• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.251-262
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• 2010

In order to provide quantitative control of the standard products of Geostationary Ocean Color Imager (GOCI), on-board radiometric correction, atmospheric correction, and bio-optical algorithm are obtained continuously by comprehensive and consistent calibration and validation procedures. The calibration/validation for radiometric, atmospheric, and bio-optical data of GOCI uses temperature, salinity, ocean optics, fluorescence, and turbidity data sets from buoy and platform systems, and periodic oceanic environmental data. For calibration and validation of GOCI, we compared radiometric data between in-situ measurement and HyperSAS data installed in the Ieodo ocean research station, and between HyperSAS and SeaWiFS radiance. HyperSAS data were slightly different in in-situ radiance and irradiance, but they did not have spectral shift in absorption bands. Although all radiance bands measured between HyperSAS and SeaWiFS had an average 25% error, the 11% absolute error was relatively lower when atmospheric correction bands were omitted. This error is related to the SeaWiFS standard atmospheric correction process. We have to consider and improve this error rate for calibration and validation of GOCI. A reference target site around Dokdo Island was used for studying calibration and validation of GOCI. In-situ ocean- and bio-optical data were collected during August and October, 2009. Reflectance spectra around Dokdo Island showed optical characteristic of Case-1 Water. Absorption spectra of chlorophyll, suspended matter, and dissolved organic matter also showed their spectral characteristics. MODIS Aqua-derived chlorophyll-a concentration was well correlated with in-situ fluorometer value, which installed in Dokdo buoy. As we strive to solv the problems of radiometric, atmospheric, and bio-optical correction, it is important to be able to progress and improve the future quality of calibration and validation of GOCI.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.383-389
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• 2009

The main objective of the research is a feasibility study on the intertidal zone using a X-band radar satellite, TerraSAR-X. The TerraSAR-X data have been acquired in the west coast of Korea where large tidal flats, Ganghwa and Yeongjong tidal flats, are developed. Investigations include: 1) waterline and backscattering characteristics of the high resolution X-band images in tidal flats; 2) polarimetric signature of halophytes (or salt marsh plants), specifically Suaeda japonica; and 3) phase and coherence of interferometric pairs. Waterlines from TerraSAR-X data satisfy the requirement of horizontal accuracy of 60 m that corresponds to 20 cm in average height difference while current other spaceborne SAR systems could not meet the requirement. HH-polarization was the best for extraction of waterline, and its geometric position is reliable due to the short wavelength and accurate orbit control of the TerraSAR-X. A halophyte or salt marsh plant, Suaeda japonica, is an indicator of local sea level change. From X-band ground radar measurements, a dual polarization of VV/VH-pol. is anticipated to be the best for detection of the plant with about 9 dB difference at 35 degree incidence angle. However, TerraSAR-X HH/TV dual polarization was turned to be more effective for salt marsh monitoring. The HH-HV value was the maximum of about 7.9 dB at 31.6 degree incidence angle, which is fairly consistent with the results of X-band ground radar measurement. The boundary of salt marsh is effectively traceable specifically by TerraSAR-X cross-polarization data. While interferometric phase is not coherent within normal tidal flat, areas of salt marsh where the landization is preceded show coherent interferometric phases regardless of seasons or tide conditions. Although TerraSAR-X interferometry may not be effective to directly measure height or changes in tidal flat surface, TanDEM-X or other future X-band SAR tandem missions within one-day interval would be useful for mapping tidal flat topography.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.289-298
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• 2019

