Geostationary Ocean Color Imager Research Articles

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

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

Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia

Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.

Marine Disaster Detection Using the Geostationary Ocean Color Imager (GOCI)

Recently, harmful algae (e.g., red tide) has damaged human and marine ecosystems. To address this, a response system should be developed to quickly cope with these ocean disasters. However, it is difficult to simultaneously monitor the vast ocean areas. Here, a marine disaster detection system can be developed through a convergence between the satellite-based ocean color remote sensing and the marine sensor network. The system architecture is divided into two steps: first, the system detects ocean anomalies in realtime using the satellite-based techniques, and secondly, the detected disaster information is transferred to the ships via the marine sensor networks. In this paper, we only focused on the first step and the second step is reserved for future work. Although the polar orbit satellite-based ocean color sensor platforms (e.g., MODIS, MERIS, and SeaWifs) can be used to simultaneously monitor the vast ocean areas, they are unsuitable for capturing subtle changes on a geographically equivalent area. On the other hand, the Geostationary Ocean Color Imager (GOCI), the world’s first ocean color remote sensor platform operated on a geostationary orbit, receives ocean color data around the Northeast Asia region every hour, eight times a day. Therefore, GOCI can be more effectively utilized to observe subtle changes and to detect anomalies in ocean environments in real-time. In this paper, we attempted to build a system to monitor marine disasters by detecting ocean anomalies using the ocean color data derived from GOCI. This system directly compares the test spectrum vectors (i.e. anomaly candidates) to a predefined reference spectrum vector (i.e. a target anomaly) through the cosine similarity. The experimental result showed that the proposed system could efficiently detect the disasters (e.g., the red tide) on the ocean environments.

GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia

Abstract. To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers a part of Northeast Asia (113–146° E; 25–47° N), were used. Although GOCI can provide a higher number of AOD data in a semicontinuous manner than low Earth orbit (LEO) satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatiotemporal-kriging (STK) method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages in using the STK method in this study is that more observed AOD data can be used to prepare the best initial AOD fields compared with other methods that use single frame of observation data around the time of initialization. It is demonstrated in this study that the short-term PM forecast system developed with the application of the STK method can greatly improve PM10 predictions in the Seoul metropolitan area (SMA) when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ∼ 60 and ∼ 70 %, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC (particle-into-liquid sampler coupled with ion chromatography) and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM forecast system, several future research directions were also discussed and suggested.

Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea.

In this paper, a new daytime sea fog detection algorithm has been developed by using Geostationary Ocean Color Imager (GOCI) data. Based on spectral analysis, differences in spectral characteristics were found over different underlying surfaces, which include land, sea, middle/high level clouds, stratus clouds and sea fog. Statistical analysis showed that the Rrc (412 nm) (Rayleigh Corrected Reflectance) of sea fog pixels is approximately 0.1-0.6. Similarly, various band combinations could be used to separate different surfaces. Therefore, three indices (SLDI, MCDI and BSI) were set to discern land/sea, middle/high level clouds and fog/stratus clouds, respectively, from which it was generally easy to extract fog pixels. The remote sensing algorithm was verified using coastal sounding data, which demonstrated that the algorithm had the ability to detect sea fog. The algorithm was then used to monitor an 8-hour sea fog event and the results were consistent with observational data from buoys data deployed near the Sheyang coast (121°E, 34°N). The goal of this study was to establish a daytime sea fog detection algorithm based on GOCI data, which shows promise for detecting fog separately from stratus.

Retrieval of the diffuse attenuation coefficient from GOCI images using the 2SeaColor model: A case study in the Yangtze Estuary

Abstract The 2SeaColor model (Salama & Verhoef, 2015) was proposed to analytically retrieve the diffuse attenuation coefficient (Kd) from remote sensing reflectance (Rrs), but its parameterization was based on approximations and subjected to large uncertainties. In this study, we present an improvement on the parameterization equations in the inverse scheme of the 2Seacolor model. The improved model is then validated with three in-situ datasets and compared with the Zhang model (Zhang & Fell, 2007) and the Lee model (Lee, Darecki et al., 2005). Validation with radiometric data shows that the 2SeaColor model provides the best estimates of Kd for the full range of observations, with the largest determination coefficient (R2 = 0.935) and the smallest root mean squared error (RMSE = 0.078 m− 1). For clear waters, where Kd(490) 0.2 m− 1, the 2SeaColor model is found to be more accurate, with an RMSE of 0.186 m− 1, compared to RMSEs of 0.279 m− 1 and 0.388 m− 1 for the Zhang model and the Lee model, respectively. The 2SeaColor model is finally applied to the GOCI (Geostationary Ocean Color Imager) level 2 product (L2P) to produce Kd maps over the Yangtze Estuary, resulting in a reasonable distribution and expected range of Kd, as for example Kd(490) was varying from 0.04 to 9.82 m− 1 for the image acquired at 02:16 UTC, on March 8th 2013. The analytical 2SeaColor model is able to provide consistently stable and fairly accurate Kd estimates in both clear and turbid waters without the need of tuning empirical coefficients from field measurements, and thus has great potential for estimating Kd over optically complex waters.

Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite

The purpose of this study was to optimize a composite method for the Geostationary Ocean Color Imager (GOCI), which is the first geostationary ocean color sensor in the world. Before interpreting the sensitivity of each composite with ground measurements, we evaluated the accuracy of bidirectional reflectance distribution function (BRDF) performance by comparing modeled surface reflectance from BRDF simulation with GOCI-measured surface reflectance according to composite period. The root mean square error values for modeled and measured surface reflectance showed reasonable accuracy for all of composite days since each BRDF composite period includes at least seven cloud-free angular sampling for all BRDF performances. Also, GOCI-BRDF-adjusted NDVIs with four different composite periods were compared with field-observation NDVI and we interpreted the sensitivity of temporal crop dynamics of GOCI-BRDF-adjusted NDVIs. The results showed that vegetation index seasonal profiles appeared similar to vegetation growth curves in both field observations from crop scans and GOCI normalized difference vegetation index (NDVI) data. Finally, we showed that a 12-day composite period was optimal in terms of BRDF simulation accuracy, surface coverage, and real-time sensitivity.

春季辽东湾静止轨道海洋水色遥感产品的真实性检验

春季辽东湾静止轨道海洋水色遥感产品的真实性检验

基于静止轨道水色卫星数据的渤海总吸收系数遥感反演和逐时变化研究

基于静止轨道水色卫星数据的渤海总吸收系数遥感反演和逐时变化研究

Estimation of critical shear stress for erosion in the Changjiang Estuary: A synergy research of observation, GOCI sensing and modeling

AbstractSimulating the sediment transport in a high‐turbidity region with spatially varying bed properties is challenging. A comprehensive strategy that integrates multiple methods is applied here to retrieve the critical shear stress for erosion, which plays a major role in suspended sediment dynamics in the Changjiang Estuary (CE). Time‐series of sea surface suspended sediment concentration (SSC) were retrieved from the Geostationary Ocean Color Imager (GOCI) satellite data at hourly intervals (for 8 h each day) and combined with hydrodynamic modeling of high‐resolution CE Finite‐Volume Community Ocean Model (CE‐FVCOM) to estimate the near‐bed critical shear stress in the clay‐dominated bed region (plasticity index > 7%). An experimental algorithm to determine the in situ critical shear stress via the plasticity index method was also used to verify the GOCI‐derived critical shear stress. Implemented with this new critical shear stress, the sediment transport model significantly improved the simulation of the distribution and spatial variability of the SSC during the spring and neap tidal cycles in the CE. The results suggest that a significant lateral water exchange between channels and shoals occurred during the spring flood tide, which led to a broader high‐SSC area in the CE throughout the water column.

Estimating ground-level PM<sub>2.5</sub> in eastern China using aerosol optical depth determined from the GOCI satellite instrument

Abstract. We determine and interpret fine particulate matter (PM2.5) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5 / AOD ratio exhibits high consistency with ground-based measurements in Taiwan (MFB = −0.52 %) and Beijing (MFB = −8.0 %). We evaluate the satellite-derived PM2.5 versus the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in situ observations in both annual averages (r2 = 0.66, N = 494) and monthly averages (relative RMSE = 18.3 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical composition of GOCI-derived PM2.5 reveals that secondary inorganics (SO42-, NO3-, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating increase the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over eastern China for 2013 is 53.8 μg m−3, with 400 million residents in regions that exceed the Interim Target-1 of the World Health Organization.

