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Abstract Background Tuberculosis is an infectious disease influenced by social factors rather than a simple infectious disease. In this study, we investigated the relationship between tuberculosis rates and socioeconomic status. Methods This study was conducted using data of the 49,483 participants of the Korean National Health and Nutrition Examination Survey (KNHANES) VI–VIII (2013–2021). The relationships between tuberculosis rates and the quartiles of monthly household income and education level were examined using a multivariate logistic regression analysis. Results The KNHANES data revealed that the prevalence of tuberculosis as substantially related to monthly household income (odds ratio [OR], 6.0; 95% confidence interval [CI], 1.1–32.0 for lowest vs. highest incomes) and education level (OR, 3.8; 95% CI, 1.2–12.0 for 10–12 years vs. ≥13 years; OR, 4.1; 95% CI, 1.2–14.8 for ≤ 6 years vs. ≥13 years). Furthermore, current tuberculosis treatment was significantly related to monthly household income and education level. Conclusion There were substantial correlations between tuberculosis rates and socioeconomic status in South Korea.

Despite the increasing numbers of red tide events in the pristine subtropical ocean, a paucity of previous observations has limited understanding of harmful algae in the seas around the Korean Peninsula. Therefore, using six years (2012–2017) of Geostationary Ocean Color Imager (GOCI) satellite data, we characterized the red tides around Jeju Island, a volcanic island located near the paths of the Jeju Warm Current and Tsushima Warm Current, using the Normalized Red Tide Index (NRTI) method. The seawater around Jeju Island has for a long time been considered to be very clear, with relatively low suspended particulate matter concentrations and few harmful algae. Nonetheless, the satellite-based NRTI detection method used in this study detected and supported the existence of red tides in the coastal region around Jeju Island. Analysis of the red tide distribution showed that red tide first began to appear near the western coast of Jeju Island, then developed in the northern and eastern coastal regions, and finally vanished in the eastern coastal region. The monthly averages of the NRTI demonstrated a bloom event from April to May in every year. Additional fall blooms were detected in August–September, particularly in 2013 and 2016. The NRTI revealed strong interannual variations. The longest blooms occurred in 2015, and the most comprehensive and strongest event occurred in the spring of 2016. The latter three years (2015–2017) had much higher NRTI than the former three years (2012–2014). The probability of red tide occurrence at a given point during the 6-year study period revealed spatial differences. Relatively high probability of 0.3–0.5 was determined along the northern coastal region, whereas low probability of less than 0.2 was found along the southern region. Ground truth data also showed more frequent observations and higher red tide cell densities along the northern coast. Changes in NRTI in spring are positively correlated with changes in ENSO indices in winter. This study is the first to use a satellite-based approach with a vast long-term satellite database to elucidate the existence and probability of red tides near Jeju Island. We anticipate that this study will provide a useful strategy for remote monitoring of harmful algal blooms over wide regions using optical data.

A hazardous noxious substance (HNS) spill accident is one of the most devastating maritime disasters as it is accompanied by toxicity, fire, and explosions in the ocean. To monitor an HNS spill, it is necessary to develop a remote sensing–based HNS monitoring technique that can observe a wide area with high resolution. We designed and performed a ground HNS spill experiment using a hyperspectral sensor to detect HNS spill areas and estimate the spill volume. HNS images were obtained by pouring 1 L of toluene into an outdoor marine pool and observing it with a hyperspectral sensor capable of measuring the shortwave infrared channel installed at a height of approximately 12 m. The pure endmember spectra of toluene and seawater were extracted using principal component analysis and N-FINDR, and a Gaussian mixture model was applied to the toluene abundance fraction. Consequently, a toluene spill area of approximately 2.4317 m2 was detected according to the 36% criteria suitable for HNS detection. The HNS thickness estimation was based on a three-layer two-beam interference theory model. Because toluene has a maximum extinction coefficient of 1.3055 mm at a wavelength of 1,678 nm, the closest 1,676.5 nm toluene reflectance image was used for thickness estimation. Considering the detection area and ground resolution, the amount of leaked toluene was estimated to be 0.9336 L. As the amount of toluene used in the actual ground experiment was 1 L, the accuracy of our estimation is approximately 93.36%. Previous studies on HNS monitoring based on remote sensing are lacking in comparison to those on oil spills. This study is expected to contribute to the establishment of maritime HNS spill response strategies in the near future based on the novel hyperspectral HNS experiment.

