Geostationary Ocean Color Imager Research Articles

Progress in Current Research and Applications of the Geostationary Ocean Color Imager Series (GOCI and GOCI-II): A Bibliometric Analysis

Progress in Current Research and Applications of the Geostationary Ocean Color Imager Series (GOCI and GOCI-II): A Bibliometric Analysis

Monitoring the Vertical Variations in Chlorophyll-a Concentration in Lake Chaohu Using the Geostationary Ocean Color Imager

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) 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 Rrs (Rrs_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 Rrs data (Rrs_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 (AFAIRrs), 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 AFAIRrs (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 Rrs_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.

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Continuity and Enhancements in Sea Surface Salinity Estimation in the East China Sea Using GOCI and GOCI-II: Challenges and Further Developments

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 (Rrs) 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.

Improving GOCI ocean color data under high solar-zenith angle over open oceans using neural networks

ABSTRACT With hourly measurements available during daytime between local times of 09:00–16:00, ocean color data derived from the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological (COMS) satellite have been useful for research and surveillance of diurnal processes in the western Pacific Ocean region. However, in early morning and late afternoon measurements, there are significant errors in GOCI-derived ocean color products as the solar-zenith angle (θ 0 ) goes beyond 70°, especially in autumn and winter seasons. In this study, we employ a neural network (NN) model to make corrections on the GOCI-measured normalize water-leaving radiance spectra, nL w (λ), with high θ 0 (>70°) in open oceans. Results show that NN-corrected nL w (λ) are consistent with the previous-hour nL w (λ) and make the diurnal variations in the region much more stable and reasonable. Specifically, the GOCI-measured nL w (λ) with θ 0 ≤65° in earlier hours of the day (including some nL w (λ) diurnal variation) are considered accurate and used as the ground truth to train NN models for nL w (λ) correction with high θ 0 . Further analysis of the relationship of ratios (between the NN-corrected and original nL w (λ)) with θ 0 shows that the nL w (λ) ratios increase as θ 0 increase, which indicates that there are more significant corrections with larger θ 0 (>70°). The performance evaluation of the NN models is based on the comparison of NN-corrected nL w (λ) with the original previous hour nL w (λ) data. The ratios of NN-corrected nL w (λ) to the original previous hour nL w (λ) are 0.968–1.045 for the short blue/blue and green bands, and the performance of nL w (λ) correction at 14:00 and 15:00 is slightly better than that at 16:00, due to significantly large θ 0 at late afternoon hours. The NN-corrected nL w (λ) data are also used to derive chlorophyll-a (Chl-a) concentration, showing significantly improved Chl-a in GOCI’s late afternoon measurements with θ 0 >70°.

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures.

GOCI operation during the 10 years of sun interference in COMS

A practical approach has been studied to cope with the sun interference of Earth observation geostationary satellite and the results of 10-year satellite operation for preventing sun outage are presented with detailed analysis in this paper. The Communication Ocean Meteorological Satellite (COMS) is equipped with the Geostationary Ocean Color Imager (GOCI), which performs ocean observation mission in the geostationary orbit. After the launch of the COMS, this study was initiated to prevent image data loss due to sun interference in receiving GOCI image data at the ground station of the principle user site under the operation configuration of single satellite and single ground station. This paper fully covers the entire research and the operation process which have been conducted over the 10 years from 2011 to 2021, including unexpected changes of operational environment such as ground station antenna change and satellite longitude position change. The specific characteristics of sun interference on the COMS operation is analysed through the theoretical simulation on the strength and the occurrence time of sun interference affecting the GOCI image data reception. The simulation outcomes are used to specify trigger level, date and time of sun outage in the COMS operation. From the concept that the sun outage could be prevented by adjusting the GOCI imaging time in advance, a GOCI special operation plan was established for the practical way of the sun outage prevention that was optimized for the COMS mission and could be applied to actual satellite operation. The approach of this study is verified by confirming the success of the sun outage prevention after the execution of the GOCI special operation plan. The verification is based on the operation results of the satellite link Radio Frequency (RF) signal performance and the image reception success status which has been obtained during the 10 years of the COMS operation. And, this study shows good consistency between the simulation outcomes and the operational measurements as well as the operational flexibility that it is possible to cope with the sun outage successfully even in the unexpected operational changes during the 10 years.