천리안 GOCI영상을 이용한 남해안 적조우심해역 분석

매년 적조가 발생하여 양식어민들에게 막대한 피해를 주고 있으며 발생해역도 남해안을 중심으로 발생되던 패턴에서 전국 연안 해역으로 확대되는 추이를 보이고 있다. 광활한 해양에서 발생되는 적조를 효과적으로 모니터링하기 위해 2010년에 발사된 천리안 위성의 GOCI(Geostationary Ocean Color Imager)영상을 이용한 적조 탐지기술 개발에 관심이 모아지고 있다. 본 연구에서는 남해안 해역에 대해 최근 3년간(2012, 2013, 2014년) 관측된 천리안 GOCI영상을 이용하여 적조해역을 탐지하고 탐지된 해역에 대한 적조발생빈도와 밀도를 분석하였다. 그 결과 3년간 남해안을 대상으로 적조 발생 해역을 추출하고 중첩분석과 밀도분석을 통하여 적조우심해역을 추출하여 제시하였다. 또한 연도별 적조발생 경향은 2012년에 적조 발생규모가 작고 산발적으로 발생하였고, 2013년은 적조 발생해역이 광범위하게 분포하면서 공간적 밀집도도 높게 나타났으며, 2014년의 경우에는 소규모의 적조가 산발적으로 발생하였다. 이처럼 연도별 적조발생의 공간적 분포패턴은 불규칙한 특징을 보였으며 다양하게 변화되고 있음을 알 수 있었다. 하지만 적조발생빈도를 기반으로 핫스팟을 분석한 결과 특정 해역에서는 발생빈도가 꾸준히 증가되고 있어서 천리안 GOCI 영상과 같은 위성영상모니터링 기술을 이용하여 지속적으로 모니터링을 실시함으로써 적조의 움직임을 정확히 예측할 수 있고 이에 따른 방재계획을 체계적으로 수립할 수 있다고 판단된다. The area of red tides occurences, which brings enormous damages every year, have been expanded to the coastal waters across the nation. Regarding to this trend, the development of red tide detection technology by using the GOCI (Geostationary Ocean Color Imager) of COMS lauched in 2010 has been drawn attentions of researchers. This study purposed on analyzing the frequency and density of red tides occurence by using the GOCI for detecting the southern sea, whereas targeted area. The observation has brought over the last three years (2012, 2013, and 2014) before the analysis was conducted. Followingly, the study could be resulted in extracting and revealing the hot spots of the red tides from two of analysis in the overlay and density. The distribution patterns of red tide occurrences according to those observed years has been shown in irregular characteristics and various changes. However, the analysis of hot spots, based on the frequency of the red tide occurrence, has revealed that the frequency of red tide occurences is continuously increased in the specific sea area. Therefore, it is concluded in that the continuous monitoring can contribute to predict accurate movements of red tides, so as establish systematic plans for preventing disasters.

Comparative analysis of GOCI ocean color products.

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color sensor in orbit that provides bio-optical properties from coastal and open waters around the Korean Peninsula at unprecedented temporal resolution. In this study, we compare the normalized water-leaving radiance () products generated by the Naval Research Laboratory Automated Processing System (APS) with those produced by the stand-alone software package, the GOCI Data Processing System (GDPS), developed by the Korean Ocean Research & Development Institute (KORDI). Both results are then compared to the measured by the above water radiometer at the Ieodo site. This above-water radiometer is part of the Aerosol Robotic NETwork (AeroNET). The results indicate that the APS and GDPS processed correlates well within the same image slot where the coefficient of determination (r2) is higher than 0.84 for all the bands from 412 nm to 745 nm. The agreement between APS and the AeroNET data is higher when compared to the GDPS results. The Root-Mean-Squared-Error (RMSE) between AeroNET and APS data ranges from 0.24 at 555 nm to 0.52 at 412 nm while RMSE between AeroNET and GDPS data ranges from 0.47 at 443 nm to 0.69 at 490 nm.

Correction of Stray-Light-Driven Interslot Radiometric Discrepancy (ISRD) Present in Radiometric Products of Geostationary Ocean Color Imager (GOCI)

The radiometric calibration of satellite data is critical in many environmental studies and applications that are based on remote sensing data. The Geostationary Ocean Color Imager (GOCI) has suffered from what is called an interslot radiometric discrepancy (ISRD), which creates clear inconsistency between the adjacent slots in GOCI Level 1B (L1B) radiometric products, the largest source of which is currently believed to be the stray light generated in the sensor instrument. Difficulties in removing the stray-light-driven anomalies are that the intensity and the spatial extent vary with time and location, depending on the reflectance of nearby bright targets, such as cloud and land. This paper proposes an image-based correction method that removes the stray-light-driven radiometric inflation without involving an independent reference so that the method can be used for GOCI operational data processing. First, the radiometric inflation pattern is characterized by independent sources, such as Moderate Resolution Imaging Spectrometer (MODIS) data, and the inflation pattern is modeled by the minimum noise fraction transform of the input data. The modeled inflation patterns in individual slots are then adjusted across the slots in such a way that the overall ISRD in all slot boundaries is minimized. The analysis shows that the stray-light-driven radiometric anomalies can be up to 20% of the normal signals in Bands 6 (680 nm) and 8 (865 nm) of the uncorrected L1B images, and the proposed correction method reduces it to less than 2% in most of the cases, recovering the spatial continuity of natural variability across the slots.

