Jong‐Min Yeom

1.4k total citations
77 papers, 1.0k citations indexed

About

Jong‐Min Yeom is a scholar working on Ecology, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Jong‐Min Yeom has authored 77 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Ecology, 42 papers in Global and Planetary Change and 33 papers in Atmospheric Science. Recurrent topics in Jong‐Min Yeom's work include Remote Sensing in Agriculture (41 papers), Remote Sensing and Land Use (20 papers) and Solar Radiation and Photovoltaics (15 papers). Jong‐Min Yeom is often cited by papers focused on Remote Sensing in Agriculture (41 papers), Remote Sensing and Land Use (20 papers) and Solar Radiation and Photovoltaics (15 papers). Jong‐Min Yeom collaborates with scholars based in South Korea, Australia and France. Jong‐Min Yeom's co-authors include Seungtaek Jeong, Jonghan Ko, Hyun-Ok Kim, Kyung‐Soo Han, Chang-Suck Lee, Ravinesh C. Deo, Seonyoung Park, Do‐Woo Kim, Hyun Ok Kim and Jae‒Jin Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and The Science of The Total Environment.

In The Last Decade

Jong‐Min Yeom

70 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jong‐Min Yeom South Korea 21 517 429 300 286 218 77 1.0k
Bimal K. Bhattacharya India 17 352 0.7× 536 1.2× 493 1.6× 334 1.2× 165 0.8× 86 1.0k
Shujing Qin China 23 207 0.4× 725 1.7× 296 1.0× 189 0.7× 240 1.1× 49 1.3k
Lamin R. Mansaray China 23 566 1.1× 426 1.0× 404 1.3× 430 1.5× 307 1.4× 46 1.3k
Sherrie Wang United States 13 538 1.0× 317 0.7× 259 0.9× 218 0.8× 265 1.2× 31 1.1k
Jurandir Zullo Brazil 16 300 0.6× 229 0.5× 215 0.7× 100 0.3× 284 1.3× 65 910
Filiz Bektaş Balçık Türkiye 13 318 0.6× 373 0.9× 250 0.8× 144 0.5× 74 0.3× 37 703
Daxiang Xiang China 8 568 1.1× 372 0.9× 348 1.2× 229 0.8× 122 0.6× 19 898
Yonghua Qu China 18 734 1.4× 620 1.4× 545 1.8× 276 1.0× 334 1.5× 66 1.3k
Shuai Gao China 22 899 1.7× 432 1.0× 704 2.3× 148 0.5× 278 1.3× 67 1.4k

Countries citing papers authored by Jong‐Min Yeom

Since Specialization
Citations

This map shows the geographic impact of Jong‐Min Yeom's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jong‐Min Yeom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jong‐Min Yeom more than expected).

Fields of papers citing papers by Jong‐Min Yeom

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jong‐Min Yeom. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jong‐Min Yeom. The network helps show where Jong‐Min Yeom may publish in the future.

Co-authorship network of co-authors of Jong‐Min Yeom

This figure shows the co-authorship network connecting the top 25 collaborators of Jong‐Min Yeom. A scholar is included among the top collaborators of Jong‐Min Yeom based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jong‐Min Yeom. Jong‐Min Yeom is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jeong, Seungtaek, et al.. (2024). Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction. Ecological Informatics. 84. 102886–102886. 10 indexed citations
2.
3.
Yu, J., et al.. (2024). Enhanced Short-Term Prediction of Solar Radiation Using HRNet Model With Geostationary Satellite Data. IEEE Geoscience and Remote Sensing Letters. 21. 1–5. 2 indexed citations
4.
Jeong, Seungtaek, et al.. (2023). The Introduction of Naju Ground Observation Site Measurement Data and Web Service for Validation of Satellite Value-Added Products. SHILAP Revista de lepidopterología. 5(2). 103–109.
5.
Park, Seonyoung, et al.. (2022). Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems. Remote Sensing. 14(23). 6161–6161. 1 indexed citations
6.
Ko, Jonghan, et al.. (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). 1972–1972. 7 indexed citations
7.
Lee, Chang-Suck, et al.. (2021). Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning. Remote Sensing. 13(11). 2121–2121. 17 indexed citations
8.
Xue, Wei, Seungtaek Jeong, Jonghan Ko, & Jong‐Min 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). 3992–3992. 5 indexed citations
9.
Yeom, Jong‐Min, Ravinesh C. Deo, Jan Adamowski, Seonyoung Park, & Chang-Suck 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(9). 94025–94025. 31 indexed citations
10.
Lee, Chang-Suck, et al.. (2020). Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument. Remote Sensing. 12(15). 2500–2500. 13 indexed citations
11.
Jeong, Seungtaek, et al.. (2019). Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity. Remote Sensing. 11(18). 2131–2131. 20 indexed citations
12.
13.
Ko, Jonghan, et al.. (2019). Delineation of Rice Productivity Projected via Integration of a Crop Model with Geostationary Satellite Imagery in North Korea. National Remote Sensing Bulletin. 35(1). 57–81. 1 indexed citations
14.
Yeom, Jong‐Min, Jonghan Ko, Jisoo Hwang, et al.. (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(5). 697–697. 10 indexed citations
15.
Jeong, Seungtaek, Jonghan Ko, & Jong‐Min 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(10). 1665–1665. 23 indexed citations
16.
Yeom, Jong‐Min, et al.. (2017). New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data. Journal of Sensors. 2017. 1–15. 11 indexed citations
17.
Lee, Chang-Suck, Jong‐Min Yeom, Jinkyu Hong, et al.. (2017). Surface albedo from the geostationary Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager (MI) observation system. GIScience & Remote Sensing. 55(1). 38–62. 5 indexed citations
18.
19.
Yeom, Jong‐Min, et al.. (2016). Radiometric Characteristics of KOMPSAT-3 Multispectral Images Using the Spectra of Well-Known Surface Tarps. IEEE Transactions on Geoscience and Remote Sensing. 54(10). 5914–5924. 8 indexed citations
20.
Yeom, Jong‐Min, et al.. (2008). An Improved Validation Technique for the Temporal Discrepancy when Estimated Solar Surface Insolation Compare with Ground-based Pyranometer: MTSAT-1R Data use. National Remote Sensing Bulletin. 24(6). 605–612. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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