• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1497-1503
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• 2023

To address challenges in classifying clouds and snow cover when calculating ground reflectance in Near-UltraViolet (UV) wavelengths, this study introduces a methodology that combines cloud data from the Geostationary Environmental Monitoring Spectrometer (GEMS) and the Advanced Meteorological Imager (AMI)satellites for snow cover analysis. The proposed approach aims to enhance the quality of surface reflectance calculations, and combined cloud data were generated by integrating GEMS cloud data with AMI cloud detection data. When applied to compute GEMS surface reflectance, this fusion approach significantly mitigated underestimation issues compared to using only GEMS cloud data in snow-covered regions, resulting in an approximately 17% improvement across the entire observational area. The findings of this study highlight the potential to address persistent underestimation challenges in snow areas by employing fused cloud data, consequently enhancing the accuracy of other Level-2 products based on improved surface reflectivity.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1529-1539
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• 2023

The data from Geostationary Ocean Color Imager-II (GOCI-II), which observes the color of the sea to monitor marine environments, undergoes various correction processes in the ground station system, producing data from Raw to Level-2 (L2). Quality issues arising at each processing stage accumulate step by step, leading to an amplification of errors in the satellite data. To address this, improvements were made to the GOCI-II ground station system to measure potential optical quality and geolocation accuracy errors in the Level-1A/B (L1A/B) data. A newly established Radiometric and Geometric Performance Assessment Module (RGPAM) now measures five optical quality factors and four geolocation accuracy factors in near real-time. Testing with GOCI-II data has shown that RGPAM's functions, including data processing, display and download of measurement results, work well. The performance metrics obtained through RGPAM are expected to serve as foundational data for real-time radiometric correction model enhancements, assessment of L1 data quality consistency, and the development of reprocessing strategies to address identified issues related to the GOCI-II detector's sensitivity degradation.

• 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.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.961-970
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• 2017

Despite of large demand for high spatial resolution products of aerosol properties from satellite remote sensing, it has been very difficult due to the weak signal by a single pixel and higher noise from clouds. In this study, aerosol retrieval algorithm with the high spatial resolution ($500m{\times}500m$) was developed using Geostationary Ocean Color Imager (GOCI) data during the Korea-US Air Quality (KORUS-AQ) period in May-June, 2016.Currently, conventional GOCI Yonsei aerosol retrieval(YAER) algorithm provides $6km{\times}6km$ spatial resolution product. The algorithm was tested for its best possible resolution of 500 m product based on GOCI YAER version 2 algorithm. With the new additional cloud masking, aerosol optical depth (AOD) is retrieved using the inversion method, aerosol model, and lookup table as in the GOCI YAER algorithm. In some cases, 500 m AOD shows consistent horizontal distribution and magnitude of AOD compared to the 6 km AOD. However, the 500 m AOD has more retrieved pixels than 6 km AOD because of its higher spatial resolution. As a result, the 500 m AOD exists around small clouds and shows finer features of AOD. To validate the accuracy of 500 m AOD, we used dataset from ground-based Aerosol Robotic Network (AERONET) sunphotometer over Korea. Even with the spatial resolution of 500 m, 500 m AOD shows the correlation coefficient of 0.76 against AERONET, and the ratio within Expected Error (EE) of 51.1%, which are comparable to the results of 6 km AOD.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.257-266
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• 2017

In this study, the dust/smoke detection algorithm was developed with a multi-spectral satellite remote sensing method using Moderate resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and the results were validated as RGB composite images of red(R; band 1), green(G; band 4), blue(B; band 3) channels using MODIS L1B data and Cloud-Aerosol Lidar with Orthogonal Polarization Satellite Observations(CALIPSO) Vertical Feature Mask (VFM) product. In the daytime on March 30, 2007 and April 27, 2012, the consistencies between the dust/smoke detected by this algorithm and verification data were approximately 56.4 %, 72.0 %, respectively. During the nighttime, the similar consistency was 40.5 % on April 27, 2012. Although these results were analyzed for limited cases due to the spatiotemporal matching for the MODIS and CALIPSO satellites, they could be used to utilize the aerosol detection of geostationary satellites for the next generations in Korea through further research.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1339-1348
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• 2020

Phytoplankton controls marine ecosystems in terms of nutrients, photosynthetic rate, carbon cycle, etc. and the degree of its influence on the marine environment depends on their physical size. Many studies have been attempted to identify marine phytoplankton size classes using the remote sensing techniques. One of successful approach was the three-component model which estimates the chlorophyll concentrations of three phytoplankton size classes (micro-phytoplankton; >20 ㎛, nano-; 2-20 ㎛ and pico-; <2 ㎛) as a function of total chlorophyll. Here, we examined the applicability of Geostationary Ocean Colour Imager (GOCI) to the mapping of the phytoplankton size class distribution in the East Sea. A fit of the three-component model to a biomarker pigment dataset collected in the study area for some years including a large harmful algal bloom period has been carried out to derive size-fractioned chlorophyll concentration (CHL). The tuned three-component model was applied to the hourly GOCI images to identify the fractions of each phytoplankton size class for the entire CHL. Then, we investigated the distribution of phytoplankton community in terms of the size structure in the East Sea during the harmful Cochlodinium polykrikoides blooms in the summer of 2013.

• Proceedings of the KSRS Conference
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• v.1
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• pp.467-470
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• 2006

Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. To achieve these mission objectives, it is necessary to develop an atmospheric correction technique which is capable of delivering geophysical products, particularly for highly turbid coastal regions that are often dominated by strongly absorbing aerosols from the adjacent continental/desert areas. In this paper, we present a more realistic and cost-effective atmospheric correction method which takes into account the contribution of NIR radiances and include specialized models for strongly absorbing aerosols. This method was tested extensively on SeaWiFS ocean color imagery acquired over the Northwest Pacific waters. While the standard SeaWiFS atmospheric correction algorithm showed a pronounced overcorrection in the violet/blue or a complete failure in the presence of strongly absorbing aerosols (Asian dust or Yellow dust) over these regions, the new method was able to retrieve the water-leaving radiance and chlorophyll concentrations that were consistent with the in-situ observations. Such comparison demonstrated the efficiency of the new method in terms of removing the effects of highly absorbing aerosols and improving the accuracy of water-leaving radiance and chlorophyll retrievals with SeaWiFS imagery.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• 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.