Application of deep learning in predicting suspended sediment concentration: A case study in Jiaozhou Bay, China

Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harnessed Convolutional Neural Networks (CNNs) to create a deep learning model, which has been applied to the remote sensing data procured from the Geostationary Ocean Color Imager (GOCI) spanning April 2011 to March 2021. Our research indicates that on a small time scale, wind and hydrodynamic forces both have a significant impact on the prediction results of CNNs model. Considering both wind and hydrodynamic forces can effectively improve the model's prediction efficiency for SSC. Moreover, we have employed CNNs to interpolate absent values within the remote sensing datasets, yielding enhancements superior to those attained via linear or multivariate regression techniques. Finally, the correlation coefficient between CNN-derived SSC estimates for Jiaozhou Bay (JZB) and its corresponding remote sensing data is 0.72. Correlation coefficient and root mean square error differ in different regions. In the shallow water of JZB, due to water level changes, there is limited data, and the correlation coefficient in this area is about 0.5–0.6. In the central region of JZB with sufficient data, the correlation coefficient is generally higher than 0.75. Therefore, we believe that this CNNs model can be used to predict the hourly variation of SSC. When juxtaposed with alternative methodologies, the CNN approach is found to economize computational resources and enhance processing efficiency.

Ocean Colour Atmospheric Correction for Optically Complex Waters under High Solar Zenith Angles: Facilitating Frequent Diurnal Monitoring and Management

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρrc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R2 ≥ 0.90, RMSD ≤ 0.0034 sr−1); (ii) the model had a high validation accuracy with an independent dataset (R2 = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day.

Quantitative Retrieval of Chlorophyll-a Concentrations in the Bohai–Yellow Sea Using GOCI Surface Reflectance Products

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 (Rrsλ) products. Firstly, the GOCI Rrsλ 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.

Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes

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 (Rrc) 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.

Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

An improved sea surface salinity retrieval algorithm for the Chinese Bohai Sea based on hyperspectral reconstruction and its applicability analysis

Salinity is regarded as a critical factor in marine systems. It is essential to monitor the long-term changes in sea surface salinity (SSS), as well as its short-term changes. Along these lines, in this work, to monitor the hourly, daily, and monthly changes in SSS in the Chinese Bohai Sea from GOCI (Geostationary Ocean Color Imager) images, the feasibility of adopting a MERIS-based SSS retrieval algorithm was systematically explored to develop a GOCI-based SSS retrieval algorithm. To avoid the influence of the atmospheric correction error on Rrs(λ) of the similar bands of MERIS (490 nm, 560 nm, and 665 nm) and those of GOCI (490 nm, 555 nm, and 660 nm), a simple linear regression model was applied based on the ρrs(λ) matchup of the similar bands by using a hyperspectral reconstruction method with the measured ρ(λ). Despite the aerosol-based scattering errors, a more reasonable result (R2 = 0.86, RMSE = 0.79 psu) over a long time scale was gained by the improved SSS retrieval algorithm, which was validated by the measured data (R2 = 0.92, RMSE = 0.72 psu). On top of that, the temporal and spatial distribution of SSS, as well as its diurnal, daily, and monthly changes, were then obtained in the Chinese Bohai Sea from April 2011 to March 2012. The impact of runoff on SSS was also analysed based on instant and lag correlations.

