Assessment of the MCC method to estimate sea surface currents in highly turbid coastal waters from GOCI

The Geostationary Ocean Color Imager (GOCI) can produce good ocean colour products in the open sea. However, an atmospheric correction problem continues to occur for turbid coastal water environment monitoring. In this communication, a regional atmospheric correction method based on an artificial neural network (ANN) model has been proposed. The ANN model was built according to differences in the spatial and radiometric characteristics between the Medium Resolution Imaging Spectrometer (MERIS) and GOCI, with 3000 pixels of the top-of-atmosphere (TOA) reflectance of seven GOCI images from 2011 to 2012 above turbid water used as the inputs and coinciding validated remote-sensing reflectance (Rrs) of MERIS 1 used as the outputs. Subsequently, the water-leaving reflectance of GOCI in turbid coastal water areas of the Bohai Sea was derived. Compared with the products produced by the standard GOCI Data Processing System (GDPS Version 1.3), the Rrs retrieved according to the proposed method showed a significant improvement in spatial pattern. Although the ANN model displayed a degree of difficulty in representing high water-leaving reflectance values, a comparison with three in situ measurements collected on 11 November 2011 in the study area showed encouraging results. The results suggest that the ANN method can be used for atmospheric correction process in turbid waters without requiring numerous in situ measurements.

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components.

An improved spectral optimization algorithm for atmospheric correction over turbid coastal waters: A case study from the Changjiang (Yangtze) estuary and the adjacent coast

The Geostationary Ocean Color Imager (GOCI) sensor, with high temporal and spatial resolution (eight images per day at an interval of 1 hour, 500 m), is the world’s first geostationary ocean color satellite sensor. GOCI provides good data for ocean color remote sensing in the Western Pacific, among the most turbid waters in the world. However, GOCI has no shortwave infrared (SWIR) bands making atmospheric correction (AC) challenging in highly turbid coastal regions. In this paper, we have developed a new AC algorithm for GOCI in turbid coastal waters by using quasi-synchronous Visible Infrared Imaging Radiometer Suite (VIIRS) data. This new algorithm estimates and removes the aerosol scattering reflectance according to the contributing aerosol models and the aerosol optical thickness estimated by VIIRS’s near-infrared (NIR) and SWIR bands. Comparisons with other AC algorithms showed that the new algorithm provides a simple, effective, AC approach for GOCI to obtain reasonable results in highly turbid coastal waters.

The first geostationary ocean color sensor, Geostationary Ocean Color Imager (GOCI), on board the Korean Communication Ocean and Meteorological Satellite (COMS), was successfully launched on June 26 of 2010. GOCI includes 8 spectral bands in visible and near-infrared wavelengths with a coverage area of 2,500×2,500 km2 centered at 36°N and 130°E over the Korean seas. GOCI will provide an important capability to monitor ocean phenomenon with one hour temporal and 500 m spatial resolutions for a better understanding of biogeochemical processes in the Korean seas. However, there are uncertainties in estimating bio-optical properties since water properties in large areas of Koreans are optically characterized as Case-2 waters due to strong tidal mixing and large amount of river discharges. The newly-developed semi-analytical algorithm of diffusion attenuation coefficient at the wavelength of 490 nm, Kd(490), for the turbid coastal waters was assessed using in situ radiometric and Kd(490) measurement obtained from clear and turbid waters over the global ocean. Results of the Kd(490) data using the new model is well correlated with the in situ Kd(490) measurements. Synoptic maps of Kd(490) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite using the new model were derived in the Yellow Sea and East China Sea. The MODIS-derived Kd(490) data show significant increased values along the turbid coastal waters including the Bohai Sea and the Yangtze River Estuary. In general, the highest Kd(490) appeared in winter and the lowest Kd(490) are presented in summer over the all area. Interannual variability of Kd(490) in timing and magnitude is apparent, but there is no consistent trend of interannual variability across all areas.

The Geostationary Ocean Color Imager (GOCI) provided images at hourly intervals up to 8 times per day with a spatial resolution of 500 m from 2011 to 2021. However, in the typical sediment-laden turbid water of Hangzhou Bay, valid ocean color parameters in operational data products have been extensively missing due to failures in atmospheric correction (AC) and bio-optical retrieval procedures. In this study, the seasonal variations in chlorophyll a (Chl-a) concentrations in Hangzhou Bay derived using GOCI data in 2020 were presented. First, valid remote sensing reflectance data were obtained by transferring neighboring aerosol properties of less to more turbid water pixels. Then, we improved a regionally empirical Chl-a retrieval algorithm in extremely turbid waters using GOCI-derived surface reflectance and field Chl-a measurements and proposed a combined Chl-a retrieval scheme for both moderately and extremely turbid water in Hangzhou Bay. Finally, the seasonal variation in Chl-a was obtained by the GOCI, which was better than operational products and in good agreement with the buoy data. The method in this study can be effectively applied to the inversion of Chl-a concentration in Hangzhou Bay and adjacent sea areas. We also presented its seasonal variations, offering insight into the spatial and seasonal variation of Chl-a in Hangzhou Bay using the GOCI.

Mapping surface currents with high spatiotemporal resolution over a wide coverage is crucial for understanding ocean dynamics and associated biogeochemical processes. The most widely used algorithm for estimating surface velocities from sequential satellite observations is the maximum cross-correlation (MCC) method. However, many unrealistic vectors still exist, despite the utilization of various filtering techniques. In this study, an objective method has been developed through the combination of MCC and multivariate optimum interpolation (MOI) analysis under a continuity constraint. The MCC method, with and without MOI, is applied to sequences of simulated sea surface temperature (SST) fields with a 1/48° spatial resolution over the East China Sea continental shelf. Integration of MOI into MCC reduces the average absolute differences between the model’s ‘actual’ velocity and the SST-derived velocity by 19% in relative magnitude and 22% in direction, respectively. Application of the proposed method to Geostationary Ocean Color Imager (GOCI) satellite observations produces good agreement between derived surface velocities and the Oregon State University (OSU) regional tidal model outputs. Our results demonstrate that the incorporation of MOI into MCC can provide a significant improvement in the reliability and accuracy of satellite-derived velocity fields.

