• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1255-1272
• /
• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1651-1669
• /
• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Korean Journal of Ecology and Environment
• /
• v.45 no.4
• /
• pp.474-481
• /
• 2012

This study was performed to investigate the degree of long-term pollution at the mainstream of the Han River by comparing the concentration of BOD and COD from 1975 to 2011. The long-term annual average BOD and COD concentration at the mainstream of the Han River showed an increasing trend as it flowed downstream from Paldang Dam to Gayang. The concentration of BOD ($r^2$=0.646) and COD ($r^2$=0.260) showed a consistent decreasing trend for 37 years. In the case of Paldang Dam, BOD has maintained a decreasing trend, whereas the COD value showed an increasing trend after the 1990s. Therefore, a control of non-biodegradable materials in areas around Paldang Dam is required. The result of the seasonal variations of BOD and COD is as follows: spring>winter>summer and fall (p<0.001). The time series analysis revealed a strong correlation for every 12-month period. Also, the amount of water discharge at Paldang Dam has to be systematically controlled because the amount of water discharge from the dam influences the water quality at the mainstream of the Han River.

• Journal of the Korean earth science society
• /
• v.40 no.6
• /
• pp.613-623
• /
• 2019

Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.11 no.2
• /
• pp.68-81
• /
• 2006

As one of the on-going projects to investigate the biogeochemical characteristics of tidal flat, we develop seasonal mass balance calculations (or DIP, DIN and DON in Gomso Bay. We have obtained 13-hours time-series data of salinity, tidal current, nutrients, and chlorophyll-a of seawater for spring, dry summer, rainy summer and winter during $1999{\sim}2000$. DIP of $-1.10{\times}10^6g\;P\;day^{-1},\;-4.50{\times}10^5g\;P\;day^{-1}$ was out-fluxed from the bay to the bay proper for spring and dry summer, respectively. Whereas $1.06{\times}10^4g\;P\;day^{-1}$ of net influx of DIP was found during winter and $2.72{\times}10^6g\;P\;day^{-1}$ of net influx was also found during the rainy summer. Therefore we suggest the role of Gomso tidal flat as a source of DIP fur the seasons of spring and summer, but as an opposite role during the rainy summer and winter but much smaller in magnitude. Except winter, the advection process by tidal current is found the most dominant flux among the diverse fluxes of DIP in the bay. Whereas ground water is estimated as the strongest flux of TDN except winter. TDN of $1.38{\times}10^7g\;N\;day^{-1},\;2.45{\times}10^6g\;N\;day^{-1},\;and\;4.65{\times}10^7g\;N\;day^{-1}$ was in-fluxed to the bay from the bay proper far spring, rainy summer and summer, respectively. Only $-1.70{\times}10^7g\;N\;day^{-1}$ of net out-flux was found during the winter. Therefore we suggest the role of Gomso tidal flat as a sink of TDN far the year round except winter.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.543-557
• /
• 2021

Because aerosols have different spectral characteristics according to the size and composition of the particle and to the satellite sensors, a comparative analysis of aerosol products from various satellite sensors is required. In South Korea, however, a comprehensive study for the comparison of various official satellite AOD (Aerosol Optical Depth) products for a long period is not easily found. In this paper, we aimed to assess the performance of the AOD products from MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), Himawari-8, and Sentinel-3 by referring to the AERONET (Aerosol Robotic Network) sun photometer observations for the period between January 2015 and December 2019. Seasonal and geographical characteristics of the accuracy of satellite AOD were also analyzed. The MODIS products, which were accumulated for a long time and optimized by the new MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm, showed the best accuracy (CC=0.836) and were followed by the products from VIIRS and Himawari-8. On the other hand, Sentinel-3 AOD did not appear to have a good quality because it was recently launched and not sufficiently optimized yet, according to ESA (European Space Agency). The AOD of MODIS, VIIRS, and Himawari-8 did not show a significant difference in accuracy according to season and to urban vs. non-urban regions, but the mixed pixel problem was partly found in a few coastal regions. Because AOD is an essential component for atmospheric correction, the result of this study can be a reference to the future work for the atmospheric correction for the Korean CAS (Compact Advanced Satellite) series.

• Journal of the Korean earth science society
• /
• v.41 no.6
• /
• pp.617-629
• /
• 2020

Sea surface temperature (SST), which plays an important role in climate change and global environmental change, can be divided into skin sea surface temperature (SSST) observed by satellite infrared sensors and the bulk temperature of sea water (BSST) measured by instruments. As sea surface temperature products distributed by many overseas institutions represent temperatures at different depths, it is essential to understand the relationship between the SSST and the BSST. In this study, we constructed an observation system of infrared radiometer onboard a marine research vessel for the first time in Korea to measure the SSST. The calibration coefficients were prepared by performing the calibration procedure of the radiometer device in the laboratory prior to the shipborne observation. A series of processes were applied to calculate the temperature of the layer of radiance emitted from the sea surface as well as that from the sky. The differences in skin-bulk temperatures were investigated quantitatively and the characteristics of the vertical structure of temperatures in the upper ocean were understood through comparison with Himawari-8 geostationary satellite SSTs. Comparison of the skin-bulk temperature differences illustrated overall differences of about 0.76℃ at Jangmok port in the southern coast and the offshore region of the eastern coast of the Korean Peninsula from 21 April to May 6, 2020. In addition, the root-mean-square error of the skin-bulk temperature differences showed daily variation from 0.6℃ to 0.9℃, with the largest difference of 0.83-0.89℃ at 1-3 KST during the daytime and the smallest difference of 0.59℃ at 15 KST. The bias also revealed clear diurnal variation at a range of 0.47-0.75℃. The difference between the observed skin sea surface temperature and the satellite sea surface temperature showed a mean square error of approximately 0.74℃ and a bias of 0.37℃. The analysis of this study confirmed the difference in the skin-bulk temperatures according to the observation depth. This suggests that further ocean shipborne infrared radiometer observations should be carried out continuously in the offshore regions to understand diurnal variation as well as seasonal variations of the skin-bulk SSTs and their relations to potential causes.