● 연구분야

환경원격탐사

인공지능 (머신러닝, 딥러닝)

위성영상 처리 및 모델링 연구

신재생에너지 잠재량 모델링 연구

지구환경 미래예측 기술 연구

● 주요학력

2005.03∼2009.02 환경대기과학과 부경대학교 (이학박사)

2003.03∼2005.02 환경대기과학과 부경대학교 (이학석사)

1996.03∼2003.02 환경대기과학과 부경대학교 (이학사)

● 주요경력

2023.04∼현재 전북대학교 지구환경과학과, 부교수

2010.09∼2023.03 한국항공우주연구원, 선임연구원

2015.03∼2018.02 UST 과학기술연합대학원대학교, 부교수

2009.09∼2010.08 미국 South Dakota State University, Geospatial Sciences Center of Excellence, Post Doc

2009.03∼2009.08 부경대학교 BK21 지구환경시스템사업단, 박사후연구원

● 기타경력

2013.01∼현재 대한원격탐사학회, 편집위원

2019.10∼현재 GEO DATA Journal, 편집위원

● 수상경력

2022.10 우수학술상, GeoAI데이터학회

2022.10 우수포스터상, 한국대기환경학회

2015.10 우수연구상, 한국항공우주연구원

2008.10 우수논문상, Asia GIS 학회

● 연구실적 (*교신저자)

· S. Jeong, Y. Ryu, B. Dechant, X. Li, J. Kong, W. Choi, M. Kang, J.M. Yeom, J. Lim, K. Jang, J. Chun, 2023, Tracking diurnal to seasonal variations of gross primary productivity using a geostationary satellite, GK-2A advanced meteorological imager. Remote Sensing of Environment, 284.

· S. Park, J. Lee, J.M. Yeom*, E. Seo, J. Im, 2022, Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems. Remote Sensing, 14(23).

· S. Jeong, J. Ko, T. Shin, J.M. Yeom, 2022, Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth. Scientific Reports, 12(1).

· J.-H. Ryu, D. Oh, J. Ko, H.Y. Kim, J.M. Yeom, J. Cho, 2022, Remote Sensing-Based Evaluation of Heat Stress Damage on Paddy Rice Using NDVI and PRI Measured at Leaf and Canopy Scales. Agronomy, 12(8)

· W. Xue, S. Jeong, J. Ko, J.M. Yeom, 2021, Contribution of Biophysical Factors to Regional Variations of Evapotranspiration and Seasonal Cooling Effects in Paddy Rice in South Korea. Remote Sensing, 13(19), 3992.

· J.M. Yeom, S. Jeong, J.S. Ha, K.H. Lee, C.S. Lee, S. Park, 2021, Estimation of the Hourly Aerosol Optical Depth from GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models. IEEE Transactions on Geoscience and Remote Sensing, 60.

· S. Jeong, J. Ko*, J.M. Yeom*, 2021, Predicting scalable rice yield through synthetic use of crop and deep learning models with satellite data in South and North Korea. Science of the Total Environment, 802.

· C. Lee, K. Lee, S. Kim, J. Yu, S. Jeong, J.M. Yeom*, 2021, Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning. Remote Sensing, 13(11), 2121.

· J.M. Yeom, S. Jeong, R.C. Deo, J. Ko, 2021, Mapping rice area and yield in northeastern asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite. GIScience & Remote Sensing, 58(1), 1-27.

· J.M. Yeom, R.C. Deo, J.F. Adamowski, S. Park, C.S. Lee, 2020, Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea. Environmental Research Letters, 15.

· K.S. Lee, S.R. Chung, C. Lee, M. Seo, S. Choi, N.H. Seong, D. Jin, M. Kang, J.M. Yeom, J.L. Roujean, D. Jung, S. Sim, K.S. Han, 2020, Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument. Remote Sensing, 12.

· J.M. Yeom, J.L. Roujean, K.S. Han, K.S. Lee, H.W. Kim, 2020, Thin cloud detection over land using background surface reflectance based on the BRDF model from Geostationary Ocean Color Imager (GOCI) Satellite. Remote Sensing of Environment, 239.