Typhoons, such as tropical cyclones, can produce a variety of ocean responses through drastic changes in atmospheric and oceanic environments. However, the uncertainty in satellite salinity data increases during the passage of a typhoon and may limit its potential application. To investigate whether the satellite salinity data can explain oceanic responses to typhoons in the Northwest Pacific, we validated the satellite salinity using Argo float data for the past decade (2011–2020). The Soil Moisture Active Passive (SMAP) and Aquarius salinity were relatively accurate in subtropical regions at low latitudes under high sea surface temperature conditions in summer. This demonstrates the validity of the satellite salinity data in typhoon studies. We analyzed the oceanic responses to 20 representative typhoons over the past decade. Both the Aquarius and SMAP satellites observed a decrease in the SSS on the left side of the typhoon in contrast to the high salinity on the right side of the typhoon. The locations of SSS freshening coincided with those of higher precipitation to the left of the typhoon centers. We also observed that the higher the precipitation rate, the lower the satellite salinity. The ratio of the salinity freshening to the precipitation rate was significant at approximately –0.0401 psu mm-1 h-1. Changes in the vertical profiles of the Argo data supported this partial freshening of salinity as well as the characteristic surface cooling and deepening of the mixed layer after the passage of the typhoon. We further demonstrated that the atmospheric environments in a rotated coordinate system along the typhoon paths showed clear salinity freshening in the forward and slightly left sides of the typhoon center. The spatial distinction of the wind and precipitation fields along the typhoon paths induced the characteristic synoptic response of salinity prior to the arrival of each typhoon. Our results provide reasonable observational evidence of oceanic responses to typhoons in the Northwest Pacific and contribute to the understanding of atmospheric and oceanic processes related to tropical storms.

In this study, to determine the spatiotemporal variability of satellite-observed chlorophyll-a (Chl-a) concentrations in the East Sea (Japan Sea, EJS), monthly composite images were generated via noise processing using Level-2 MODIS Chl-a data from 2003 to 2020. Harmonic analysis was performed on time-series Chl-a data to present the spatial distribution of seasonal and intraseasonal variability with 1–4 cycles per year. In the EJS, seasonal cycles contributed less than approximately 30% to the total variance in Chl-a variability, indicating the existence of dominant interannual variability. Analysis of the temporal trend in Chl-a concentrations showed that they increased (< 0.06 mg m–3 yr–1) in most of the EJS over almost two decades (2003–2020). In recent years, in the areas showing positive trends in Chl-a, it tended to increase with time, especially in the northern part of the EJS. As a result of examining the trend associated with the physical environment that affects the long-term trend in Chl-a concentrations, sea surface temperature (SST) trends were mostly increased. The wind speeds showed a characteristic strengthening trend in the northeastern part of the EJS and the North Korean coast. Long-term changes in wind direction indicated strengthening of the northerly wind components on the Russian coast and the westerly components on the eastern coast of the Korean Peninsula. These wind changes were closely related to the Arctic Oscillation (AO) index variability in relation to the recent warming of the Arctic Ocean. When the AO index was greater than 1, the wind speed tended to be weakened and the SSTs showed a tendency to increase. This led to general increasing responses in Chl-a concentrations during positive AO. The summer SST anomaly revealed an inverse relationship between higher positive values during the La Niña period and lower ones during the El Niño period. When the amplitude of MEI (Multi-variate ENSO Index) was high (| MEI| > 1), the SST anomaly indicated an inverse correlation with the Chl-a concentration anomaly in the EJS. This study demonstrated the regional effects of climate change on Chl-a variability in the EJS in response to tropical–subtropical and arctic–subarctic interactions between ocean and atmospheric variations.

Extreme value analysis (EVA) has been extensively used to understand and predict long-term return extreme values. This study provides the first approach to EVA using satellite-observed sea surface temperature (SST) data over the past decades. Representative EVA methods were compared to select an appropriate method to derive SST extremes of the East/Japan Sea (EJS). As a result, the peaks-over-threshold (POT) method showed better performance than the other methods. The Optimum Interpolation Sea Surface Temperature (OISST) database was used to calculate the 100-year-return SST values in the EJS. The calculated SST extremes were 1.60–3.44°C higher than the average value of the upper 5th-percentile satellite-observed SSTs over the past decades (1982–2018). The monthly distribution of the SST extremes was similar to the known seasonal variation of SSTs in the EJS, but enhanced extreme SSTs exceeding 2°C appeared in early summer and late autumn. The calculated 100-year-return SSTs were compared with the simulation results of the Coupled Model Intercomparison Project 5 (CMIP5) climate model. As a result, the extreme SSTs were slightly smaller than the maximum SSTs of the model data with a negative bias of –0.36°C. This study suggests that the POT method can improve our understanding of future oceanic warming based on statistical approaches using SSTs observed by satellites over the past decades.

