• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.609-625
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• 2022

Validation of satellite data products is critical for subsequent analysis that is based on the data. Particularly, performance of ocean color products in turbid and shallow near-land ocean areas has been questioned for long time for its difficulty that stems from the complex optical environment with varying distribution of water constituents. Furthermore, validation with ship-based or station-based measurements has also exhibited clear limitation in its spatial scale that is not compatible with that of satellite data. This study firstly performed validation of major GOCI-II products such as remote sensing reflectance, chlorophyll-a concentration, suspended particulate matter, and colored dissolved organic matter, using the in-situ measurements collected from ship-based field campaign. Secondly, this study also presents preliminary analysis on the use of drone images for product validation. Multispectral images were acquired from a MicaSense RedEdge camera onboard a UAV to compensate for the significant scale difference between the ship-based measurements and the satellite data. Variation of water radiance in terms of camera altitude was analyzed for future application of drone images for validation. Validation conducted with a limited number of samples showed that GOCI-II remote sensing reflectance at 555 nm is overestimated more than 30%, and chlorophyll-a and colored dissolved organic matter products exhibited little correlation with in-situ measurements. Suspended particulate matter showed moderate correlation with in-situ measurements (R²~0.6), with approximately 20% uncertainty.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.85-85
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• 2021

CDOM(Colored or Chromophoric Dissolved Organic Matter)은 바다, 호수 및 강에서 담수, 오수, 퇴적물 등으로부터 공급된 유기물질의 일종으로 가시광선에서 빛을 흡수하는 성질을 가지며, 2016년부터 환경부에서 선정한 하천, 호수 등 방류수의 수질오염 표준인 TOC(Total Organic Carbon)를 간접 추정할 수 있는 매개변수가 될 수 있다. 따라서, 본 연구에서는 백제보 상류 23 km 구간을 대상으로 2개년(2016~2017) 중 7일의 초분광영상 자료를 활용하여 내륙지역의 CDOM에 대한 적정 반사도 밴드값(R_rs)과 CDOM을 추정하는 알고리즘을 개발하고자 한다. CDOM은 흡수계수(α_CDOM)를 통해 간접 추정되며, 흡수계수의 기준 파장값(λ)은 연구별로 350 nm, 375 nm, 400 nm, 412 nm 및 440 nm 등 다르게 나타난다. 초분광영상은 AsaFENIX 초분광 센서에서 관측된 380~970 nm까지 4 nm 간격, 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 영상을 활용하였으며, 자료의 연속성을 위해 smoothing 기법을 활용하여 가공하였다. 추정 알고리즘은 Random forest를 활용하였으며, 70%의 trainning과 30%의 test로 구분하여 적용하였다. 산출된 CDOM은 결정계수(R²), Nash-Sutcliffe efficiency(NSE)를 이용하여 실측 CDOM과 비교하였다. 흡수계수별 CDOM의 산정 결과 α_CDOM(350 nm)의 trainning, test에서 각각 R²가 0.71, 0.74, NSE가 0.25, 0.49로 가장 높았으며, 적정 반사도 밴드값은 R_rs(466), R_rs(493), R_rs(548), R_rs(641)를 사용하였을 때 trainning, test에서 각각 R²가 0.93, 0.90, NSE가 0.85, 0.69로 가장 높게 나타났다.

• Journal of the Korean association of regional geographers
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• v.14 no.2
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• pp.114-125
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• 2008

Colored dissolved organic matter(CDOM) is an important component of ocean color that can be used as an invaluable tool in water quality and ocean color studies. With the largest source of coastal CDOM appearing to be from freshwater discharge into the ocean, coastal predictive models will do much to refine our knowledge about major processes that control CDOM distributions in coastal waters and provide a better insight into the global carbon cycle. This study aims at developing a GIS-based watershed-scale predictive model of CDOM distributions in Neponset river watersheds that can be used to appraise our understanding of CDOM sources and distributions in coastal waters and predict the response of CDOM concentration to changes in land use patterns. Weighting factors are developed for CDOM freshwater sources after extensive groundtruthing from various landuse types in the watershed. This model makes use of a publicly available DEM(Digital elevation model) as the base data for analysis. Stream networks, discharge, and land use data are used from public repositories while sub- watershed delineation, pour-points, and land use parcels are generated using Spatial Analysis of ArcGIS 9.2 to estimate the CDOM loading from various sources to the lower tributaries of rivers. The Neponset Watershed in eastern Massachusetts is selected as the site for development of the model.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.37-45
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• 2000