· J.M. Yeom, R.C. Deo, J.F. Adamowski, T. Chae, D.S. Kim, K.S. Han, D.Y. Kim, 2020, Exploring solar and wind energy resources in North Korea with COMS MI Geostationary satellite data coupled with numerical weather prediction reanalysis variables. Renewable and Sustainable Energy Reviews, 119, 109570.

· S. Jeong, J. Ko*, M. Kang, J.M. Yeom*, C. Tim Ng, S. H. Lee, Y.G. Lee, H.Y. Kim, 2020, Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula. Science of the Total Environment, 714.

· K.S. Lee, C.S. Lee, M. Seo, S. Choi, N.H. Seong, D. Jin, J.M. Yeom, K.S. Han, 2020, Improvements of 6S Look-Up-Table Based Surface Reflectance Employing Minimum Curvature Surface Method. Asia-Pacific Journal of Atmospheric Sciences, 56.

· V.C. Nguyen, S. Jeong, J. Ko, C.T. Ng, J.M. Yeom, 2019, Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity. Remote Sensing, 11, 2131.

· J.M. Yeom, S. Park, T. Chae, J.Y. Kim, C.S. Lee, 2019, Spatial assessment of solar radiation by machine learning and deep neural network models using data provided by the COMS MI geostationary satellite: a case study in South Korea. Sensors, 19, 2082.

· J.M. Yeom, S. Jeong, G. Jeong, C.T. Ng, R.C. Deo, J. Ko, 2018, Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model. Scientific Reports, 8:16121.

· S. Jeong, J. Ko*, J.M. Yeom*, 2018, Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea. Remote Sensing, 10, 1665.

· J.M. Yeom, J. Ko, J. Hwang, C.S. Lee, C.U. Choi, S. Jeong, 2018, Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II. Remote Sensing, 10, 697.

· S. Jeong, J. Ko*, J. Choi, W. Xue, J.M. Yeom*, 2018, Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model. International Journal of Remote Sensing, 39(8), 2441-2462.

· S. Hong, Y.W. Lee, J.H. Ryu, J.M. Yeom, W. Kim, J. Cho, 2018, Satellite-based assessment of rapid mega-urban development on agricultural land. Journal of Agricultural Meteorology, 74(2): 87-91.

· K.S. Lee, D. Jin, J.M. Yeom, M. Seo, S. Choi, J.J. Kim, K.S. Han, 2017, New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data. Journal of Sensors, 2017, 15.

· H.W. Kim, J.M. Yeom*, D.G. Shin, S.W. Choi, K.S. Han, J. L. Roujean, 2017, An assessment of thin cloud detection by applying bidirectional reflectance distribution function model-based background surface reflectance using Geostationary Ocean Color Imager (GOCI): a case study for South Korea. Journal of Geophysical Research: Atmospheres, 122.

· C.S. Lee, K.S. Han, J.M. Yeom, K. Lee , M. Seo, J. Hong, J.W. Hong , K. Lee, J. Shin, I. Chul S., J. Chun, J.L. Roujean, 2017, Surface albedo from the geostationary Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager (MI) observation system. GIScience & Remote Sensing, 55.

· D.W. Kim, R.C. Deo, J.S. Lee, J.M. Yeom, 2017, Mapping heatwave vulnerability in Korea. Natural Hazards, 89.

· J.M. Yeom, R.C. Deo, J.H. Chun, J.K. Hong, D.S. Kim, K.S. Han, J.I. Cho, 2017, Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements. International Journal of Remote Sensing, 38.

· J.M. Yeom, J.S. Hwang, J.H. Jung, K. H. Lee, C. S. Lee, 2017, Initial radiometric characteristics of KOMPSAT-3A multispectral imagery using the 6S radiative transfer model, well-known radiometric tarps, and MFRSR measurements. Remote Sensing, 9(130).

· M. Kim, J. Ko, S. Jeong, J.M. Yeom, H. Kim, 2017, Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery. GIScience & Remote Sensing.

· J.M. Yeom, J.S. Hwang, C.G. Jin, D. H. Lee, K. S. Han, 2016, Radiometric characteristics of KOMPSAT-3 Multispectal using the spectra of well-known surface tarps. IEEE Transactions on Geoscience and Remote Sensing, 54(10).