In this present study, the effects of Signal to Noise Ratio (SNR), Full Width Half Maximum (FWHM), Aerosol Optical Depth (AOD), $O_3$ Vertical Column Density ($O_3$ VCD), and Solar Zenith Angle (SZA) on the accuracy of sulfur dioxide Vertical Column Density ($SO_2$ VCD) retrieval have been quantified using the Differential Optical Absorption Spectroscopy (DOAS) method with the ground-based direct-sun synthetic radiances. The synthetic radiances produced based on the Beer-Lambert-Bouguer law without consideration of the diffuse effect. In the SNR condition of 650 (1300) with FWHM = 0.6 nm, AOD = 0.2, $O_3$ VCD = 300 DU, and $SZA=30^{\circ}$, the Absolute Percentage Difference (APD) between the true $SO_2$ VCD values and those retrieved ranges from 80% (28%) to 16% (5%) for the $SO_2$ VCD of $8.1{\times}10^{15}$ and $2.7{\times}10^{16}molecules\;cm^{-2}$, respectively. For an FWHM of 0.2 nm (1.0 nm) with the $SO_2$ VCD values equal to or greater than $2.7{\times}10^{16}molecules\;cm^{-2}$, the APD ranges from 6.4% (29%) to 6.2% (10%). Additionally, when FWHM, SZA, AOD, and $O_3$ VCD values increase, APDs tend to be large. On the other hand, SNR values increase, APDs are found to decrease. Eventually, it is revealed that the effects of FWHM and SZA on $SO_2$ VCD retrieval accuracy are larger than those of $O_3$ VCD and AOD. The SZA effects on the reduction of $SO_2$ VCD retrieval accuracy is found to be dominant over the that of FWHM for the condition of $SO_2$ VCD larger than $2.7{\times}10^{16}molecules\;cm^{-2}$.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.229-237
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• 2018

As IoT and AI have recently developed, interest in digital healthcare is increasing. Therefore, this study aims to identify technology trends through a patent analysis on digital healthcare and present future promising areas by analyzing domestic and foreign technology competitiveness and keywords. The detailed technologies to be analyzed were designated as Health Information Measurement Technology, Healthcare Platform Technology and Healthcare Remote Service Technology, and 61,166 patents were analyzed to identify the patent trends of the world's major patent offices and major patent applications. In addition, the analysis of the technological competitiveness of each detailed technology and Korea's technological competitiveness based on its patent activity, the rate of major market securing, and the uses of the patents showed that Korea's technological competitiveness was lower than global technology. In addition, the key keyword analysis showed that the core promising areas of digital healthcare were expected to require a focused strategy for fostering health care platform technologies in Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.5
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• pp.475-489
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• 2010

In order to verify the enhancement of ozone and carbon monoxide (CO) during springtime in East Asia, we investigated weather conditions and data from remote sensors, air quality models, and air quality monitors. These include the geopotential height archived from the final (FNL) meteorological field, the potential vorticity and the wind velocity simulated by the Meteorological Mesoscale Model 5 (MM5), the back trajectory estimated by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the total column amount of ozone and the aerosol index retrieved from the Total Ozone Mapping Spectrometer (TOMS), the total column density of CO retrieved from the Measurement of Pollution in the Troposphere (MOPITT), and the concentration of ozone and CO simulated by the Model for Ozone and Related Chemical Tracers (MOZART). In particular, the total column density of CO, which mightoriginate from the combustion of fossil fuels and the burning of biomass in China, increased in East Asia during spring 2000. In addition, the enhancement of total column amounts of ozone and CO appeared to be associated with both the upper cut-off low near 500 hPa and the frontogenesis of a surface cyclone during a weak Asian dust event. At the same time, high concentrations of ozone and CO on the Earth's surface were shown at the Seoul air quality monitoring site, located at the surface frontogenesis in Korea. It was clear that the ozone was invaded by the downward stretched vortex anomalies, which included the ozone-rich airflow, during movement and development of the cut-off low, and then there was the catalytic photochemical reaction of ozone precursors on the Earth's surface during the day. In addition, air pollutants such as CO and aerosol were tracked along both the cyclone vortex and the strong westerly as shown at the back trajectory in Seoul and Busan, respectively. Consequently, the maxima of ozone and CO between the two areas showed up differently because of the time lag between those gases, including their catalytic photochemical reactions together with the invasion from the upper troposphere, as well as the path of their transport from China during the weak Asian dust event.