Ocean Color Imager Research Articles

Eutrophication and lakes dynamic conditions control the endogenous and terrestrial POC observed by remote sensing: Modeling and application

The sources of particulate organic carbon (POC) determine its conversion, thereby playing an important role in the carbon cycle of lakes. Accurate estimation of the sources and dynamic characteristics of POC is important for understanding the migration and transformation of organic carbon. However, the synchronous observation of POC sources with large areas through remote sensing is still challenging because of the complex composition of POC and the optical conditions of inland lakes. In this study, a three-band (1/ Rrs (689)–1/ Rrs (717)) × Rrs (697)) empirical algorithm of POC sources was constructed based on remote sensing reflectance (Rrs(λ)) and the proportion of endogenous POC estimated from the field-measured stable isotope (δ13CPOC) values. The validation and calibration results of the three-band algorithm showed robust performance, with MAPE and RMSE of estimated values and measured values of 10% and 0.07, respectively. The three-band algorithm had good simulation results for the Ocean and Land Color Instrument (OLCI), Moderate Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), and Medium Resolution Imaging Spectrometer (MERIS) spectra. The POC sources estimated by the three-band algorithm suggest that the endogenous POC of Taihu Lake in August showed a decreasing trend from 2006 to 2019. The variation in terrestrial POC was slow and stable for both annual and monthly variations. The analysis of POC sources with total phosphorus (TP), total nitrogen (TN), water temperature, and wind speed indicated that terrestrial POC was closely related to wind speed (r = 0.33, P < 0.001), while endogenous POC was significantly associated with TP (r = 0.6, P < 0.001), TN (r = 0.56, P < 0.001), and water temperature (r = 0.49, P < 0.001). The use of remote sensing algorithms to evaluate POC from different sources is convenient and effective; furthermore, it helps to better understand the carbon cycle in lacustrine ecosystems.

Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns

Abstract. The Yonsei Aerosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 min AHI or 1 h GOCI data at 6 km×6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-Earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE.

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R &gt; 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI.

Intercalibration of MERIS, MODIS, and OLCI Satellite Imagers for Construction of Past, Present, and Future Cyanobacterial Biomass Time Series

Satellite imagery has been used to monitor and assess Harmful Algal Blooms (HABs), specifically, cyanobacterial blooms in Lake Erie (the USA and Canada) for over twelve years. In recent years, imagery has been applied to the other Great Lakes as well as other U.S. lakes. The key algorithm used in this monitoring system is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms. The CI is a “spectral shape” (or curvature) algorithm, which is a form of the second derivative around the 681 nm (MERIS/OLCI) or 678 nm (MODIS) band, which is robust and implicitly includes an atmospheric correction, allowing reliable use for many more scenes than analytical algorithms. Monitoring of cyanobacterial blooms with the CI began with the European Space Agency’s (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensor (2002–2012). With the loss of data from MERIS in the spring of 2012, the monitoring system shifted to using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS has bands that allow computation of a CI product, which was intercalibrated with MERIS at the time to establish a conversion of MODIS CI to MERIS CI. In 2016, ESA launched the Ocean and Land Color Imager (OLCI), the replacement for MERIS, on the Sentinel-3 spacecraft. MODIS can serve two purposes. It can provide a critical data set for the blooms of 2012–2015, and it offers a bridge from MERIS to OLCI. We propose a basin-wide integrated technique for intercalibrating the CI algorithm from MODIS to both MERIS and OLCI. This method allowed us to intercalibrate OLCI CI to MERIS CI, which would then allow the production of a 20-year and ongoing record of cyanobacterial bloom activity. This approach also allows updates as sensor calibrations change or new sensors are launched, and it could be readily applied to spectral shape algorithms.

Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (&lt;10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

Deriving Ocean Surface Currents: A method using Geostationary Ocean Color Imager hourly data

Tracking the movement of natural surface features across the time intervals between successive images has gained widespread acceptance for mapping ocean surface currents (OSCs). The 500-m and 1-h spatiotemporal resolutions of the Geostationary Ocean Color Imager (GOCI), launched in 2010, are ideal for observing the dynamics of mesoscale eddies and the diurnal changes dominated by tides. All reported works to date, however, are limited to a few occasions when there were cloudless skies and pixel-level accuracy. As a result, the abundant GOCI hourly data have not been put into an operational service to derive OSCs. This article revisits ocean feature detection and tracking techniques to discuss the development of a satellite image-matching system (SIMS). The results show that a SIMS can detect and track features from two consecutive GOCI images to approximately a 0.25-pixel level of accuracy in the presence of variable gaps due to land and clouds. Suspicious vectors can be ruled out by initially using size-irrelevant filtering and majority filtering methods with minimum preset thresholds. The products of pathlines and streamlines derived from GOCI hourly data make it possible to gain a better understanding of OSCs, both qualitatively and quantitatively.

Diurnal Variation of the Diffuse Attenuation Coefficient for Downwelling Irradiance at 490 nm in Coastal East China Sea

The diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd(490)) has complex characteristics in the coastal regions. However, owing to the scarcity of in situ data, our knowledge on the diurnal variation is inadequate. In this study, an optical-buoy dataset was used to investigate the diurnal variation of Kd(490) in the coastal East China Sea, and to evaluate the Kd(490) L2 products of geostationary ocean color imager (GOCI), as well as the performance of six empirical algorithms for Kd(490) estimation in the Case-2 water. The results of validation show that there was high uncertainty in GOCI L2 Kd(490), with mean absolute percentage errors (MAPEs) of 69.57% and 68.86% and root mean square errors (RMSEs) of 0.70 and 0.71 m−1 compared to buoy-measured Kd12(490) and Kd13(490), respectively. Meanwhile, with the coefficient of determination (R2) of 0.71, as well as the lowest MAPE of 27.31% and RMSE of 0.29 m−1, the new dual ratio algorithm (NDRA) performed the best in estimating Kd(490) in the target area, among the six algorithms. Further, four main types of Kd(490) diurnal variation were found from buoy data, showing different variabilities compared to the area closer to the shore. One typical diurnal variation pattern showed that Kd(490) decreased at flood tide and increased at ebb tide, which was confirmed by GOCI images through the use of NDRA. Hydrometeorological factors influencing the diurnal variations of Kd(490) were also studied. In addition to verifying the predominant impact of tide, we found that the dominant effect of tide and wind on the water column is intensifying sediment resuspension, and the change of sediment transport produced by them are secondary to it.

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.

Effects of ocean circulation on the eutrophication of a Mediterranean gulf with river inlets: The Northern Thermaikos Gulf

Thermaikos Gulf is a typical, river-fed, microtidal, semi-enclosed, coastal inlet of the east-central Mediterranean Sea. It is an important coastal ecosystem susceptible to several anthropogenic pressures, strong river discharges and variable meteorological and ocean (met-ocean) conditions. One of the most significant environmental problems of the region is the occasional formation of extended eutrophication phenomena (red tides, mucilaginous aggregates), especially over the Northern Thermaikos Gulf (NTG). Herein, we investigate the contribution of hydrodynamic processes on the formation of such events, under the effects of different meteorological and river discharge conditions during the annual cycle. We conducted field observations (physical-chemical measurements), microscopy analysis of phytoplankton samples, satellite ocean color image analysis, and implemented high-resolution numerical hydrodynamic simulations with updated river discharge outflows to detect eutrophication events and correlate them with the prevailing physical processes and ocean circulation patterns. The eutrophication events were mainly associated with the dominance of southerly winds, which affect the ocean circulation over the NTG in three ways: i) they confined the surface waters in the northern parts of the NTG separating the waters masses between the northern and southern regions, ii) they contribute on the northward spreading of nutrient-rich brackish waters towards the northern parts of the Gulf, and iii) they impose an anticyclonic circulation, especially in the inner- and central-Gulf weakening its renewal process. Northerly winds contribute on the renewal of the Gulf imposing a two-layer flow, especially along the eastern coasts. The seasonal observational campaigns of the 2017–2018 period captured three eutrophication events (June–July 2017, December 2017, and May 2018) and two renewal periods (late-July 2017 and October 2017) that were favoured by the two aforementioned types of met-ocean synergy. New insight is provided on the mesoscale ocean circulation and sub-mesoscale local effects on eutrophication events in a typical microtidal coastal system of the east-central Mediterranean, where freshwater discharges by a multi-river inlet.

