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32 results on '"Seong, Noh-Hun"'

4. Improving atmospheric correction for KOMPSAT-3A by optimizing the 6SV look-up table.

Author

Jeong, Daeseong, Seong, Noh-Hun, Choi, Sungwon, Sim, Suyoung, Woo, Jongho, Kim, Nayeon, Park, Sungwoo, and Han, Kyung-Soo

Subjects
*ARTIFICIAL neural networks, *ZENITH distance, *REMOTE sensing, *ATMOSPHERIC models, *SOLAR spectra
Abstract

We present techniques for enhancing the look-up table (LUT) method, using the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6 SV) model for atmospheric correction (AC) and accurate surface reflectance (SR) computation across the multispectral (MS) KOMPSAT-3A channels. We improved the LUT-based SR accuracy using three interpolation and prediction methods for AC coefficients: minimum curvature surface (MCS), six-dimensional linear interpolation (6D), and a deep neural network (DNN). When assessed based on the solar zenith angle (SZA) and aerosol optical depth (AOD), MCS had limitations interpolating atmospheric effects, particularly at shorter wavelengths. The DNN method had high predictive ability for atmospheric effect variability with an increased SZA, but struggled to predict minor changes at low SZAs. In comparison, the 6D method, operating in real-time and considering all AC input variables, consistently retrieved high-quality SR across all MS channels. Our findings offer insights into each method's strengths and limitations, guiding future remote sensing research and applications. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Improving atmospheric correction for KOMPSAT-3A by optimizing the 6SV look-up table

Author

Jung, Daeseong, Seong, Noh-Hun, Choi, Sungwon, Sim, Suyoung, Woo, Jongho, Kim, Nayeon, Park, Sungwoo, and Han, Kyung-Soo

Abstract

ABSTRACTWe present techniques for enhancing the look-up table (LUT) method, using the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6 SV) model for atmospheric correction (AC) and accurate surface reflectance (SR) computation across the multispectral (MS) KOMPSAT-3A channels. We improved the LUT-based SR accuracy using three interpolation and prediction methods for AC coefficients: minimum curvature surface (MCS), six-dimensional linear interpolation (6D), and a deep neural network (DNN). When assessed based on the solar zenith angle (SZA) and aerosol optical depth (AOD), MCS had limitations interpolating atmospheric effects, particularly at shorter wavelengths. The DNN method had high predictive ability for atmospheric effect variability with an increased SZA, but struggled to predict minor changes at low SZAs. In comparison, the 6D method, operating in real-time and considering all AC input variables, consistently retrieved high-quality SR across all MS channels. Our findings offer insights into each method’s strengths and limitations, guiding future remote sensing research and applications.

Published
2024
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6. An Ensemble-Based Model for Specific Humidity Retrieval from Landsat-8 Satellite Data for South Korea.

Author

Choi, Sungwon, Seong, Noh-Hun, Jung, Daeseong, Sim, Suyoung, Woo, Jongho, Kim, Nayeon, Park, Sungwoo, and Han, Kyung-soo

Subjects
*STANDARD deviations, *ATMOSPHERIC models, *SURFACE of the earth, *HUMIDITY, *MACHINE learning, *ATMOSPHERE
Abstract

Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth's surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

Author

Lee, Kyeong-Sang, primary, Chung, Sung-Rae, additional, Lee, Changsuk, additional, Seo, Minji, additional, Choi, Sungwon, additional, Seong, Noh-Hun, additional, Jin, Donghyun, additional, Kang, Minseok, additional, Yeom, Jong-Min, additional, Roujean, Jean-Louis, additional, Jung, Daeseong, additional, Sim, Suyoung, additional, and Han, Kyung-Soo, additional

Published
2020
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