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m3, respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm.

Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice

The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology.

Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model

In this paper, satellite remote sensing was used as the input parameter of the GRAMI rice model to evaluate its applicability for simulating paddy rice crop condition and yield assessment at the field scale. Especially, the world’s first Geostationary Ocean Color Imager (GOCI), which provides better temporal resolution than does MODIS, was applied to evaluate the estimation of intuitive paddy rice growth and development and to examine the feasibility for vegetation index profiles of the GRAMI rice model. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data at 500-m resolution were used as reference data to validate the quality of the crop growth and development data derived from GOCI. Field measurements of paddy rice at Chonnam National University, Gwangju, South Korea, were performed to determine initial parameters of the GRAMI rice model, which is used to optimize biophysical processes in the soil–crop–atmosphere system. For angular-dependent vegetation products, daily rolling time series of vegetation indices of GOCI and MODIS were estimated using semi-empirical BRDF modeling based on 16-day composite procedures. The observed temporal variation in GOCI vegetation indices (VIs) based on BAR (bidirectional reflectance distribution function adjusted reflectance) showed a similar growing pattern to the simulated VIs of the crop model, but MODIS showed a difference between measured and simulated VIs during the cloudy monsoon season. The rice yields predicted by integrating satellite data and the GRAMI rice model were compared with field measurements and showed reasonable agreement with reference to paddy rice productivity in the study area.

Vicarious calibration of the Geostationary Ocean Color Imager.

Measurements of ocean color from Geostationary Ocean Color Imager (GOCI) with a moderate spatial resolution and a high temporal frequency demonstrate high value for a number of oceanographic applications. This study aims to propose and evaluate the calibration of GOCI as needed to achieve the level of radiometric accuracy desired for ocean color studies. Previous studies reported that the GOCI retrievals of normalized water-leaving radiances (nLw) are biased high for all visible bands due to the lack of vicarious calibration. The vicarious calibration approach described here relies on the assumed constant aerosol characteristics over the open-ocean sites to accurately estimate atmospheric radiances for the two near-infrared (NIR) bands. The vicarious calibration of visible bands is performed using in situ nLw measurements and the satellite-estimated atmospheric radiance using two NIR bands over the case-1 waters. Prior to this analysis, the in situ nLw spectra in the NIR are corrected by the spectrum optimization technique based on the NIR similarity spectrum assumption. The vicarious calibration gain factors derived for all GOCI bands (except 865nm) significantly improve agreement in retrieved remote-sensing reflectance (Rrs) relative to in situ measurements. These gain factors are independent of angular geometry and possible temporal variability. To further increase the confidence in the calibration gain factors, a large data set from shipboard measurements and AERONET-OC is used in the validation process. It is shown that the absolute percentage difference of the atmospheric correction results from the vicariously calibrated GOCI system is reduced by ~6.8%.

Innovative GOCI algorithm to derive turbidity in highly turbid waters: a case study in the Zhejiang coastal area.

An innovative algorithm is developed and validated to estimate the turbidity in Zhejiang coastal area (highly turbid waters) using data from the Geostationary Ocean Color Imager (GOCI). First, satellite-ground synchronous data (n = 850) was collected from 2014 to 2015 using 11 buoys equipped with a Yellow Spring Instrument (YSI) multi-parameter sonde capable of taking hourly turbidity measurements. The GOCI data-derived Rayleigh-corrected reflectance (R(rc)) was used in place of the widely used remote sensing reflectance (R(rs)) to model turbidity. Various band characteristics, including single band, band ratio, band subtraction, and selected band combinations, were analyzed to identify correlations with turbidity. The results indicated that band 6 had the closest relationship to turbidity; however, the combined bands 3 and 6 model simulated turbidity most accurately (R(2) = 0.821, p<0.0001), while the model based on band 6 alone performed almost as well (R(2) = 0.749, p<0.0001). An independent validation data set was used to evaluate the performances of both models, and the mean relative error values of 42.5% and 51.2% were obtained for the combined model and the band 6 model, respectively. The accurate performances of the proposed models indicated that the use of R(rc) to model turbidity in highly turbid coastal waters is feasible. As an example, the developed model was applied to 8 hourly GOCI images on 30 December 2014. Three cross sections were selected to identify the spatiotemporal variation of turbidity in the study area. Turbidity generally decreased from near-shore to offshore and from morning to afternoon. Overall, the findings of this study provide a simple and practical method, based on GOCI data, to estimate turbidity in highly turbid coastal waters at high temporal resolutions.

Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands

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