Geostationary Ocean Color Imager Research Articles

Geometric Correction for the Geostationary Ocean Color Imager from a Combination of Shoreline Matching and Frequency Matching.

Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth’s rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines.

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.

Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images.

Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain economic damage. Plenty of threshold-based index methods have been developed to detect surface algae pixels in various ocean color data with different manners; however, these methods cannot be used for every satellite sensor because of the spectral band configuration. Also, these traditional methods generally require other reliable indicators, and even visual inspection, in order to achieve an acceptable mapping of MAB that appears under diverse environmental conditions (cloud, aerosol, and sun glint). To overcome these drawbacks, a machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery. The method consists of two MLP models, which consider both spectral and spatial features of Rayleigh-corrected reflectance (Rrc) maps. Accuracy assessment and performance comparison showed that the proposed method has the capability to provide prediction maps of MAB with high accuracy (F1-score approaching 90% or more), and with more robustness than the traditional methods. Most importantly, the model is practically adaptable for other ocean color instruments. This allows customized models to be built and used for monitoring MAB in any regional areas. With the development of machine learning models, long-term mapping of MAB in global ocean is conducive to promoting the associated studies.

COMPARISON OF GEOMETRIC CORRECTION SCHEMES FOR GEOSTATIONARY OCEAN COLOR IMAGER SLOTS WITHOUT GCPS

Abstract. Currently, the geometric correction process of GOCI (Geostationary Ocean Color Imager) image is performed by matching slot images against shorelines and utilizing the matching results as GCPs (Ground Control Point). However, there are several GOCI slots without shorelines and for such slots acquiring GCPs is not easy. The purpose of this paper is to compare several alternative geometric correction schemes applicable to the slots without GCPs. We analyzed three schemes. The first scheme is to apply the correction angle of the same slot in the most recent dataset. The second scheme is to apply correction angle of the previous slot in the current dataset. And the last scheme is to apply correction angle of the slot with the largest number of GCPs in the same time dataset. Overall process for comparing the quality of the three geometric correction schemes consisted of the following steps. Firstly, using ephemeris metadata of GOCI Level 1A, we established initial sensor model, which defines geometry relationship between ground coordinate system and image coordinate system of a GOCI image. And then, by matching edge detected from GOCI slot images and shoreline landmark chips, we obtained GCPs. Using these GCPs, we calculated correction angle of each slot. After then, through the three schemes, we conducted precision sensor modeling. Among three schemes, geometric correction applying the previous slot correction angle showed the best quality. The average RMSE of this scheme was about 1.4 km, which was quite close to geometric correction quality applying correction angles from GCPs.

Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index

This work focuses on the detection of tiny macroalgae patches in the eastern parts of the Yellow Sea (YS) using high-resolution Landsat-8 images from 2014 to 2017. In the comparison between floating algae index (FAI) and normalized difference vegetation index (NDVI) better detection by FAI was observed, but many tiny patches still remained undetected. By applying a modification on the FAI around 12% to 27% increased and correct detection of macroalgae is achieved from 35 images compared to the original. Through this method many scattered tiny patches were detected in June or July in Korea Bay and Gyeonggi Bay. Though it was a small-scale phenomenon they occurred in the similar period of macroalgal bloom occurrence in the YS. Thus, by using this modified method we could detect macroalgae in the study areas around one month earlier than the previously used Geostationary Ocean Color Imager NDVI-based detection. Later, more macroalgae patches including smaller ones occupying increased areas were detected. Thus, it seems that those macroalgae started growing locally from tiny patches rather than being transported from the western parts of the YS. Therefore, this modified FAI could be used for the precise detection of macroalgae.

A study of the environmental factors influencing the growth phases of Ulva prolifera in the southern Yellow Sea, China

Macroalgal blooms of U. prolifera in the southern Yellow Sea (SYS) have become an ecological disaster. In this study, we attempt to explore the influence of environmental factors and human activity on the annual development of U. prolifera during 2011–2016, combining geostationary ocean color imager images and monitored environmental factors. Blooms of U. prolifera were mainly distributed in the central SYS, drifting from the southern Yellow Sea Radial Sand Ridges. Three growth phases were defined (initial phase, outbreak phase and dissipation phase) to facilitate analysis of the relationship between the environmental factors and growth conditions of U. prolifera. Seaweed cultivation, sea surface temperature (SST) and available radiation influenced the algal biomass during the initial phase; the seawater transparency, precipitation and wind activity affected the algae during the outbreak phase; and SST, intense radiation and cleanup operations increased the rate of algal extinction in the dissipation phase.