· J.M. Yeom, Y.K. Seo, D.S. Kim, K.S. Han, 2016, Solar radiation received by slopes using COMS imagery, a physically based radiation model, and GLOBE. Journal of Sensors, 2016.

· S.l. Kim, D.S. Ahn, K.S. Han, J.M. Yeom*, 2016, Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite. Journal of Sensors, 2016.

· M. Seo, H.C. Kim, M. Huh, J.M. Yeom, C.S. Lee, K.S. Lee, S. Choi, K.S. Han, 2016, Long-Term Variability of Surface Albedo and Its Correlation with Climatic Variables over Antarctica. Remote Sensing, 8, 981.

· J.M. Yeom, H.O. Kim, 2015, Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice. Remote Sensing, 7, 113-11343.

· J.M. Yeom, J.H. Ko, H.O. Kim, 2015, Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model. Computers and Electronics in Agricultures, 118, 1-8.

· K.S. Han, Y.Y. Park, J.M. Yeom*, 2015, Detection of Change in Vegetation in the Surrounding Desert Areas of Northwest China and Mongolia with Multi-Temporal Satellite Images. Asia-Pacific Journal of Atmospheric Science, 51(2), 173-181.

· J.M. Yeom, C.S. Lee, S.J. Park, J.H. Ryu, J.J. Kim H.C. Kim, K.S. Han, 2015, Evapotranspiration in Korea estimated by application of a neural network to satellite images. Remote Sensing Letters, 6(6), 429-438.

· K.S. Han, J.M. Yeom*, C.S. Lee, I.C. Shin, D.H. Kim, 2015, Improved estimation of insolation by using calibrated COMS MI images over South Korea. Remote Sensing Letters, 6(3), 175-182.

· H.O. Kim, J.M. Yeom*, 2015, Sensitivity of vegetation indices to spatial degradation of RapidEye imagery for paddy rice detection: a case study of South Korea. GIScience & Remote Sensing, 52(1), 1-17.

· J.H. Ryu, K.S. Han, J. Cho, C.S. Lee, H.J. Woon, J.M. Yeom, M.L. Ou, 2015, Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data. International Journal of Remote Sensing, 36(13), 3503-3518.

· J. Ko, S. Jeong, J.M. Yeom, H. Kim, J.O. Ban, H.Y. Kim, 2015, Simulation and mapping of rice growth and yield based on remote sensing. Journal of Applied Remote Sensing, 9.

· C.S. Lee, J.M. Yeom, H.L. Lee, J.J. Kim, K.S. Han, 2015, Sensitivity Analysis of 6S-Based Look-Up Table for Surface Reflectance Retrieval. Asia-Pacific Journal of Atmospheric Sciences, 51(1), 91-101.

· E.B. Park, K.S. Han, J.M. Yeom, C.S. Lee, J.H. Ryu, H. Kim, D.H. Kim, 2015, Effects of GSICS correction on estimation of sea surface temperature using COMS data. International Journal of Remote Sensing, 36(4), 1026-1037.

· H.O. Kim, J.M. Yeom*, 2014, Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multispectral satellite image data. International Journal of Remote Sensing, 35(19), 7046-7068.

· J.M. Yeom, H.O. Kim, 2013, Feasibility of using Geostationary Ocean Colour Imager (GOCI) data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling. International Journal of Remote Sensing, 34(20), 7329-7339.

· J.M. Yeom, K.S. Han, J. J. Kim, 2012, Evaluation on Penetration Rate of Cloud for Incoming Solar Radiation Using Geostationary Satellite Data. Asia-Pacific Journal of Atmospheric Sciences, 115-123.

· J.M. Yeom, K.S. Han, 2010, Improved estimation of surface solar insolation using a neural network and MTSAT-1R data. Computers & Geosciences, 36, 590-597.

· J.M. Yeom, K.S. Han, 2009, An Efficiency Analysis for Data Synthesis of Sun- and Geo-Synchronous Satellites in Kernel-driven BRDF Model. Asia-Pacific Journal of Atmospheric Sciences, 45(4), 499-511.

· J.M. Yeom, K.S. Han, Y.S. Kim, J.D. Jang, 2008, Neural network determination of cloud attenuation to estimate insolation using MTSAT-1R data. International Journal of Remote Sensing, 29(21), 6193-6208.