Characterization of ocean color retrievals and ocean diurnal variations using the Geostationary Ocean Color Imager (GOCI)

Using measurements from the Geostationary Ocean Color Imager (GOCI) on the Communication, Ocean, and Meteorological Satellite (COMS), we characterize and quantify some advantages and applications of the satellite geostationary measurements, compared with those of the polar-orbiting satellites. With eight daily measurements in the western Pacific Ocean, the average GOCI daily coverage of ocean color retrievals reached ∼43.81%, compared to ∼21.77% from the Visible Infrared Imaging Radiometer Suite (VIIRS). The GOCI-measured ocean property data, such as normalized water-leaving radiance [nLw(λ)] and diffuse attenuation coefficient at 490 nm [Kd(490)], show significant diurnal variability over the region, particularly over highly turbid coastal regions. It is noted that GOCI-derived nLw(λ) spectra and Kd(490) in the region have been compared with those from VIIRS in 2012, 2016, and 2019, and shown consistent results (within ∼5–10%). Using the GOCI daily statistical results from its hourly data, i.e., standard deviations (STDs) and coefficient of variations (CVs) of nLw(λ) and Kd(490), the diurnal variability has been demonstrated and quantified. Over turbid coastal regions, STDs and CVs of nLw(λ) at GOCI 555 and 660 nm bands are important for characterizing diurnal variability, while for open oceans STDs and CVs of nLw(λ) at 412, 443, and 555 nm are useful. Specifically, using the four GOCI daily examples in 2019 on January 24, May 21, August 16, and November 15, we have carried out quantitative analyses for understanding regional diurnal variations. In addition, using the GOCI measurements from 2011 to 2020 in the region, hourly climatology Kd(490) data in four seasons and quantitative results are derived and presented. Results show important characteristics in ocean diurnal variabilities over this highly dynamic and complex western Pacific Ocean region. Indeed, the regional diurnal variability has strong spatial and temporal (seasonal) dependences, which are mostly related to regional ocean optical and bio-optical properties.

A novel quantitative analysis for diurnal dynamics of Ulva prolifera patch in the Yellow Sea from Geostationary Ocean Color Imager observation

IntroductionIn the last decade, the outbreak of large-scale green tides caused by Ulva prolifera has continuously occurred in the Yellow Sea. Satellite remote sensing techniques have been widely used to monitor the distribution area and duration of green tides due to their advantages of their large-area synchronous observation. Ulva prolifera in the Yellow Sea is mainly distributed in bands or large patches during its flourishing stage. Previous studies have rarely reported the quantitative analysis of a single Ulva prolifera patch and its changes in the short term.MethodsConsidering the high temporal resolution of the Geostationary Ocean Color Imager (GOCI) sensor and the patchy distribution of Ulva prolifera floating on the sea surface, we developed a feasible method for monitoringUlva prolifera by performing clustering analysis with density-based spatial clustering of applications with noise (DBSCAN) to capture the diurnal variation characteristics of a single Ulva prolifera patch.ResultsThis new approach was used to extract informationfrom a single Ulva prolifera patch in the Yellow Sea in 2012 and 2017. The results showed that during the time of GOCI imaging, the tidal current was the main factor driving the drift of Ulva prolifera, and the drifting direction of Ulva prolifera was consistent with the direction of the local tidal current, with a coefficient of determination of 0.94.DiscussionBy changing the normalized difference vegetation index (NDVI) threshold, further more accurate atmospheric correction (AC) of GOCI data during the twilight periods was indirectly achieved. By comparing the areal change in the single patch before and after AC, we speculated that the daily change in signal intensity received by the GOCI sensor may be the main reason for the diurnal variation in the Ulva proliferacoverage area. The results showed the details of the diurnal variation in Ulvaprolifera patches in the dynamic marine environment, and the main reason that may cause this variation was speculated.