Atmospheric correction of geostationary Himawari-8 satellite data for Total Suspended Sediment mapping: A case study in the Coastal Waters of Western Australia

Recent studies in the application of geostationary satellite sensors, such as the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the European Union’s meteorological satellite, and the Geostationary Ocean Color Imager (GOCI) from South Korea, to monitor the Total Suspended Sediment (TSS) in coastal waters have shown that the higher temporal dynamics in the coastal processes can be better achieved with high temporal resolution geostationary satellite sensors. The availability of the geostationary satellite, Himawari-8 carrying the Advanced Himawari Imager (AHI) sensor with visible, Near InfraRed (NIR) and Shortwave InfraRed (SWIR) bands over the coastal waters of Australia has prompted this study to test the feasibility of AHI in mapping TSS concentration. In this study, we present an atmospheric correction method for AHI data using two different aerosol correction methods, a combination of two SWIR bands, and a combination of one NIR and one SWIR band. The outcome from this study indicated that the atmospherically corrected AHI data at 10 min temporal resolution can be used to discern TSS concentrations greater than ∼0.15 mg/L when solar zenith angles (θ0) are less than 60°. The result of the cross-validation of AHI derived TSS concentration, with MODIS-Aqua and Lansat-8 Operational Land Imager (OLI) derived TSS concentration showed good correlation, with correlation coefficients r = 0.71 and r = 0.91, respectively.

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.

Evaluation of the sensitivity of China’s next-generation ocean satellite sensor MWI onboard the Tiangong-2 space lab over inland waters

The Moderate-resolution Wide-wavelengths Imager (MWI) is the next-generation ocean-color sensor independently designed by China and was successfully launched on September 15, 2016 onboard the Tiangong-2 (TG-2) Space Lab. It is currently urgent to assess the capability and data quality of the MWI sensor to determine its future applications. In this study, we first assessed the signal-to-noise ratio (SNR) and the radiometric sensitivity of the MWI. With this goal, we chose the third largest freshwater lake in China, Lake Taihu, as our test area and assessed the capability of the MWI to monitor the turbid inland waters by comparing its consistency with that of Moderate Resolution Imaging Spectroradiometer (MODIS) data and Geostationary Ocean Color Imager (GOCI) data. Our results show that (1) the SNR values of the MWI are greater than 400 in the visible band and are sufficient to effectively avoid ocean-color data errors caused by the sensor noise; however, the SNR values of the MWI are below 200 in the near-infrared (NIR) band and are lower than the theoretical expectation for the design of ocean-color sensors; (2) on the one hand, the MWI-derived remote sensing reflectance (Rrs) data have large errors in the blue band (R2 < 0.20, MAPE < 50%) but reduced errors in the red and NIR bands with a high correlation (R2 > 0.90, MAPE < 10%) compared with in situ Rrs; on the other hand, the MWI-derived Rrs data are generally consistent with the MODIS- and GOCI-derived Rrs data (R2 > 0.90, MAPE < 10%); and (3) the MWI-derived concentration distributions of cyanobacterial algal blooms and suspended particulate matter (SPM) in Lake Taihu exhibit high spatial resolutions and good accuracies and are in good agreement with the corresponding distributions yielded by the MODIS and GOCI satellites (R2 > 0.90, MAPE < 10%). However, the MWI cannot detect signals at the important 700–710 bands and consequently has a relatively weak capability to detect chlorophyll-a (Chla) concentrations in inland waters. In summary, as the Chinese next-generation ocean-color sensor, the overall performance metrics of the MWI approach the present international level. Meanwhile, we suggest that the band of the MWI can be further optimized to improve the derived SNR values in the near-infrared band in order to satisfy the requirements of satellite observations over inland waters.

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...

3-D Simulation of the Suspended Sediment Transport in the Jiao jiang Estuary: Based on Validating by Remote Sensing Retrieval

