Search Results (78)

Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China’s Huanjing (Chinese for environmental, HJ)-1A/B (charged couple device) CCD (30 m), and from Korea’s Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution HJ CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding HJ CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays. Full article

Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. Full article

The delimitation of an ecotone is an important reference for ecosystem conservation; however, the assessment of a marine ecotone from an ecological point of view represents a knowledge gap. The Yellow River Estuary (YRE) serves as both spawning and feeding grounds for numerous economically important organisms. Delineating the boundary of YRE and assessing the boundary change have great importance in maintaining its ecosystem health. This study attempts to apply a Moving Split Window (MSW) to determine marine boundary in YRE. Level 2 remote sensing satellite data spanning from 2012 to 2020 sourced from the Geostationary Ocean Color Imager (GOCI) were utilized. Chlorophyll-a, Chromophoric Dissolved Organic Matter (CDOM), and Total Suspended Solids (TSS) were employed as variables, with Squared Euclidean Distance (SED) serving as the determinant for identifying the marine ecological ecotone within the Yellow Estuary and its adjacent waters. Results indicate the following: (1) SED values exhibit distinct peaks and valleys, facilitating the accurate identification of marine ecotones via MSW. (2) Evident ecotones are observable in both the gate and coastal regions. (3) The influence range of TSS on the gate spans between 10 km and 14 km. In synthesis, the ensuing conclusions are drawn: MSW proves to be a reliable method for quantitatively determining ecotones in marine environments. Furthermore, MSW introduces a novel approach to the delineation of marine ecotones. Full article

Sea surface currents (SSCs) play a pivotal role in material transport, energy exchange, and ecosystem dynamics in coastal marine environments. While traditional methods to obtain wide-range SSCs, such as satellite altimetry, often struggle with limited performance in coastal regions due to waveform contamination, deriving SSCs from sequential ocean color data using maximum cross-correlation (MCC) has emerged as a promising approach. In this study, we proposed a novel SSC estimation method, called tide-restricted maximum cross-correlation (TRMCC), and implemented it on hourly ocean color data obtained from the Geostationary Ocean Color Imager II (GOCI-II) and the global tide model FES2014 to derive SSCs in coastal seas and turbid estuaries. Cross-comparison over three years with buoy data, high-frequency radar, and numerical model products shows that TRMCC is capable of obtaining high-resolution SSCs with good accuracy in coastal and estuarine areas. Both large-scale ocean circulation patterns in seas and fine-scale surface current structures in estuaries can be effectively captured. The deriving accuracy, especially in coastal and estuarine areas, can be significantly improved by integrating tidal current data into the MCC workflow, and the influence of invalid data can be minimized by using a flexible reference window size and normalized cross-correlation in the Fourier domain technique. Seasonal SSC structure in the Bohai Sea and diurnal SSC variation in the Yangtze River Estuary were depicted via the satellite method, for the first time. Our study highlights the vast potential of TRMCC to improve the understanding of current dynamics in complex coastal regions. Full article

Rapid and accurate dynamic monitoring and quantitative analysis of red tide disasters are of significant practical importance to national economic development. Remote sensing technology is an effective means for monitoring red tides. This paper utilizes GOCI satellite data and employs a quasi-analytical algorithm (QAA) to retrieve the spectral curves of phytoplankton absorption coefficients. On the basis of a detailed analysis of the differences in the spectral curves of the phytoplankton absorption coefficients between red tide and non-red tide waters, we establish a red tide identification algorithm for the East China Sea on the basis of phytoplankton absorption coefficients. The algorithm is applied to multiple red tide events in the East China Sea. The results indicate that this algorithm can effectively determine the occurrence locations of red tides and extract relevant information about them. Full article

The recurring occurrence of green tides as an ecological disaster has been reported annually in the Yellow Sea. While remote sensing technology effectively tracks the scale, extent, and duration of green tide outbreaks, there is limited research on the underlying driving mechanisms of green tide drift transport and the determination of the leeway coefficient. This study investigates the green tide transport mechanism and evaluates the feasibility of estimating the leeway coefficient by analyzing green tide drift velocities obtained from Geostationary Ocean Color Imager-II (GOCI-II) images using the maximum cross-correlation (MCC) technique and leeway method across various time intervals alongside ocean current and wind speed data. The results reveal the following: (1) Significant spatial variations in green tide movement, with a distinct boundary at 34°40′N. (2) Short-term green tide transport is primarily influenced by tidal forces, while wind and ocean currents, especially the combined Ekman and geostrophic current component, predominantly govern net transport. (3) Compared to 1, 3, and 7 h intervals, estimating the leeway coefficient with a 25 h interval is feasible for moderate-resolution geostationary images, yielding values consistent with previous studies. This study offers new insights into exploring the transport mechanisms of green tides through remote sensing-driven velocity. Full article

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (R_rs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field R_rs (R_rs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI R_rs data (R_rs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R² = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAI_Rrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAI_Rrs (R² = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with R_rs_G and environmental parameters (R² = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management. Full article

