• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1541-1551
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• 2023

Radiometric calibration is a fundamental step in ocean color remote sensing since the step to derive solar radiance spectrum in visible to near-infrared wavelengths from the sensor-observed electromagnetic signals. Generally, satellite sensor suffers from degradation over the mission period, which results in biases/uncertainties in radiometric calibration and the final ocean products such as water-leaving radiance, chlorophyll-a concentration, and colored dissolved organic matter. Therefore, the importance of radiometric calibration for the continuity of ocean color satellites has been emphasized internationally. This study introduces an approach to improve the radiometric calibration algorithm for the visible bands of the Geostationary Ocean Color Imager-II (GOCI-II) satellite with a focus on stability. Solar Diffuser (SD) measurements were employed as an on-orbit radiometric calibration reference, to obtain the continuous monitoring of absolute gain values. Time series analysis of GOCI-II absolute gains revealed seasonal variations depending on the azimuth angle, as well as long-term trends by possible sensor degradation effects. To resolve the complexities in gain variability, an azimuth angle correction model was developed to eliminate seasonal periodicity, and a sensor degradation correction model was applied to estimate nonlinear trends in the absolute gain parameters. The results demonstrate the effects of the azimuth angle correction and sensor degradation correction model on the spectrum of Top of Atmosphere (TOA) radiance, confirming the capability for improving the long-term stability of GOCI-II data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.598-608
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• 2023

To comprehensively grasp the dynamic changes in the coastal terrain and coastal erosion, it is imperative to incorporate temporal and spatial continuity through frequent and continuous monitoring. Recently, there has been a proliferation of research in coastal monitoring using remote sensing, accompanied by advancements in image monitoring and analysis technologies. Remote sensing, typically involves collection of images from aircraft or satellites from a distance, and offers distinct advantages in swiftly and accurately analyzing coastal terrain changes, leading to an escalating trend in its utilization. Remote satellite image-based coastal line detection involves defining measurable coastal lines from satellite images and extracting coastal lines by applying coastal line detection technology. Drawing from the various data sources surveyed in existing literature, this study has comprehensively analyzed encompassing the definition of coastal lines based on satellite images, current status of remote satellite imagery, existing research trends, and evolving landscape of technology for satellite image-based coastal line detection. Based on the results, research directions, on latest trends, practical techniques for ideal coastal line extraction, and enhanced integration with advanced digital monitoring were proposed. To effectively capture the changing trends and erosion levels across the entire Korean Peninsula in future, it is vital to move beyond localized monitoring and establish an active monitoring framework using digital monitoring, such as broad-scale satellite imagery. In light of these results, it is anticipated that the coastal line detection field will expedite the progression of ongoing research practices and analytical technologies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.28-36
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• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.123-135
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• 2013

The reflectance observed in the visible channels of a geostationary meteorological satellite can be used to calculate the amount of cloud by comparing the reflectance with the observed solar radiation data at the ground. Using this, the solar radiation arriving at the surface can be estimated. This study used the Meteorological Imager (MI) reflectance observed at a wavelength of 675 nm and the Geostationary Ocean Color Imager (GOCI) reflectance observed at similar wavelengths of 660 and 680 nm. Cloudy days during a typhoon and sunny days with little cloud cover were compared using observation data from the geostationary satellite. Pixels that had more than 40% reflectance in the satellite images showed less than 0.3 of the cloud index and blocked more than 70% of the solar energy. Pixels that showed less than 15% reflectance showed more than 0.9 of the cloud index and let through more than 90% of the solar energy to the surface. The calculated daily accumulated solar radiation was compared with the observed daily accumulated solar radiation in 22 observatories of the Korean Meteorological Administration. The values calculated for the COMS and MTSAT MI sensors were smaller than the observation and showed low correlations of 0.94 and 0.93, respectively, which were smaller than the 0.96 correlation coefficient calculated for the GOCI sensor. The RMSEs of MTSAT, COMS MI and GOCI calculation results showed 2.21, 2.09, 2.02 MJ/$m^2$ in order. Comparison of the calculated daily accumulated results from the GOCI sensor with the observed data on the ground gave correlations and RMSEs for cloudy and sunny days of 0.96 and 0.86, and 1.82 MJ/$m^2$ and 2.27 MJ/$m^2$, respectively, indicating a slightly higher correlation for cloudy days. Compared to the meteorological imager, the geostationary ocean color imager in the COMS satellite has limited observation time and observation is not continuous. However, it has the advantage of providing high resolution so that it too can be useful for solar energy analysis.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.3
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• pp.166-178
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• 2015