Since being launched for ocean observing in 1997, the SeaWiFS sensor has supplied data on ocean chlorophyll distribution and environmental conditions of the atmosphere. Until now, a lot of SeaWiFS data have been archived and utilized for ocean monitoring and land observation. The SeaWiFS sensor has 1km spatial resolution, therefore, it is difficult to obtain data at the coastal zone. Since atmospheric correction algorithms at the coastal area have not been confirmed for chlorophyll algorithm, the ocean color data analysis for coastal zone is not common. In particular, domestic coastal areas have high suspended sediments concentrations and higher absorption influence of colored dissolved organic matter (CDOM), released from in-land, than open-sea. Thus, a useful algorithm for analysis of chlorophyll distribution in domestic coastal areas has not been developed. In this study, empirical algorithms, using data from the ocean color sensor, were developed for monitoring of chlorophyll distribution of coastal areas. In the process of the development of the algorithms, we can find that the red band (665nm) should be used for analyzing of domestic coastal areas near the Yellow Sea.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.46-46
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• 2022

유기물의 복잡한 혼합물인 CDOM(Colored or Chromophoric Dissolved Organic Matter)은 하천 내 BOD(Biological Oxygen Demand), COD(Chemical Oxygen Demand) 및 유기 오염물질과 상당한 관련이 있다. CDOM은 가시광선 영역에서 빛을 흡수하는 성질을 가지고 있으며, 최근 원격감지 기술로 CDOM을 모니터링하기 위한 연구가 진행되고 있다. 본 연구에서는 백제보 상류 23km 구간에서 3년(2016~2018) 중 13일의 초분광영상을 활용하여 머신러닝 기반 CDOM을 추정 알고리즘을 개발하고자 한다. 초분광영상은 400~970 nm의 범위의 4 nm 간격 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 항공기 탑재 AsiaFENIX 초분광 센서를 통해 수집하였으며 CDOM은 Millipore polycarbonate filter (𝚽47, 0.2 ㎛)에서 여과된 CDOM 샘플 자료를 200~800 nm의 흡수계수 스펙트럼으로 추출하여 사용하였다. CDOM 값은 전체기간 동안 2.0~11.0 m^-1의 값 분포를 보였으며 5 m^-1이상의 고농도 구간 자료개수가 전체 153개 샘플자료 중 21개로 불균형하다. 따라서 ADASYN(Adaptive Synthesis Sampling Approach)의 oversampling 방법으로 생성된 합성 데이터를 사용하여 원본 데이터의 소수계층 데이터 불균형을 해결하고 모델 예측 성능을 개선하고자 하였다. 생성된 합성 데이터를 입력변수로 하여 ANN(Artificial Neural Netowk)을 활용한 CDOM 예측 알고리즘을 구축하였다. ADASYN 기법을 통한 합성 데이터는 관측된 데이터의 불균형을 해결하여 기계학습 모델의 CDOM 탐지 성능을 향상시킬 수 있으며, 저수지 내 유기 오염물질 관리를 위한 설계를 지원하는데 사용할 수 있을 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.531-548
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• 2012

To identify Cochlodinium polykrikoides red tide from non-red tide water (satellite high chlorophyll waters) in the South Sea of Korea (SSK), we improved a spectral classification method proposed by Son et al.(2011) for the world first Geostationary Ocean Color Imager (GOCI). C. polykrikoides blooms and non-red tide waters were classified based on four different criteria. The first step revealed that the radiance peaks of potential red tide water occurred at 555 and 680 nm (fluorescence peak). The second step separated optically different waters that were influenced by relatively low and high contributions of colored dissolved organic matter (CDOM) (including detritus) to chlorophyll. The third and fourth steps discriminated red tide water from non-red tide water based on the blue-to-green ratio, respectively. After applying the red tide classification, the spectral response of C. polykrikoides red tide water, which is influenced by pigment concentration as well as CDOM (detritus), showed different slopes for the blue and green bands (lower slope at blue bands and higher slope at green bands). The opposite result was found for non-red tide water. This modified spectral classification method for GOCI led to increase user accuracy for C. polykrikoides and non-red tide blooms and provided a more reliable and robust identification of red tides over a wide range of oceanic environments than was possible using chlorophyll a concentration, or proposed red tide detection algorithms. Maps of C. polykrikoides red tide in SSK outlined patches of red tide covering the area near Naro-do and Tongyeong during the end of July and early of August, 2012 and extending into from Wan-do and Geoje-do during the middle of August, 2012.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.59-68
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• 1999

The study for detecting suspended sediment distribution in Lake Sihwa, which has a large surface area and coastal area, using remote sensing technique was carried out with development of satellite data collected since 1970. The research, however, analysis of spatial distribution and quantity, is not common in domestic study and useful algorithms have not been proposed. In this study, a suspended sediment algorithm was composed with in-situ data obtained in study area and remote sensing reflectance obtained in-water optical instrument, which has SeaWiFS wavelength bands. However, when the algorithm was applied to Landsat TM data, including an in-situ data set, and some problems arose. The composition of the algorithm which was structured with band difference and band ratio showed the correlation of $R^2$=0.7649 with concentration of suspended sediments. And, between calculated and observed concentration of suspended sediments there was a correlation of $R^2$=0.6959. However, remote sensing reflectance obtained from Landsat TM is not good for the estimation of concentration of suspended sediments, because of high concentration of chlorophyll and CDOM(colored dissolved organic matter).

• 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.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.