Prof. Kyung-Soo Han Profile

Prof. Kyung-Soo Han

Associate Professor at Pukyong National Univ

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 25 October 2016 Paper

Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

Kyeong-sang Lee, Sungwon Choi, Minji Seo, Chang suk Lee, Noh-hun Seong, Kyung-Soo Han

Proceedings Volume 9998, 999820 (2016) https://doi.org/10.1117/12.2241732

KEYWORDS: Snow cover, Reflectivity, Clouds, MODIS, RGB color model, Satellites, Detection and tracking algorithms, Data modeling, Molybdenum, Climatology

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Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth’s energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 ~ 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer’s Accuracy), UA (User’s Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Proceedings Article | 19 October 2016 Paper

Retrieval of background surface reflectance with BRD components from pre-running BRDF

Sungwon Choi, Kyeong-Sang Lee, Donghyun Jin, Darae Lee, Kyung-Soo Han

Proceedings Volume 10001, 1000110 (2016) https://doi.org/10.1117/12.2241770

KEYWORDS: Reflectivity, Bidirectional reflectance transmission function, Scattering, Satellites, Remote sensing, Data modeling, Clouds, Climatology, Near infrared, Temporal resolution

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Many countries try to launch satellite to observe the Earth surface. As important of surface remote sensing is increased, the reflectance of surface is a core parameter of the ground climate. But observing the reflectance of surface by satellite have weakness such as temporal resolution and being affected by view or solar angles. The bidirectional effects of the surface reflectance may make many noises to the time series. These noises can lead to make errors when determining surface reflectance. To correct bidirectional error of surface reflectance, using correction model for normalized the sensor data is necessary. A Bidirectional Reflectance Distribution Function (BRDF) is making accuracy higher method to correct scattering (Isotropic scattering, Geometric scattering, Volumetric scattering). To correct bidirectional error of surface reflectance, BRDF was used in this study. To correct bidirectional error of surface reflectance, we apply Bidirectional Reflectance Distribution Function (BRDF) to retrieve surface reflectance. And we apply 2 steps for retrieving Background Surface Reflectance (BSR). The first step is retrieving Bidirectional Reflectance Distribution (BRD) coefficients. Before retrieving BSR, we did pre-running BRDF to retrieve BRD coefficients to correct scatterings (Isotropic scattering, Geometric scattering, Volumetric scattering). In pre-running BRDF, we apply BRDF with observed surface reflectance of SPOT/VEGETATION (VGT-S1) and angular data to get BRD coefficients for calculating scattering. After that, we apply BRDF again in the opposite direction with BRD coefficients and angular data to retrieve BSR as a second step. As a result, BSR has very similar reflectance to one of VGT-S1. And reflectance in BSR is shown adequate. The highest reflectance of BSR is not over 0.4μm in blue channel, 0.45μm in red channel, 0.55μm in NIR channel. And for validation we compare reflectance of clear sky pixel from SPOT/VGT status map data. As a result of comparing BSR with VGT-S1, bias is from 0.0116 to 0.0158 and RMSE is from 0.0459 to 0.0545. They are very reasonable results, so we confirm that BSR is similar to VGT-S1. And weakness of this study is missing pixel in BSR which are observed less time to retrieve BRD components. If missing pixels are filled, BSR is better to retrieve surface products with more accuracy. And we think that after filling the missing pixel and being more accurate, it can be useful data to retrieve surface product which made by surface reflectance like cloud masking and retrieving aerosol.

Proceedings Article | 19 October 2016 Paper

Correlation analysis between variability pattern of TPW and climate variables

Darae Lee, Kyung-Soo Han, Chaeyoung Kwon, Minji Seo, Kyeong-sang Lee

Proceedings Volume 10001, 1000111 (2016) https://doi.org/10.1117/12.2241772

KEYWORDS: Climatology, Electroluminescence, Absorption, Earth observing sensors, Satellites, Time series analysis, MODIS, Analytical research, Climate change, Atmospheric monitoring

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Proceedings Article | 19 October 2016 Paper

Analysis on long-term variability of sea ice albedo and its relationship with sea ice concentration over Antarctica

Minji Seo, Hyun-cheol Kim, Noh-hun Seong, Chaeyoung Kwon, Honghee Kim, Kyung-Soo Han

Proceedings Volume 9999, 99990T (2016) https://doi.org/10.1117/12.2240156

KEYWORDS: Analytical research, Satellites, Climate change, Climatology, Curium, Time series analysis, Spatial resolution, Positive feedback, Remote sensing, Environmental sensing

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Proceedings Article | 24 October 2013 Paper

An influence assessment of GSICS correction using sea surface temperature from geostationary satellite: COMS