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.

Using Triple Collocation Observations to Estimate Satellite Measurement Noise

Knowing how much measurement noise is in a signal is critical for evaluating the overall performance of a satellite observation. We developed a triple collocation observation (TCO) algorithm for estimating measurement noise by collocation comparing the local deviations of three satellite data sets. When we evaluated our algorithm with a synthetic data set, the results showed that the algorithm effectively derived measurement noise from satellite signals despite the many intermission signal differences among the satellites. The TCO algorithm produced &#x003C;6.66&#x0025; uncertainty in the measurement noise estimates that we derived from the synthetic data set. In addition, to maximally isolate measurement noise from ocean color images, we developed a set of data quality control criteria to apply when identifying synchronous pixel pairs. Using images from the Medium Resolution Spectral Imager II (MERSI II), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, we applied our data quality control criteria and found that the TCO algorithm produced measurement noise consistent with the measured prelaunch or specifications for VIIRS and MERSI II instrument noise. However, the TCO measurement noise was significantly lower than the spaced MODIS noise because MODIS&#x2019;s extended service time likely produced instrument degradation. Overall, MODIS performed better than MERSI II but worse than VIIRS. Furthermore, we found that the residual error in remote sensing reflectance exponentially decreased as the measurement signal-to-noise ratio (MSNR) increased. Because of this exponential relationship, the MSNR should not be lower than 181 to achieve the &#x003C;5&#x0025; uncertainty goal of remote sensing reflectance at 443 nm that NASA proposed. Our results suggest that the TCO algorithm is an effective approach for comprehensively estimating and comparing instrument performance.

Submesoscale Eddies in the South China Sea

AbstractSubmesoscale eddies are often seen in high‐resolution satellite‐derived ocean color images. To efficiently identify these eddies from surface chlorophyll data, here, we develop an automatic submesoscale eddy detection method and apply it to the South China Sea. The detected submesoscale eddies are found to have a radius of 13 ± 5 km and an aspect ratio of 0.5 ± 0.2, with a notable predominance of cyclones. Further investigation reveals that the surface structure of these eddies displays a unique “cat's‐eye” pattern, and the eddies become more circular with increasing eddy radius. Submesoscale eddies can strongly regulate surface chlorophyll via horizontal advection while they have less coherent signatures in sea surface temperature. These findings may help to improve submesoscale parameterizations in Earth system models.

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

Temporal unmixing-based cloud removal algorithm for optically complex water images

ABSTRACT Remote sensing has become an essential technique in water environment monitoring. However, the cloud contamination of remote sensing images breaks its spatial and temporal continuity in practical application. The current cloud removal algorithms always based on the hypothesis that the ground surface has no significant changes temporally, which is not proper for optically complex water body. Therefore, to overcome the weakness of current cloud removal algorithms on temporally fast-changing landscapes, a temporal unmixing-based cloud removal (TUBCR) algorithm is proposed to estimate the under-cloud information of optically complex water images. In this algorithm, the spatial information and temporal information were separated into temporal abundance images and temporal endmembers. By extracting the high temporal resolution endmembers, the fast-changing water information were estimated. The performance of the proposed algorithm was compared with those of two widely used cloud removal algorithms: the modified neighbourhood similar pixel interpolator (mNSPI) algorithm and the weighted linear regression (WLR) algorithm. The results of four simulated datasets (500 images in total) and two real Geostationary Ocean Colour Imager (GOCI) datasets (8 images in total) show that the proposed algorithm performed better in both quantitative indexes and visual effect compared with the mNSPI algorithm and the WLR algorithm. In satellite image time series, the proposed algorithm could accurately estimate under-cloud water information and correct the offsets of the statistical temporal trends that caused by cloud contamination. Hence, theoretically, the proposed TUBCR algorithm altered the similar pixel-based strategy of classic methods and got a satisfied performance in fast-changing water images. In applications, the TUBCR algorithm has great potential to improve the spatiotemporal coverage of optically complex water remote sensing.

Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean.