Lu, C.Z.; Li, H.; Dai, W.Q.; Tao, J.F.; Xu, F.; Cybele, S.; Zhang, X.Y., and Guo, H.T., 2018. 3-D Simulation of the Suspended Sediment Transport in the Jiaojiang Estuary: Based on Validating by Remote Sensing Retrieval. 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. 116–120. Coconut Creek (Florida), ISSN 0749-0208.Suspended sediment concentration (SSC) in Jiaojiang Estuary is characterized by high concentration and significant diurnal dynamics. The large-scale reclamation will influence the sediment transport in Jiaojiang Estuary. At present, ship-borne data, remote sensing observations, and numerical modeling technique are the main technological methods to acquire the distributions of SSC. However, every method has its own limitations in application. Conventional ship-borne survey requires a lot of manpower and resources to obtain sediment distribution of an estuary although its accuracy is high. The cloudy and rainy environment has been the bottle-neck for monitoring the land and ocean surface. With the influence of land-use by human activities (such as tidal flats reclamation) being strengthened gradually, previous numerical models are not suitable for the new environment. This paper aims to combine numerical modeling technique with remote sensing observations to acquire a higher-resolution surface suspended sediment field in time and space. MIKE3 3D model was used to simulate the suspended sediments and it is composed of hydrodynamic module and mud transport module. The surface-layer sediment distribution was calculated using suspended sediment dynamic mathematical model in MIKE 3. Based on Geostationary Ocean Color Imager (GOCI) and the simultaneous surface layer sediment concentration data, a remote sensing model was developed to retrieve the distribution of SSC in the Jiaojiang Estuary. The degree of matching between the remotely sensed SSC and the numerically simulated SSC is up to 72%, both of which reveal temporal-spatial variation of the SSC. The research provides a reference for real-time monitoring and forecasting the dynamic variation of the SSC.

Comparison of Internal Waves in Various Ocean Fields Around the Korean Peninsula

Kim, H.; Son, Y.B.; Jeong, J.-Y., and Jo, Y.-H., 2018. Comparison of Internal Waves in Various Ocean Fields around the Korean Peninsula. 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. 466–470. Coconut Creek (Florida), ISSN 0749-0208.The sea surrounding the Korean Peninsula is roughly divided into the western part (the Yellow Sea (YS) and the South Sea (SS); shallow inland sea) and the eastern part (the East Sea (ES); drastically deep). While internal wave (IW) studies have been mainly localized because of the complicated environmental conditions around the Korean Peninsula, this study determined the differences of the IWs in various ocean fields for over three years (2013–2015). First, satellite measurements with high spatial and temporal resolutions from Landsat 8 and the Geostationary Ocean Color Imager (GOCI) were used. The curvature analysis was used to define and compare the shapes of the objective factors of the IWs, such as sizes, ovalities, and maximum wavelengths. In the YS, IWs appeared at a densely spaced interval compared with the ES and the maximum wavelength of the packet was nonlinearly increased with depth, and that of the YS (1.1 km) was about 10 times larger than and ES (10.6 km). In addition, the standard deviations of the wavelengths in a packet, which can determine the irregularity of the within-packet spacings from front to rear; were 0.2 and 1.4 for the YS and ES, respectively. Second, the ocean temperature and salinity data from HYCOM and Korteweg-de Vries equation were used to calculate the theoretical phase speed of IWs. Typically, the IW phase speed increased nonlinearly with the water depth; and the mean value was approximately 0.1–0.7 m/s and 0.9–1.7 m/s for the YS and ES, respectively.

In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

AbstractPhytoplankton size classes (PSCs) is of great significance for exploring marine ecological and biogeochemical processes. Remote sensing of PSCs has been successfully applied to open oceans; however, it is still quite limited for optically complex coastal oceans. In this study, the entire continental shelf sea of China including Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) characterized by distinctive turbid waters and impacted by plumes of large world‐class river (the Changjiang River) was taken as an example of turbid coastal ocean for remotely sensed spatial‐temporal distributions of PSCs. In situ data were collected from cruises during April to June in 2014 and an improved algorithm for PSCs retrieval was proposed. PSCs derived from GOCI (Geostationary Ocean Color Imager) images revealed that microplankton was dominant in the BS, the YS, and the nearshore ECS and nanoplankton distributed widely in the entire study area, while picoplankton mainly distributed in the offshore ECS in April, which was consistent with in situ investigation and related to environmental factors. Validation indicated that the improved algorithm provided a more accurate estimation of PSCs, with the root mean square error (RMSE) between estimated and measured size‐fractionated concentrations been 0.774, 0.257, and 0.142 mg m−3 for micro, nano, and picoplankton, respectively. Diurnal variations of PSCs were mainly affected by tidal currents and light intensity depending on different water types. These illustrated that remote sensed spatial distributions as well as diurnal variations of PSCs are effective in turbid continental shelf seas of China.