The biological carbon pump in the southwestern East Sea (Japan Sea, EJS hereafter) was investigated based on examination of sinking particulate matter samples intercepted by bottom-tethered sediment traps deployed on a mooring at three depths (500, 1000, and 2000 m) between 2011 and 2017. The total particle flux increased as the sampling depth increased, while particulate organic carbon (POC) flux was greatest at 500 m. The lithogenic material content was high at all depths, and accounted for an average of ∼42% of the particulate matter. The total particle flux at all sampling depths consistently shifted toward much higher values in 2014–2016. During this period, the POC flux at 500 m increased by 32% while net primary production (NPP) increased only slightly. Consequently, the POC flux/NPP ratio increased significantly, indicating greater biological pump efficiency than in earlier years of the study. The flux of lithogenic material derived primarily from sediment resuspension was much greater at 500 m in 2014−2016 compared with previous years, implying its potential role as a ballast mineral in enhancing particle export and transfer to the deep sea interior. The radiocarbon isotope ratio of POC was higher, and the excess Mn content values were lower at 500 m in 2014–2016, suggesting that the resuspended sediment at 500 m likely originated from a shallow region during this period, and differed in provenance from the lithogenic material intercepted at 2000 m. The period of enhanced particle flux coincided with the 2015/16 El Niño and a mesoscale warm (anticyclonic) eddy that persisted for 2 years in the study region. The East Korean Warm Current and the eddy may have facilitated the transport of resuspended particles entrained in the Korea Strait and/or the western shelf and upper slope of the basin to the study site.

The estimation of extreme ocean wave heights is important for understanding the ocean’s response to long-term changes in the ocean environment and for the effective coastal management of potential disasters in coastal areas. In order to estimate extreme wave height values in the Northwest Pacific Ocean, a 100-year return period were calculated by applying a Peak over Threshold (PoT) method to satellite altimeter SWH data from 1992 to 2016. Satellite altimeter SWH data were validated using in situ measurements from the Ieodo Ocean Research Station (IORS) south of Korea and the Donghae buoy of the Korea Meteorological Administration (KMA) off the eastern coast of Korea. The spatial distribution and seasonal variations of the estimated 100-year return period SWHs in the Northwest Pacific Ocean were presented. To quantitatively analyze the suitability of the PoT method in the Northwest Pacific, where typhoons frequently occur, the estimated 100-year return period SWHs were compared by classifying the regions as containing negligible or significant typhoon effects. Seasonal variations of extreme SWHs within the upper limit of 0.1% and the PoT-based extreme SWHs indicated the effect of typhoons on the high SWHs in the East China Sea and the southern part of the Northwest Pacific during summer and fall. In addition, this study discusses the limitations of satellite altimeter SWH data in the estimation of 100-year extreme SWHs.

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

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

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

Validation of daily Optimum Interpolation Sea Surface Temperature (OISST) data from 1982 to 2018 was performed by comparison with quality-controlled in situ water temperature data from Korea Meteorological Administration moored buoys and Korea Oceanographic Data Center observations in the coastal regions around the Korean Peninsula. In contrast to the relatively high accuracy of the SSTs in the open ocean, the SSTs of the coastal regions exhibited large root-mean-square errors (RMSE) ranging from 0.75 K to 1.99 K and a bias ranging from −0.51 K to 1.27 K, which tended to be amplified towards the coastal lines. The coastal SSTs in the Yellow Sea presented much higher RMSE and bias due to the appearance of cold water on the surface induced by vigorous tidal mixing over shallow bathymetry. The long-term trends of OISSTs were also compared with those of in situ water temperatures over decades. Although the trends of OISSTs deviated from those of in situ temperatures in coastal regions, the spatial patterns of the OISST trends revealed a similar structure to those of in situ temperature trends. The trends of SSTs using satellite data explained about 99% of the trends in in situ temperatures in offshore regions (>25 km from the shoreline). This study discusses the limitations and potential of global SSTs as well as long-term SST trends, especially in Korean coastal regions, considering diverse applications of satellite SSTs and increasing vulnerability to climate change.