In turbid coastal waters, sea surface currents often exhibit frequent spatiotemporal variability, retrieving them from optical satellite data remains challenging due to the saturated signal caused by suspended particles. This study uses the maximum cross‐correlation (MCC) method to retrieve Eulerian currents and Lagrangian trajectories in turbid nearshore areas, and the performance of MCC with different correlation coefficients and tracers are evaluated. Current vectors calculated using the Cosine correlation coefficient show the highest consistency with high frequency radar currents. The minimum average magnitude error and average angular error were 0.51 and 23.98°, respectively. They also demonstrate strong statistical correlations of 0.99 for direction and 0.8 for speed with measured tidal currents, surpassing results calculated using Pearson and Tanimoto correlation coefficients. Eulerian currents and Lagrangian trajectories can effectively be derived from the tracers inverted from Rayleigh‐corrected reflectance. The number of valid current vectors from total suspended matter (TSM) and chlorophyll‐a concentration (Chl) is more than double that estimated with the tracers inverted from thoroughly atmospheric corrected reflectance. The fusion of these two tracers further enhances the reliability and consistency of the estimated currents. In tidal‐dominant regions, Lagrangian trajectories from TSM and Chl closely match trends in observed tidal currents. Applying the estimated currents, the maximum resuspension rate in Haizhou Bay and northern Subei Shoal is 105.77 mg/(L·h), whereas the maximum deposition rate is 50.78 mg/(L·h). This study enhances the capabilities of high‐resolution optical satellite data for observing and analyzing marine environments and physical processes in turbid coastal waters.

Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrated that this improved method can identify the spatial positions and shapes of clouds more realistically, and more accurate pixels of turbid waters were retained. This improved method can be effectively applied in typical turbid coastal waters. It has potential to be used in cloud masking procedures of spaceborne ocean color sensors without short-wave infrared bands.

Satellite remote sensing technology shows excellent potential and application in the observation of sea surface current dynamics. In this study, we used the high temporal resolution geostationary satellite images from the Advanced Himawari Imager (AHI) and the Geo-stationary Ocean Color Imager (GOCI) to detect the sea surface currents (SSC) in the Bohai Sea, China. The sequential speed and direction of sea surface currents were estimated from 8:30 to 15:30 local time on 8 July 2020, using the maximum correlation coefficient (MCC) technique. The sea surface currents estimated from AHI imagery were compared with those from GOCI imagery, and current data from the OSU TPXO Tidal Model. The results show that the high temporal sea surface currents estimated from geostationary satellite imagery are accordant with the model data. It is clear that AHI can observe sea surface currents more frequently than GOCI and reflects more details of the diurnal dynamic changes in coastal waters.

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

Measuring sea surface currents is a technological challenge in oceanography. Feature tracking in time series of remote sensing imagery has been proposed as a way to address this problem. The most commonly used approach is the maximum cross-correlation (MCC) method, originally developed to track cloud motion. We propose a new technique that makes use of Daubechies wavelet analysis combined with the MCC method. In our approach, satellite images are decomposed into various spatial scales using the wavelet transform, and the location with the MCC coefficient among all the scales is selected as the most likely new position of the tracked feature. Results from the analysis of five pairs of sequential National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) images of the Gulf of St. Lawrence area show that wavelet analysis improves the estimated sea surface current field by increasing the number of current vectors about 20% under the same confidence level (0.9) as compared with that using the MCC method alone.

One of the significant challenges in physical oceanography is getting an adequate space/time description of the ocean surface currents. One possible solution is the maximum cross-correlation (MCC) method that we apply to hourly ocean color images from the Geostationary Ocean Color Imager (GOCI) over five years. Since GOCI provided a large number of image pairs, we introduce a new MCC search strategy to improve the computational efficiency of the MCC method saving 95% of the processing time. We also use an MCC current merging method to increase the total spatial coverage of the currents, proving a 25% increase. Five-year mean and seasonal time-average flows are computed to capture the major currents in the area of interest. The mean flows investigate the Kuroshio path, support the triple-branch pattern of the Tsushima Warm Current (TC), and reveal the origin of the TC. The evolution of a warm core ring shed by the Kuroshio near the northeast coast of Honshu, Japan, is clearly depicted by a sequence of three monthly MCC composites. We capture the evolution of the Kuroshio meander over seasonal, monthly, and weekly time scales. Three successive weekly MCC composite maps demonstrate how a large anticyclonic eddy, to the south of the Kuroshio meander, influences its formation and evolution in time and space. The unique ability to view short space/time scale changes in these strong current systems is a major benefit of the application of the MCC method to the high spatial resolution and rapid refresh GOCI data.

Aimed at high turbid coastal water of Yellow Sea, an improved algorithm for retrieval of aerosol optical properties from Geostationary Ocean Color Imager (GOCI) is proposed. The algorithm can retrieve aerosol optical depth (AOD) and aerosol types. The algorithm adopt support vector machine (SVM) to separate the interfering signal of phytoplankton pigments, suspended matter and chromophoric dissolved organic matter (CDOM). Radiative transfer model is utilized to simulate the transmitting process. AERONET data and GOCI service product is used to estimate the accuracy of the advanced method. The study shows that this algorithm has better performance compared with GOCI service algorithm for turbid water in Yellow Sea.

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.