Eun-Bin Park, Kyung-Soo Han, Jae-il Cho, Chang-Suk Lee, In-Hwan Kim, Kyoung-Jin Pi, Jae-Hyun Ryu, Jung-Mok Ha

Proceedings Volume 8893, 88931N (2013) https://doi.org/10.1117/12.2029142

KEYWORDS: Satellites, Meteorological satellites, Sensors, Infrared imaging, Calibration, Environmental sensing, Infrared sensors, Satellite communications, Climate change, Infrared radiation

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Proceedings Article | 16 October 2013 Paper

11-year variability of summer snow cover extent over Himalayas

Jung-Mok Ha, Kyung-Soo Han, Jae-Il Cho, Chang-Suk Lee, Kyoung-jin Pi, In-Hwan Kim, Jae-Hyun Ryu, Eun-Bin Park

Proceedings Volume 8887, 88871K (2013) https://doi.org/10.1117/12.2029146

KEYWORDS: Snow cover, Climatology, MODIS, Climate change, Clouds, Agriculture, Satellites, Meteorology, Reflectivity, Water

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Proceedings Article | 16 October 2013 Paper

Estimation of maximum air temperature using COMS data in Northeast Asia

Jae-Hyun Ryu, Kyung-Soo Han, Jae-Il Cho, Chang-Suk Lee, In-Hwan Kim, Kyoung-Jin Pi, Jung-Mok Ha, Eun-Bin Park

Proceedings Volume 8887, 88871X (2013) https://doi.org/10.1117/12.2029143

KEYWORDS: Tantalum, Satellites, Meteorology, Meteorological satellites, Climatology, Vegetation, Temperature metrology, Ecosystems, Data analysis, Remote sensing

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Proceedings Article | 7 October 2011 Paper

Assessment of tundra-taiga boundary changes using MODIS LAI data

Min-ji Lee, Kyung-Soo Han, Kyoung-Jin Pi, Sang-Il Kim, In-Hwan Kim

Proceedings Volume 8174, 81741G (2011) https://doi.org/10.1117/12.898370

KEYWORDS: Vegetation, Climatology, Climate change, MODIS, Data acquisition, Satellites, Sensors, Environmental sensing, Remote sensing, Ecosystems

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Proceedings Article | 7 October 2011 Paper

An improvement of satellite-based algorithm for gross primary production estimation optimized over Korea

Kyoung-Jin Pi, Kyung-Soo Han, In-Hwan Kim, Sang-Il Kim, Min-Ji Lee

Proceedings Volume 8174, 81741F (2011) https://doi.org/10.1117/12.898061

KEYWORDS: MODIS, Carbon, Ecosystems, Vegetation, Satellites, Coastal modeling, Solar radiation models, Clouds, Climatology, Sensors

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Proceedings Article | 22 October 2010 Paper

Inter-annual variation of NDVI over Korea Peninsula using harmonic analysis

In-hwan Kim, Kyung-Soo Han, Kyoung-Jin Pi, Soo-Jae Park, Sang-Il Kim

Proceedings Volume 7824, 78240B (2010) https://doi.org/10.1117/12.868555

KEYWORDS: Vegetation, Climate change, Analytical research, Climatology, Ecosystems, Composites, Remote sensing, Environmental sensing, Satellites, Negative feedback

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Proceedings Article | 22 October 2010 Paper

Dynamic threshholds for land surface change detection using image differencing

Sang-il Kim, Kyung-Soo Han, In-Hwan Kim, Jong-Min Yeom, Kyoung-Jin Pi

Proceedings Volume 7824, 78241O (2010) https://doi.org/10.1117/12.868556

KEYWORDS: Satellites, Satellite imaging, Earth observing sensors, Vegetation, Image resolution, Environmental sensing, Landsat, Environmental management, Information visualization, Remote sensing

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Proceedings Article | 10 October 2009 Paper

Water stress monitoring using NDWI around deserts of China and Mongolia

Ga-Lam Lee, Kyung-Soo Han, Jong-Min Yeom, Kyoung-Jin Pi, Soo-Jae Park

Proceedings Volume 7478, 74780Z (2009) https://doi.org/10.1117/12.830260

KEYWORDS: Vegetation, Satellites, Liquids, Climatology, Reflectivity, Absorption, Sensors, Scattering, Temporal resolution, Environmental sensing

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Showing 5 of 12 publications

Conference Committee Involvement (3)

Remote Sensing of the Atmosphere, Clouds, and Precipitation VIII

2 December 2024 | Kaohsiung, Taiwan

Remote Sensing of the Atmosphere, Clouds, and Precipitation VII

24 September 2018 | Honolulu, Hawaii, United States

Remote Sensing of the Atmosphere, Clouds, and Precipitation VI

4 April 2016 | New Delhi, India

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