As marine transportation has increased in coastal regions, maritime accidents associated with vessels have steadily increased. Remotely sensed satellite or airborne images can aid rapid vessel monitoring over wide areas at high resolutions. In this study, airborne hyperspectral experiments were performed to detect marine vessels mainly including fishing boat and yacht by applying pixel-based mixture techniques and to estimate the size of the vessels through an objective ellipse fitting method. Various spectral libraries of marine objects and seawaters were constructed through in-situ experiments for spectral analysis of the internal structures of vessels. The hyperspectral images were dimensionally reduced through principal component analysis. Several hyperspectral mixture algorithms, such as N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA), were used for the detection of vessels. The N-FINDR and VCA techniques presented a total of 14 vessels, the ICA technique detected seven vessels, and the PPI technique detected two vessels. The pixel-based probability of detection (POD) and false alarm ratio (FAR) for all 14 vessels were 96.40% and 4.30%, respectively. The sizes of the vessels were estimated by extracting the boundaries of the vessels through a two-dimensional gradient and applying the ellipse fitting method. Compared with the digital mapping camera (DMC) images with resolutions of 0.10 m, the root-mean-square errors of the length and width of the vessels were approximately 1.19 m and 0.81 m, respectively. The application of spectral mixing methods provided a high probability of detecting the objects, as well as the overall structures of the decks of the vessels.

In this study, seven, global, blended, sea surface temperature (SST) analyses, including Operational SST and Sea Ice Analysis (OSTIA), Canadian Meteorological Centre (CMC) analysis, Optimum Interpolation SST (OISST), Remote Sensing System (REMSS) analysis, Multi-scale Ultra-high Resolution SST (MURSST), Merged Satellite and In situ Data Global Daily SST (MGDSST), and Geo-Polar Blended SST (Blended SST) were conducted. In-situ temperature measurements were used for the years 2014–2018, from 35 narrowly-spaced buoys distributed along the Korean Peninsula coast, to investigate how well the SST analyses represent the temperatures at the coastal regions. Contrary to the overall accuracy of the SSTs in the global ocean and offshore regions, the root-mean-square errors for the analyses were relatively large over 1.27 K. Specifically, all SST analyses resulted in warm biases over 0.31 K, which became quite distinctive in the western and the southwestern coastal regions. Investigation of the errors identified relationships with the coastal zones of vigorous tidal mixing, shallow bathymetry, and absence of microwave measurements. Overall, temporal wavelet coherency between in-situ measurements and SST products revealed high coherency of greater than 0.8 in periods longer than 180 days, however, low coherency (

The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19−29% and 37−49%, respectively, in June.

Purpose: Differing lifestyle, nutritional, and genetic factors may lead to a differing stiffness index (SI) determined by quantitative ultrasound in elderly men and women. The purpose of this study was to determine SI and the gender-specific factors associated with low SI in a Korean elderly cohort. Methods: This was a cross-sectional descriptive study identifying the gender-specific factors related to SI in 252 men and women aged 65 years and greater from local senior centers in Seoul, Korea between January and February 2009. Results: The mean SI of elderly men was significantly higher than that of the women's. A multiple regression analysis reveals that age, nutritional status, and physical activity were predictive factors of lower SI in men, whereas age, alcohol consumption, educational level, and genetic polymorphism were predictive factors for elderly women. Conclusions: Low SI was common in both elderly men and women. We found gender differences in factors linked to low SI. In multiple regression analysis, nutritional status and physical activity were more important factors in men, whereas alcohol consumption, educational level, and genetic polymorphism were significant factors predicting low SI in women. Gender-specific modifiable risk factors associated with low SI should be considered when developing osteoporosis prevention programs for the elderly.