Study on the mechanisms of interannual variation in suspended sediment concentration in the Bohai Sea based on GOCI

The launch of the Geostationary Ocean Color Imager (GOCI) satellite has made it possible to observe suspended sediment concentration (SSC) in the Bohai Sea (BHS) over a long time and a large area. We utilized remote sensing data from GOCI from 2011 to 2021 to study the spatiotemporal variation characteristics of SSC in the BHS and identified the contribution of each oceanic element to the changes in SSC. Our findings indicate that SSC is high in winter and low in summer, and related to the southeastward winds in winter (>7 m/s) and northwestward winds in summer (<4 m/s), with a correlation coefficient of 0.634. The average annual increase in SSC is 0.07 g/m3 (an increase of 1.2% per year). In terms of spatial distribution, SSC is highest in Bohai Bay and Laizhou Bay, followed by the Liaodong Bay, and is greater than that in the Central Bohai Sea, which is related to multiple river estuaries in the three bays. Additionally, the Bohai and Liaodong Bay in the BHS are covered by sea ice during winter, which affects the response of waves and currents to wind and changes the ocean current field, thereby affecting the resuspension and transport of sediment. Our EOF analysis revealed that wind-induced changes are the dominant factors in the variations of SSC, while changes in river runoff, sediment discharge, and sea ice cover are also important factors influencing SSC changes.

Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network

Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network

GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison

The Geostationary Ocean Color Imager II (GOCI-II) deployed on board GEO-KOMPSAT 2B (GK2B) satellite can observe the earth with high frequency, which is of great significance to the dynamic monitoring of regional aerosol optical depth (AOD). However, the lack of short-wave infrared bands makes the classical methods based on radiative transfer analysis difficult to achieve retrieval of aerosols. The data-driven methods based on learning from massive samples can achieve high-accuracy retrieval of aerosols on the local, regional and global scale. However, the samples required for the data-driven method are difficult to obtain due to the short operation time of the GK2B satellite and insufficient ground-based measurements. Aiming to solve such a problem, the Simplified and Robust Surface Reflectance Estimation and Simplified Aerosol Retrieval Algorithm (SEMARA) were aggregated to be applied in the study. The SEMARA method using ground-based observation can retrieve local AOD with high reliability and accuracy, which can be used to construct high-quality and high-quantity samples for training with higher efficiency compared with traditional sample construct methods. Four data-driven methods: Fully Connected Neural Network (FCNN), Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost), were chosen to retrieve the AOD over South Korea in 2021 and 2022 based on GOCI-II. The results were validated and compared using ground-based measurements from the Aerosol Robotic Network (AENRONET) sites and show that the FCNN method has high accuracy and stability, followed by, the RF and XGBoost methods, while the SVM results significantly overestimate the spatial distribution of the AOD.

Contributions of meteorology and nutrient to the surface cyanobacterial blooms at different timescales in the shallow eutrophic Lake Taihu

Quantitative assessments of the contributions of various environmental factors to cyanobacterial blooms at different timescales are lacking. Here, the hourly cyanobacterial bloom intensity (CBI) index, a proxy for the intensity of surface cyanobacterial biomass, was obtained from the geostationary satellite sensor Geostationary Ocean Color Imager (GOCI) over the years 2011–2018. Generalized additive model was applied to determine the responses of monthly and hourly CBI to the perturbations of meteorological factors, water stability and nutrients, with variation partitioning analysis used to analyze the relative importance of the three groups of variables to the inter-monthly variation of diurnal CBI in each season. The effects of environmental factors on surface cyanobacterial blooms varied at different timescales. Hourly CBI increased with increasing air temperature up to 18 °C but decreased sharply above 18 °C, whereas monthly CBI increased with increasing air temperature up to 30 °C and stabilized thereafter. Among all the environmental factors, air temperature had the largest contribution to the intra-daily variation in CBI; water stability had the highest explanation rate for the inter-monthly variation of diurnal CBI during summer (42.3 %) and autumn (56.9 %); total phosphorus explained the most variation in monthly CBI (18.5 %). Compared with cyanobacterial biomass (CB) in the water column, high light and low wind speed caused significantly lower CBI in July and higher CBI in November respectively. Interestingly, cyanobacterial blooms at the hourly scale were aggravated by climate warming during winter and spring but inhibited during summer and autumn. Collectively, this study reveals the effects of environmental factors on surface cyanobacterial blooms at different timescales and suggests the consideration of the hourly effect of air temperature in short-term predictions of cyanobacterial blooms.