• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.175-188
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• 2010

A new correction method is required for the Geostationary Ocean Color Imager (GOCI), which is the world's first ocean color observing sensor in geostationary orbit. In this paper we introduce a new method of atmospheric and the Bidirectional Reflectance Distribution Function(BRDF) correction for GOCI. The Spectral Shape Matching Method(SSMM) and the Sun Glint Correction Algorithm(SGCA) were developed for atmospheric correction, and BRDF correction was improved using Inherent Optical Property(IOP) data. Each method was applied to the Sea-Viewing Wide Field-of-view Sensor(SeaWiFS) images obtained in the Korean sea area. More accurate estimates of chlorophyll concentrations could be possible in the turbid coastal waters as well as areas severely affected by aerosols.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• Current Industrial and Technological Trends in Aerospace
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• v.6 no.2
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• pp.116-124
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• 2008

Korea Aerospace Research Institute(KARI) is developing COMS (Communication, Ocean and Meteorological Satellite) which is scheduled to take off in June, 2009. COMS is the first geosynchronous satellite developed in Korea which is able to perform three missions 24 hours a day. The oceanic payload was transferred from France to Korea in November, 2008 and made it possible to integrate all three payload together. After the integration COMS is planned to be transferred to Guiana Space Center (on French territory) to be launched. In this paper the trend of domestic and international development of the multi-purpose geosynchronous satellite considering the COMS is the first operational geosynchronous multipurpose satellite in the world.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.277-285
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• 2010

Geostationary Ocean Color Imager(GOCI-I), the world's first space-borne ocean color observation geostationary satellite, will be launched on June 2010. Development of GOCI-I took about 6 years, and its expected lifetime is about 7 years. The mission and user requirements of GOCI-II are required to be defined at this moment. Because baseline of the main mission of GOCI-II must be defined during the development time and early operational period of GOCI-I. The main difference between these missions is the global-monitoring capability of GOCI-II, which will meet the necessity of the monitoring and research on climate change in the long-term. The user requirements of GOCI-II will have higher spatial resolution, $250m{\times}250m$, and 12 spectral bands to fulfill GOCI-I's user request, which could not be implemented on GOCI-I for technical reasons. A dedicated panchromatic band will be added for the nighttime observation to obtain fishery information. GOCI-II will have a new capability, supporting user-definable observation requests such as clear sky area without clouds and special-event areas, etc. This will enable higher applicability of GOCI-II products. GOCI-II will perform observations 8 times daily, the same as GOCI-I's. Additionally, daily global observation once or twice daily is planned for GOCI-II. In this paper, we present an improved development and organization structure to solve the problems that have emerged so far. The hardware design of the GOCI-II will proceed in conjunction with domestic or foreign space agencies.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1507-1515
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• 2021

If chlorophyll-a is estimated through ocean color remote sensing, it is able to understand the global distribution of phytoplankton and primary production. However, there are missing data in the ocean color observed from the satellites due to the clouds or weather conditions. In thisstudy, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product wasreconstructed by using DINEOF (Data INterpolation Empirical Orthogonal Functions). DINEOF reconstructs the missing data based on spatio-temporal data, and the accuracy was cross-verified by removing a part of the GOCI chlorophyll-a image and comparing it with the reconstructed image. In the study area, the optimal EOF (Empirical Orthogonal Functions) mode for DINEOF wasin 10-13. The temporal and spatialreconstructed data reflected the increasing chlorophyll-a concentration in the afternoon, and the noise of outliers was filtered. Therefore, it is expected that DINEOF is useful to reconstruct the missing images, also it is considered that it is able to use as basic data for monitoring the ocean environment.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.571-580
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• 2010

Coastal shoreline movement due to erosion and deposition is a major concern for coastal zone management. Shoreline is changed by nature factor or development of coastal. Change of shoreline is threatening marine environment and destroying. Therefore, we need monitoring of shoreline change with time series analysis for coastal zone management. In this study, we analyzed the shoreline change using airphotograph, LiDAR and satellite imagery from 1971 to 2009 in Uljin, Gyeongbuk, Korea. As a result, shoreline near of the nuclear power plant show linear pattern in 1971 and 1980, however the pattern of shoreline is changed after 2000. As a result of long-term monitoring, shoreline pattern near of the nuclear power plant is changed by erosion toward sea. The pattern of shoreline near of KORDI until 2003 is changed due to deposition toward sea, but the new pattern toward land is developed by erosion from 2003 to 2009. The shoreline is changed by many factors. However, we will guess that change of shoreline within study area is due to construction of nuclear power plant. In the future work, we need sedimentary and physical studies.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.537-549
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• 2023

The distribution characteristics of tidal flat sediment components are used as an essential data for coastal environment analysis and environmental impact assessment. Therefore, a reliable classification map of surface sedimentary facies is essential. This study evaluated the applicability of regression kriging to generate a classification map of the sedimentary facies of tidal flats. For this aim, various factors such as the number of field survey data and remote sensing-based auxiliary data, the effect of regression models on regression kriging, and the comparison with other prediction methods (univariate kriging and regression analysis) on surface sedimentary facies classification were investigated. To evaluate the applicability of regression kriging, a case study using unmanned aerial vehicle (UAV) data was conducted on the Hwang-do tidal flat located at Anmyeon-do, Taean-gun, Korea. As a result of the case study, it was most important to secure an appropriate amount of field survey data and to use topographic elevation and channel density as auxiliary data to produce a reliable tidal flat surface sediment facies classification map. In addition, regression kriging, which can consider detailed characteristics of the sediment distributions using ultra-high resolution UAV data, had the best prediction performance compared to other prediction methods. It is expected that this result can be used as a guideline to produce the tidal flat surface sedimentary facies classification map.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.231.1-231.1
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• 2012

한국해양과학기술원 해양위성센터에서 주관운영을 수행하고 있는 천리안 위성의 해양탑재체인 천리안 해양관측위성(이하 GOCI)은 정지궤도위성용 해색센서로서, 태양을 광원으로 지구상의 해수 표면 부근에서 반사되어 대기를 통과한 가시광 및 근적외 대역을 8개 밴드로 분광하여 관측하는 센서이다. 해색센서의 경우, 일반적으로 센서에 입사되는 광신호의 약 90%가 대기에 의한 신호이며, 약 10%에 해당되는 신호만 원래 관측목적인 해수에 의한 신호이기 때문에, 5% 이내의 높은 복사보정 정확도가 요구된다. 이러한 높은 복사보정 정확도를 만족시키기 위해서는, 지상에서의 현장관측을 통한 위성자료 검보정 뿐만 아니라, 발사 후 위성 궤도상에서 센서의 복사보정을 수행하는 궤도상 복사보정이 체계적으로 수행되어야 한다. GOCI는 태양을 기준광원으로 하는 태양광 복사보정을 채택하여, 센서의 셔터부에 태양광 복사보정을 위한 2개의 태양광확산기(Solar Diffuser)를 장비하고 있다. 본 발표에서는 궤도상 시험 후 약 16개월에 걸친 궤도상 복사보정 운영결과와 관련하여, 발사 후 일별, 월별, 계절별 등 각 기간별 센서의 이득변화를 관찰하였으며, 그 결과 1년을 기준으로 약 3% 범위로 주기적인 이득 변화가 있음을 확인하였다. 지상시험결과와의 비교에 의해, 태양광확산기에 대한 태양입사각이 이러한 주기적인 이득 변화의 주 원인임을 궤도상 복사보정 운영결과를 통해 밝히고자 한다.