating green algae patches and interpret the physical forcing factors that affect the distribution and advection of the floating green algae. The numerical simulation results indicated that dominant southerly winds during June and July 2011 were related to offshore movement of the floating green algae, especially their eastward extension in the YS/ECS. An infrequent and unusual event occurred in June 2011: a typhoon MEARI, caused the green algae to detach from the coast and initiated movement to the east. After the typhoon event, sea surface temperature recovered rapidly enough to grow the floating green algae, and wind and local current controlled the movement of the massive floating algae patches (coastal accumulation or offshore advection in the YS/ECS). Analysis of the floating green algae pixels' movement during passage of Typhoon MAON in July 2011 revealed that the floating green algae patches were significantly controlled by both ocean currents and enhanced winds. These findings suggest that the floating green algae bloom off Qingdao and in the middle of the YS and ECS in the summer of 2011 occurred due to the combined effects of recent rapid expansion of seaweed aquaculture, strong winds, and the wind patterns in blooming regions. Our combined approach, using satellite data and numerical simulations, provides a robust estimate for tracing and monitoring changes in green algae blooms on a regional scale.

타일드 디스플레이에서의 천리안 해양관측 위성영상을 위한 다중 입력 장치 및 멀티 스케일 인터랙션 설계 및 구현

본 논문은 타일드 디스플레이에서 여러 사용자들이 천리안 해양관측 위성영상을 관측 및 분석하기 위하여 다중 입력 장치를 활용한 멀티스케일 인터랙션 기반의 가시화 시스템을 설명한다. 이 시스템은 멀티터치 스크린, 키넥트 동작 인식, 모바일 인터페이스를 제공하여 여러 사용자들이 타일드 디스플레이 화면에 근접하거나 원거리에서 다양한 인터랙션을 통하여 한반도 중심의 해양 환경 및 기후 변화 정보를 효과적으로 관측할 수 있다. 천리안 해양관측 위성영상은 고화질의 메모리양이 많아서 원본 영상을 작은 영상으로 분할한 멀티레벨 이미지 로드 기법을 사용하여 시스템의 부하를 줄이면서 사용자의 다양한 조작에서 타일드 디스플레이 화면에 매끄럽게 출력되도록 하였다. 이 시스템은 다중 사용자의 키넥트 제스처와 터치 포인트 입력 및 모바일 장치로 부터 입력된 정보를 일반화 처리하여 타일드 디스플레이 응용프로그램에 공통적으로 활용 가능한 다양한 인터랙션을 지원하였다. 또한 특정 날짜에 해당하는 시간 단위의 해양관측 위성 영상이 순차적으로 화면에 출력되며, 여러 사용자들이 동시에 위성영상을 자유롭게 확대 축소하고 상하좌우로 이동하며 다양한 기능의 버튼을 누르는 등의 인터랙션을 할 수 있도록 하였다. This paper describes a multi-scale user interaction based tiled display visualization system using multiple input devices for monitoring and analyzing Geostationary Ocean Color Imager (GOCI) observation satellite imagery. This system provides multi-touch screen, Kinect motion sensing, and moblie interface for multiple users to control the satellite imagery either in front of the tiled display screen or far away from a distance to view marine environmental or climate changes around Korean peninsular more effectively. Due to a large amount of memory required for loading high-resolution GOCI satellite images, we employed the multi-level image load technique where the image was divided into small tiled images in order to reduce the load on the system and to be operated smoothly by user manipulation. This system performs the abstraction of common input information from multi-user Kinect motion and gestures, multi-touch points and mobile interaction information to enable a variety of user interactions for any tiled display application. In addition, the unit of time corresponding to the selected date of the satellite images are sequentially displayed on the screen and multiple users can zoom-in/out, move the imagery and select buttons to trigger functions.

Machine learning approaches to coastal water quality monitoring using GOCI satellite data

Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R2 of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m3 (40.7%) for chl-a, and calibration R2 of 0.98 and CV RMSE of 11.42 g/m3 (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.

Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data

The objectives of this study are to: (i) compare the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS) and the Geostationary Ocean Color Imager (GOCI) in retrieving the particulate backscattering coefficient, robust proxy of the concentration of suspended particulate matter (SPM), in the turbid waters of the East China Sea, then (ii) combine ocean color satellite data to field data recorded by an autonomous bio-optical profiling float to reconstitute and explain the dynamics of SPM within the whole water column in the middle of the East China Sea. MODIS, MERIS and GOCI seawater reflectance products at green, red and near-infrared (NIR) wavelengths are generated using atmospheric correction algorithms designed for turbid coastal waters then compared. Good agreement is obtained in the green and red while significant differences are observed in the NIR. The standard GOCI algorithm and a new one based on a regional empirical relationship established using MODIS satellite data are both proved to perform well over the whole range of water turbidity. Finally the dynamics of SPM is reconstituted along a three-month period to highlight a steady-state and stratified situation before the overpass of a large typhoon which mixed the water column, destroyed the deep-chlorophyll maximum and enhanced resuspension of bottom sediments varying according to tidal cycles.

Application of GOCI Satellite Data to Ocean Modeling

Kim, C.S., Park, Y.J., Park, K.S., Shim, J.S. and Lim, H.S., 2013. Application of GOCI Satellite Data to Ocean ModelingThis study demonstrates possible application of satellite data to numerical modeling, and vice versa, to improve the quality of the processed information. A new geostationary satellite with the three missions of communication, ocean and meteorology was launched in June 2010. The ocean mission part, GOCI (The geostationary Ocean Color Imager), is now in data service of ocean color images at every hour during daytime with a spatial resolution of 500 m. The scanned data covers the north Pacific with focusing on Korean Peninsula and adjacent seas. Using the optical sensors, the GOCI images usually contain the cloud-blocked zone. We investigate a method to acquire the cloud-free images of environment information by using the operational 3-D ocean model, the ROMS. The clear-image data are used as boundary condition to the numerical modeling, and then the model results are used to recover the cloud-covered area. The hourly varying image data are also excellent data for the ocean numerical modeling in terms of temporal and spatial variation of the ocean environment. By close examination of hourly producing images from GOCI, the temporal variation is very useful for operational purpose along with the three-dimensional ocean model such as ROMS. Being in operational mode, the ROMS ocean model produces temporal and structural variation of coastal features by using the compatible GOCI data, or the GOCI images can be improved by using the model results for cloud-free images. This study shows some excellent test cases on various coastal phenomena, such as a river plume of highly turbid waters, coastal upwelling, transport of algal bloom, typhoon tracking and the distribution of surface suspended sediment concentration. A method for the convergence of GOCI data and ocean model results has been introduced. The clouding network system for the convergence of satellite data and ocean model data is a promising method that combines two different media, thus yielding dynamically validated products.