Effects of Wave–Current Interactions on Suspended-Sediment Dynamics during Strong Wave Events in Jiaozhou Bay, Qingdao, China

AbstractWave–current interactions are crucial to suspended-sediment dynamics, but the roles of the associated physical mechanisms, the depth-dependent wave radiation stress, Stokes drift velocity, vertical transfer of wave-generated pressure transfer to the mean momentum equation (form drag), wave dissipation as a source term in the turbulence kinetic energy equation, and mean current advection and refraction of wave energy, have not yet been fully understood. Therefore, in this study, a computationally fast wave model developed by Mellor et al., a Finite Volume Coastal Ocean Model (FVCOM) hydrodynamics model, and the sediment model developed by the University of New South Wales are two-way coupled to study the effect of each wave–current interaction mechanism on suspended-sediment dynamics near shore during strong wave events in a tidally dominated and semiclosed bay, Jiaozhou Bay, as a case study. Comparison of Geostationary Ocean Color Imager data and model results demonstrates that the inclusion of just the combined wave–current bottom stress in the model, as done in most previous studies, is clearly far from adequate to model accurately the suspended-sediment dynamics. The effect of each mechanism in the wave–current coupled processes is also investigated separately through numerical simulations. It is found that, even though the combined wave–current bottom stress has the largest effect, the combined effect of the other wave–current interactions, mean current advection and refraction of wave energy, wave radiation stress, and form drag (from largest to smallest effect), are comparable. These mechanisms can cause significant variation in the current velocities, vertical mixing, and even the bottom stress, and should obviously be paid more attention when modeling suspended-sediment dynamics during strong wave events.

Variations of transparency derived from GOCI in the Bohai Sea and the Yellow Sea

Secchi disk depth (Zsd), represents water transparency which is an intuitive indicator of water quality and can be used to derive inherent optical properties, chlorophyll-a concentrations, and primary productivity. In this study, the Zsd was derived from the Geostationary Ocean Color Imager (GOCI) data over the Bohai Sea (BHS) and the Yellow Sea (YS) using a regional tuned model. To validate the GOCI derived Zsd observations, in situ data, were collected for the BHS and YS regions. Results showed a good agreement between the GOCI derived Zsd observations and in situ measurements with a determination coefficient of 0.90, root mean square error of 2.17 m and mean absolute percent error of 24.56%. Results for diurnal variations showed an increasing trend of Zsd at the first and then decreasing, and all the maxima of Zsd in the central areas of the BHS and YS were found in the midday. For seasonal variations, higher values of Zsd, both in range and intensity, were observed in summer compared with those in winter. The reasons to explain the variations of Zsd have also been explored. Solar zenith angle (SOLZ) has an impact on the daily dynamics of Zsd, due to the influence of SOLZ on the attenuation of light radiation in water. The influence level of SOLZ on Zsd is largely determined by the water bodies' composition. The significant seasonal variations are mainly controlled by the stability of the water column stratification, because it can lead to the sediment resuspension and influence the growth and distribution of phytoplankton. Runoff and sediment discharge are not the main factors that impact the seasonal dynamics of Zsd. Tidal currents and mean currents may have influences on the variations of Zsd. However, due to the lack of in situ measurements to support, further studies are still needed.

Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations

AbstractUsing frequent and long‐term measurements (eight times per day, 2011 to present) from a geostationary satellite sensor (Geostationary Ocean Color Imager, GOCI), this study investigates diurnal changes of cyanobacterium Microcystis aeruginosa blooms (near‐surface high concentrations or surface scums) in Taihu Lake, from which vertical migration patterns could be inferred. After proper atmospheric correction, a cyanobacterial index algorithm is used to quantify equivalent surface cyanobacterial density (σ, 0–100%) at both pixel and synoptic scales from each cloud‐free image, followed by analysis of diurnal changing patterns of σ at both scales. Three typical diurnal changing patterns are identified from all images, which show distinctive and different seasonality from the long‐term statistics. Spatial distributions of the “hotspot” regions where diurnal changes are most often observed have also been established. While the seasonality of the three patterns appears to be a result of seasonality in both temperature and light availability, large blooms only occur 1 d after major wind events. Based on several lines of evidence, we hypothesize that the diurnal changes of such observed surface bloom patterns are likely a result of vertical migration rather than horizontal dissipation/aggregation of cyanobacteria. The mean migration speeds inferred from either a simple model or a radiative transfer model (&lt; 0.03 cm s−1 or &lt; 1 m h−1) are consistent with those reported earlier from laboratory measurements for certain cyanobacteria colony sizes. Complete understanding of the three types of diurnal patterns and direct validation of the hypothesis, however, require further investigations from field measurements.