We investigated marine environmental characteristics of Goheung coastal areas in August where is known to be the first outbreak site of Cochlodinium polykrikoides (hereafter C. polykrikoides) blooms, based on the oceanographic data observed from 1993 to 2013 around the Korean southern coastal waters including Eastern China Sea by National Fisheries Research and Development Institute (NFRDI). The data of NOAA/NGSST satellite images as well as numerical simulation results by Seo et al. [2013] were also used for analysis. Water temperatures at the surface and bottom layers in Goheung coast, i.e. Narodo, were $25.0^{\circ}C$ and $23.7^{\circ}C$ so that they were higher than $23.8^{\circ}C$ and $19.4^{\circ}C$ in Geoje coast where is a reference site, respectively. In addition, salinities at the surface and bottom layers in Goheung coast were 31.78 psu and 31.98 psu so that they were a little higher than 31.54 psu at the surface but a little lower than 32.79 psu at the bottom in Geoje coast, respectively. That is, the differences in water temperature or salinity between the surface and bottom layers in Goheung coast in August were not large compared to Geoje coast. This suggests that stratification in Goheung coast in August is fairly weak or may not be established. In addition, the concentrations of DIN and DIP at the surface layer were 0.068 mg/L ($4.86{\mu}M$) and 0.015 mg/L ($5.14{\mu}M$) in Goheung coast while 0.072 mg/L ($5.14{\mu}M$) and 0.01 mg/L ($0.32{\mu}M$) in Geoje coast, so they did not indicate a meaningful difference. On the other hand, when C. polykrikoides blooms, water temperature and salinity in August at the station 317-22 ($31.5^{\circ}N$, $124^{\circ}E$) of the East China Sea, where is near the mouth of Yangtze River, were $27.8^{\circ}C$ and 31.61 psu, respectively. Thus, water temperature was much higher whereas salinity was almost similar compared to Goheung coast. Furthermore, concentrations of $NO_3-N$ and $PO_4-P$ in the East China Sea in August were remarkably high compared to Goheung coast. When C. polykrikoides blooms, according to not only the image data of satellites NOAA/NGSST but also numerical experiment results by Seo et al.[2013], the freshwater out of Yangtze River was judged to clearly affect the Korean southern coastal waters. Therefore, the supply of nutrients in terms of Yangtze River may greatly contribute to the outbreak of C. polykrikoides blooms in Goheung coast in summer.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.363-370
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• 2023

Various earth observation satellites need to provide accurate and high-quality data after launch. To maintain and enhance the quality of satellite data, it is crucial to employ a cross-calibration process that accounts for differences in sensor characteristics, such as the spectral band adjustment factor (SBAF). In this study, we utilized Landsat-8 and Sentinel-2A satellite imagery collected from desert sites in Libya4, Algeria3, and Mauritania2 among pseudo-invariant calibration sites to calculate and apply SBAF, thereby compensating the uncertainties arising from variations in bandwidths. We quantitatively compared the reflectance differences based on the similarity of bandwidths, including Blue, Green, Red, and both the near-infrared (NIR) narrow, and NIR bands of Sentinel-2A. Following the application of SBAF, significant results with reflectance differences of approximately 1% or less were observed for all bands except NIR. In the case of the Sentinel-2A NIR band, it exhibited a significantly larger bandwidth difference compared to the NIR narrow band. However, after applying SBAF, the reflectance difference fell within the acceptable error range (5%) of 1-2%. It indicates that SBAF can be applied even when there is a substantial difference in the bandwidths of the two sensors, particularly in situations where satellite utilization is limited. Therefore, it was determined that SBAF could be applied even when the bandwidth difference between the two sensors is large in a situation where satellite utilization is limited. It is expected to be helpful in research utilizing the quality and continuity of satellite data.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of Astronomy and Space Sciences
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• v.22 no.3
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• pp.329-352
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• 2005

Long term observations of full-disk Lyman-o irradiance have been made by the instruments on various satellites. In addition, several sounding rockets dating back to the 1950s and up through the present have measured the $Lyman-{\alpha}$ irradiance. Previous full disk $Lyman-{\alpha}$ images of the sun have been very interesting and useful scientifically, but have been only five-minute 'snapshots' obtained on sounding rocket flights. All of these observations to date have been snapshots, with no time resolution to observe changes in the chromospheric structure as a result of the evolving magnetic field, and its effect on the Lyman-o intensity. The $Lyman-{\alpha}$ Imaging Solar Telescope(LIST) can provide a unique opportunity for the study of the sun in the $Lyman-{\alpha}$ region with the high time and spatial resolution for the first time. Up to the 2nd year development, the preliminary design of the optics, mechanical structure and electronics system has been completed. Also the mechanical structure analysis, thermal analysis were performed and the material for the structure was chosen as a result of these analyses. And the test plan and the verification matrix were decided. The operation systems, technical and scientific operation, were studied and finally decided. Those are the technical operation, mechanical working modes for the observation and safety, the scientific operation and the process of the acquired data. The basic techniques acquired through the development of satellite based solar telescope are essential for the construction of space environment forecast system in the future. The techniques which we developed through this study, like mechanical, optical and data processing techniques, could be applied extensively not only to the process of the future production of flight models of this kind, but also to the related industries. Also, we can utilize the scientific achievements which are obtained throughout the project And these can be utilized to build a high resolution photometric detectors for military and commercial purposes. It is also believed that we will be able to apply several acquired techniques for the development of the Korean satellite projects in the future.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.