High-resolution sea surface temperature (SST) images are essential to study the highly variable small-scale oceanic phenomena in a coastal region. Most previous SST algorithms are focused on the low or medium resolution SST from the near polar orbiting or geostationary satellites. The Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) makes it possible to obtain high-resolution SST images of coastal regions. This study performed a matchup procedure between 276 Landsat 8 images and in-situ temperature measurements of buoys off the coast of the Korean Peninsula from April 2013 to August 2017. Using the matchup database, we investigated SST errors for each formulation of the Multi-Channel SST (MCSST) and the Non-Linear SST (NLSST) by considering the satellite zenith angle (SZA) and the first-guess SST. The retrieved SST equations showed a root-mean-square error (RMSE) from 0.59 to 0.72 °C. The smallest errors were found for the NLSST equation that considers the SZA and uses the first-guess SST, compared with the MCSST equations. The SST errors showed characteristic dependences on the atmospheric water vapor, the SZA, and the wind speed. In spite of the narrow swath width of the Landsat 8, the effect of the SZA on the errors was estimated to be significant and considerable for all the formations. Although the coefficients were calculated in the coastal regions around the Korean Peninsula, these coefficients are expected to be feasible for SST retrieval applied to any other parts of the global ocean. This study also addressed the need for high-resolution coastal SST, by emphasizing the usefulness of the high-resolution Landsat 8 OLI/TIRS data for monitoring the small-scale oceanic phenomena in coastal regions.

Long-term trends of sea surface temperature (SST) of the East Sea (Sea of Japan, EJS) were estimated by using 37-year-long satellite data, for the observation period from 1982 to 2018. Overall, the SST tended to increase with time, for all analyzed regions. However, the warming trend was steeper in the earlier decades since the 1980s and slowed down during the recent two decades. Based on the analysis of the occurrence of events with extreme SST (high in the summertime and low in the wintertime), a shift toward the more frequent occurrence of events with extremely high SST and the less frequent occurrence of events with extremely low SST has been observed. This supports the observations of the consistent warming of the EJS. However, seasonal trends revealed continuous SST warming in the summertime, but frequent extreme SST cooling in the wintertime, in recent decades. The observed reduction in the warming rates occurred more frequently in specific regions of the EJS, where the occurrence frequency of events with extremely low SST was unusually high in the recent decade. The recent tendency toward the SST cooling was distinctively connected with variations in the Arctic Oscillation index. This suggests that changes in the Arctic Ocean environment likely affect the recently observed SST changes in the EJS, as one of the marginal seas in the mid-latitude region far from the polar region.

Korea’s first geostationary satellite, the “Communication, Ocean, and Meteorological Satellite„ (COMS), has been operating since 2010. The Meteorological Imager (MI), an sensor on-board the COMS, has observed sea-surface radiances for the estimation of sea surface temperature (SST) in the western Pacific Ocean and eastern Indian Ocean. To derive the SST coefficients of COMS, quality-controlled surface drifting buoy data were collected for the period of April 2011 to March 2015. A collocation procedure between COMS/MI data and the surface drifter data produced a matchup database for 4 years from 2011 to 2015. The coefficients for the COMS/MI SST were derived by applying appropriate algorithms, i.e., the Multi-channel SST (MCSST) and Non-linear SST (NLSST) algorithms, for daytime and nighttime data using a regression method. Validation results suggest the possibility of the continuous use of the coefficients as representative SST coefficients of COMS. The estimated SSTs near the edge of a full disk with high satellite zenith angles over 60° revealed relatively large errors compared to drifter temperatures. Most of NLSST formulations exhibited overestimation of SSTs at low SSTs (

In this study, variations of radar backscatter features of the tidal channel in Gyeonggi Bay in the Eastern Yellow Sea were investigated using spaceborne synthetic aperture radar (SAR) images. Consistent quasi-linear bright features appeared on the SAR images. Examining the detailed local bathymetry chart, we found that the features were co-located with the major axis of the tidal channel in the region. It was also shown that modulation of the radar backscatter features changed according to the environmental conditions at the time of imaging. For the statistical analysis, the bathymetric features over the tidal channel were extracted by an objective method. In terms of shape, the extracted features had higher variability in width than in length. The analysis of the variation in intensity with the coinciding bathymetric distribution confirmed that the quasi-linear bright features on the SAR images are fundamentally imprinted due to the surface current convergence and divergence caused by the bathymetry-induced tidal current variation. Furthermore, the contribution of environmental factors to the intensity modulation was quantitatively analyzed. A comparison of the variation in normalized radar cross section (NRCS) with tidal current showed a positive correlation only with the perpendicular component of tidal current (r= 0.47). This implies that the modulation in intensity of the tidal channel signatures is mainly affected by the interaction with cross-current flow. On the other hand, the modulation of the NRCS over the tidal channel tended to be degraded as wind speed increased (r= −0.65). Considering the environmental circumstances in the study area, it can be inferred that the imaging capability of SAR for the detection of tidal channel signatures mainly relies on wind speed.