Tide-Induced Variability and Mechanisms of Surface Suspended Sediment in the Zhoushan Archipelago along the Southeastern Coast of China Based on GOCI Data

The variations and dynamics of suspended sediment in the coastal waters around the Zhoushan Archipelago are complex due to strong tidal dynamics. This study aims to delineate the tide-induced variability of surface suspended sediment and reveal its mechanisms in the Zhoushan Archipelago based on Geostationary Ocean Color Imager (GOCI) data. Suspended sediment concentration (SSC) estimations through the ultraviolet wavelength atmospheric correction (UV-AC) algorithm and a tuned empirical inversion algorithm using extensive in situ measurements are presented. GOCI-processed remote sensing reflectance (Rrs) is validated using field data and is cross-validated with Landsat 8 Operational Land Imager (L8/OLI) measurements, respectively. The estimated SSC is validated with independent datasets. The validations reveal that GOCI-processed Rrs is reasonable and that the GOCI-retrieved SSC is accurate and can be used to quantify SSC distributions and variations in the Zhoushan Archipelago. The variations of the SSC in the study area are closely related to the temporal variations of the tidal level. High SSCs often occur in the middle of ebb or flood tides due to large tidal discharge and high tidal velocity. Significant hourly variations of the SSC are mainly controlled by tidal forces. Dynamic mechanism analysis indicates that during neap and middle tides, the local high SSC is mainly attributed to the sediment resuspension process driven by tidal currents; in addition, during the spring tide, the variations of the SSC are simultaneously modulated by sediment resuspension and horizontal advection processes.

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade.

A Spatially Weighted Neural Network Based Water Quality Assessment Method for Large-Scale Coastal Areas.

The accurate assessment of large-scale and complex coastal waters is a grand challenge due to the spatial nonstationarity and complex nonlinearity involved in integrating remote sensing and in situ data. We developed a water quality assessment method based on a newly proposed geographically neural network weighted regression (GNNWR) model to address that challenge and obtained a highly accurate and realistic water quality distribution on the basis of the comprehensive index of Chinese Water Quality Classification Standards. Using geostationary ocean color imager (GOCI) data and observations from 1240 water quality sampling sites, we conducted experiments for a typical large-scale coastal area of the Zhejiang Coastal Sea (ZCS), People's Republic of China. The GNNWR model achieved higher prediction performance (average R2 = 84%) in comparison to the widely used models, and the obtained water quality classification (WQC) maps in May of 2015-2017 and August 2015 can depict intuitively reasonable spatiotemporal patterns of water quality in the ZCS. Furthermore, an analysis of WQC maps successfully illustrated how terrestrial discharges, anthropogenic activities, and seasonal changes influenced the coastal environment in the ZCS. Finally, we identified essential regions and provided targeted regulatory interventions for them to facilitate the management and restoration of large-scale and complex coastal environments.

Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data

Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time.

The Spatial-Temporal Distribution of GOCI-Derived Suspended Sediment in Taiwan Coastal Water Induced by Typhoon Soudelor

This paper discusses the use of a Geostationary Ocean Color Imager (GOCI) to monitor the spatial–temporal distribution of suspended sediment (SS) along the coastal waters of northern Taiwan which was affected by Typhoon Soudelor from 8 to 10 August 2015. High temporal resolution satellite images derived from GOCI were processed to generate four-day average images of SS for pre- and post-typhoon periods. By using these four-day average images, characteristics of SS along the north of Taiwan coastal water can be tracked. The results show that SS concentration increased in the four-day average image immediately after the typhoon (11–14 August), and then decreased in the four-day average image 9 to 12 days after the typhoon (19–22 August). The mouths of the Dajia River and Tamsui River were hotspots of SS, ranging from 9 to 15 g/m3 during the two post-typhoon periods. Moreover, the maximum suspended sediment (SSmax) and its corresponding time (tmax) can be computed using GOCI hourly images for the post-typhoon period from 08:30 on 11 August to 08:30 on 22 August. The results show that SSmax occurred in the west coastal water within 4 days post-typhoon, and SSmax occurred in the east coastal water 9 to 12 days post-typhoon. Furthermore, an exponential decay model was used to compute the time when 90% of typhoon-induced SS was dissipated after Typhoon Soudelor (t90). It was found that t90 in the mouths of the Tamsui River and Heping River was the longest among all coastal waters of our study area, with a range of 360–480 h. River discharge and ocean currents with suspended sediment concentration are discussed.