Diurnal Variation of Light Absorption in the Yellow River Estuary

Considering the influence of river discharge and strong winds, the diurnal variability of ocean optical absorption properties in the Yellow River Estuary (YRE) is quantified, using in-situ measurements. The study finds that terrestrial sources due to the Yellow River discharge can cause high diurnal variation of water absorption because of the movement of river plume in the YRE, but such an influence diminishes far away from the Yellow River plume. The diurnal variability of water absorption, affected by strong winds, is found to be strengthened with a rapid increase of particles and colored dissolved organic matter (CDOM) arising from re-suspended sediment induced by wave forcing. The diurnal variability of particle absorption is controlled by non-algal particle absorption in the YRE, and the ratio of non-algal particle absorption (aNAP) and total particle absorption for most wavelengths is more than 0.56. The diurnal variation of spectral slope of non-algal particle absorption (SNAP) is found to vary within a narrow range, although large variability in the aNAP spectrum is observed. The CDOM is correlated negatively with salinity, and such negative correlation becomes weaker with the decreasing influence of riverine input. The spectral slope of CDOM absorption (Sg) may reflect the formation and constituents of CDOM with weak relationship to its concentration, and its relationship with the absorption of CDOM at 440 nm may be associated with the source of CDOM. The value of Sg, which is affected by re-suspended bottom sediment, is much lower than that derived from CDOM affected by Yellow River runoff. Disregarding the absorption of pure water, the diurnal variability of total water absorption stems principally from changes in non-algal particle matter rather than CDOM and Chl-a. By the observations of hourly GOCI (Geostationary Ocean Color Imager) data, the major diurnal variations of remote sensing reflectance at 680 nm are observed in near-coastal waters and the estuary of the Yellow River, which are mainly influenced by the flow discharge of Yellow River and strong winds. Finally, the seasonal differences of diurnal variations of water absorption caused by strong winds and river discharge are determined.

Hourly variation of green tide in the Yellow Sea during summer 2015 and 2016 using Geostationary Ocean Color Imager data

ABSTRACTIn this study, hourly shift of green tide (GT) in the Yellow Sea (YS) during summer of 2015 and 2016 is investigated using Geostationary Ocean Color Imager-based normalized difference vegetation index images. The GT started blooming around 45 and 8 km away from the coasts in the western parts of the YS during May 2015 and May 2016, respectively. The GT patches shifted northwards during June, and afterwards were spread out into the western and central YS until early July. During those periods, their average speeds were estimated as 1.97 ± 0.80 and 1.51 ± 0.55 km h−1 in 2015 and 2016, respectively. In spite of the significant hourly shift speeds, they did not move much for a day due to frequent direction changes between hours. By July, most of the GT patches dissipated from the YS; however, far apart in Korea Bay and offshore Gyeonggi Bay, Korea, some small short-lived patches were traced during August. GT coverage in 2016 increased considerably than those of 2015 and 2008 by almost 23% and 100%, respectively, which might have facilitated by a 2–3° sea surface temperature rise. This study provides an insight on hourly temporal dynamics which would be helpful for monitoring their changes and distribution.

Remote quantification of Cochlodinium polykrikoides blooms occurring in the East Sea using geostationary ocean color imager (GOCI)

Accurate and timely quantification of widespread harmful algal bloom (HAB) distribution is crucial to respond to the natural disaster, minimize the damage, and assess the environmental impact of the event. Although various remote sensing-based quantification approaches have been proposed for HAB since the advent of the ocean color satellite sensor, there have been no algorithms that were validated with in-situ quantitative measurements for the red tide occurring in the Korean seas. Furthermore, since the geostationary ocean color imager (GOCI) became available in June 2010, an algorithm that exploits its unprecedented observation frequency (every hour during the daytime) has been highly demanded to better track the changes in spatial distribution of red tide. This study developed a novel red tide quantification algorithm for GOCI that can estimate hourly chlorophyll-a (Chl a) concentration of Cochlodinium (Margalefidinium) polykrikoides, one of the major red tide species around Korean seas. The developed algorithm has been validated using in-situ Chl a measurements collected from a cruise campaign conducted in August 2013, when a massive C. polykrikoides bloom devastated Korean coasts. The proposed algorithm produced a high correlation (R2=0.92) with in-situ Chl a measurements with robust performance also for high Chl a concentration (300mg/m3) in East Sea areas that typically have a relatively low total suspended particle concentration (<0.5mg/m3).