During the summer, substantial freshwater discharge from the Changjiang River into the East China Sea (ECS) results in extensive low-salinity water (LSW) plumes that significantly affect regions along the southern Korean Peninsula and near Jeju Island. Previous research developed an empirical equation to estimate sea surface salinity (SSS) in the ECS during the summer season using remote-sensing reflectance (R_rs) data from bands 3–6 (490, 555, 660, and 680 nm) of the Geostationary Ocean Color Imager (GOCI). With the conclusion of the GOCI mission in March 2021, this study aims to ensure the continuity of SSS estimation in the ECS by transitioning to its successor, the GOCI-II. This transition was facilitated through two approaches: applying the existing GOCI-based equation and introducing a new machine learning method using a random forest model. Our analysis demonstrated a high correlation between SSS estimates derived from the GOCI and GOCI-II when applying the equation developed for the GOCI to both satellites, as indicated by a robust R² value of 0.984 and a low RMSD of 0.8465 psu. This study successfully addressed the challenge of maintaining continuous SSS estimation in the ECS post-GOCI mission and evaluated the accuracy and limitations of the GOCI-II-derived SSS, proposing future strategies to enhance its effectiveness. Full article

In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world’s inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined the GOCI’s strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by Geostationary Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs. Full article

Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and remote sensing methods have been employed for Ulva detection, yet automatic and rapid Ulva detection remains challenging mainly due to complex observation scenarios present in different satellite images, and even within a single satellite image. Here, a reliable and fully automatic method was proposed for the rapid extraction of Ulva features using the Tasseled-Cap Greenness (TCG) index from satellite top-of-atmosphere reflectance (R_TOA) data. Based on the TCG characteristics of Ulva and Ulva-free targets, a local adaptive threshold (LAT) approach was utilized to automatically select a TCG threshold for moving pixel windows. When tested on HY1C/D-Coastal Zone Imager (CZI) images, the proposed method, termed the TCG-LAT method, achieved over 95% Ulva detection accuracy though cross-comparison with the TCG and VBFAH indexes with a visually determined threshold. It exhibited robust performance even against complex water backgrounds and under non-optimal observing conditions with sun glint and cloud cover. The TCG-LAT method was further applied to multiple HY1C/D-CZI images for automatic Ulva bloom monitoring in the Yellow Sea in 2023. Moreover, promising results were obtained by applying the TCG-LAT method to multiple optical satellite sensors, including GF-Wide Field View Camera (GF-WFV), HJ-Charge Coupled Device (HJ-CCD), Sentinel2B-Multispectral Imager (S2B-MSI), and the Geostationary Ocean Color Imager (GOCI-II). The TCG-LAT method is poised for integration into operational systems for disaster monitoring to enable the rapid monitoring of Ulva blooms in nearshore waters, facilitated by the availability of near-real-time satellite images. Full article

As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance ($R_{rs}\left(\lambda \right)$) products. Firstly, the GOCI $R_{rs}\left(\lambda \right)$ correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R² of 0.916, a root mean square error (RMSE) of 0.212 mg$·$m⁻³, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg$·$m⁻³. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg$·$m⁻³. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI’s potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations. Full article

The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (R_rc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R² = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R² = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters. Full article

The regional and global scale of aerosols in the atmosphere can be quantified using the aerosol optical depth (AOD) retrieved from satellite observations. To obtain reliable satellite AODs, conducting consistent validations and refining retrieval algorithms are crucial. AODs and Ångström exponents (AEs) measured with the aerosol robotic network (AERONET) are considered as the ground truth for satellite validations. AERONET AEs are used to collocate the wavelength of the AERONET AODs to those of the satellite AODs when there is a discordancy in their wavelengths. However, numerous validation studies have proposed different strategies by applying the AERONET AODs and AEs, and spatiotemporal collocation criteria. This study examined the impact of the wavelength and spatial collocation radius variations by comparing AODs at 550 nm derived from the geostationary ocean color imager (GOCI) with those obtained from the AERONET for the year 2016. The estimated AERONET AODs at 550 nm varied from 5.18% to 11.73% depending on the selection of AOD and AE, and the spatial collocation radii from 0 to 40 km, respectively. The longer the collocation radius and the higher the AODs, the greater the variability observed in the validation results. Overall, the selection of the spatial collocation radius had a stronger impact on the variability in the validation results obtained compared to the selection of the wavelength. The variability was also found in seasonal analysis. Therefore, it is recommended to carefully select the data wavelength and spatial collocation radius, consider seasonal effects, and provide this information when validating satellite AODs using AERONET. Full article

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. Full article

In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique in combining visible and infrared observations from the second geostationary ocean color imager (GOCI-II) and the advanced meteorological imager (AMI) over Asia and the Pacific Ocean. In this study, we demonstrate that AMI total precipitable water (TPW) data to allow realistic water vapor absorption correction of GOCI-II color retrievals for the ocean. We assessed the uncertainties of two candidate TPW products for GOCI-II atmospheric correction using atmospheric sounding data, and then analyzed the sensitivity of four ocean-color products (remote sensing reflectance [${\mathrm{R}}_{rs}$], chlorophyll-a concentration [CHL], colored dissolved organic matter [CDOM], and total suspended sediment [TSS]) for GOCI-II water vapor transmittance correction using AMI and global model data. Differences between the TPW sources increased the mean absolute percentage error (MAPE) of ${\mathrm{R}}_{rs}$ from 2.97% to 6.43% in the blue to green bands, higher than the global climate observing system requirements (<5%) at 412 nm. By contrast, MAPE values of 3.53%, 6.18%, and 7.71% were increased to 6.63%, 13.53%, and 16.14% at high sun and sensor zenith angles for CHL, CDOM, and TSS, respectively. Uncertainty analysis provided similar results, indicating that AMI TPW produced approximately 3-fold lower error rates in ocean-color products than obtained using TPW values from the National Centers for Environmental Prediction. These results imply that AMI TPW can improve the accuracy and ability of GOCI-II ocean-color products to capture diurnal variability. Full article