Kwang‐Sik Yoon

1.4k total citations
84 papers, 1.2k citations indexed

About

Kwang‐Sik Yoon is a scholar working on Water Science and Technology, Soil Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Kwang‐Sik Yoon has authored 84 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Water Science and Technology, 26 papers in Soil Science and 24 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Kwang‐Sik Yoon's work include Agriculture, Soil, Plant Science (24 papers), Hydrology and Watershed Management Studies (24 papers) and Soil and Water Nutrient Dynamics (23 papers). Kwang‐Sik Yoon is often cited by papers focused on Agriculture, Soil, Plant Science (24 papers), Hydrology and Watershed Management Studies (24 papers) and Soil and Water Nutrient Dynamics (23 papers). Kwang‐Sik Yoon collaborates with scholars based in South Korea, United States and Canada. Kwang‐Sik Yoon's co-authors include Woo‐Jung Choi, Sang‐Soo Baek, Kyung Hwa Cho, Jae‐Woon Jung, Dongho Choi, Sang-Sun Lim, Hyung‐Jin Lee, Hyuk Lee, Jin‐Hyeob Kwak and Han-Yong Kim and has published in prestigious journals such as The Science of The Total Environment, Water Research and Bioresource Technology.

In The Last Decade

Kwang‐Sik Yoon

79 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kwang‐Sik Yoon South Korea 18 428 385 327 318 254 84 1.2k
Holger Rupp Germany 21 303 0.7× 354 0.9× 302 0.9× 375 1.2× 284 1.1× 62 1.4k
John Hollis United Kingdom 22 404 0.9× 515 1.3× 526 1.6× 336 1.1× 293 1.2× 40 1.5k
D. B. Jaynes United States 20 569 1.3× 272 0.7× 425 1.3× 220 0.7× 115 0.5× 29 1.1k
J. K. Koelliker United States 16 302 0.7× 471 1.2× 313 1.0× 235 0.7× 299 1.2× 43 1.1k
A. Tiktak Netherlands 23 306 0.7× 208 0.5× 283 0.9× 254 0.8× 327 1.3× 82 1.4k
C. L. Munster United States 21 163 0.4× 309 0.8× 288 0.9× 187 0.6× 231 0.9× 82 1.1k
John J. Sloan United States 16 290 0.7× 514 1.3× 204 0.6× 189 0.6× 166 0.7× 40 1.4k
Changsheng Jiang China 17 133 0.3× 335 0.9× 196 0.6× 209 0.7× 228 0.9× 59 1.1k
Girisha Ganjegunte United States 22 206 0.5× 211 0.5× 510 1.6× 155 0.5× 156 0.6× 68 1.5k
Petra Kahle Germany 20 116 0.3× 319 0.8× 289 0.9× 353 1.1× 126 0.5× 46 1.0k

Countries citing papers authored by Kwang‐Sik Yoon

Since Specialization
Citations

This map shows the geographic impact of Kwang‐Sik Yoon'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 Kwang‐Sik Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kwang‐Sik Yoon more than expected).

Fields of papers citing papers by Kwang‐Sik Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kwang‐Sik Yoon. 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 Kwang‐Sik Yoon. The network helps show where Kwang‐Sik Yoon may publish in the future.

Co-authorship network of co-authors of Kwang‐Sik Yoon

This figure shows the co-authorship network connecting the top 25 collaborators of Kwang‐Sik Yoon. A scholar is included among the top collaborators of Kwang‐Sik Yoon 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 Kwang‐Sik Yoon. Kwang‐Sik Yoon 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
2.
Jang, Jiyi, et al.. (2023). Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning. Journal of Hydrology. 624. 129973–129973. 8 indexed citations
3.
Park, Hyun-Jin, Kwang-Seung Lee, Jin‐Hyeob Kwak, et al.. (2022). Assessment of sources variability of riverine particulate organic matter with land use and rainfall changes using a three-indicator (δ13C, δ15N, and C/N) Bayesian mixing model. Environmental Research. 216(Pt 3). 114653–114653. 4 indexed citations
4.
Song, Jung‐Hun, et al.. (2020). Evaluating the Applicability of Drainage Routing Schemes for Paddy Fields. Journal of Irrigation and Drainage Engineering. 146(9). 2 indexed citations
5.
Yang, Hye In, Hyun-Jin Park, Dong Hwan Lee, et al.. (2019). Vegetated ridge and sandbag may not reduce soil erosion and loss of carbon and nutrients from upland fields. Soil Science & Plant Nutrition. 66(1). 195–205. 5 indexed citations
6.
Yoo, Seung‐Hwan, et al.. (2018). Water Supply Alternatives for Drought by Weather Scenarios Considering Resilience: Focusing on Naju Reservoir. Journal of The Korean Society of Agricultural Engineers. 60(5). 115–124. 1 indexed citations
7.
Song, Jung‐Hun, et al.. (2018). Simulating Arsenic Concentration Changes in Small Agricultrual Reservoir Using EFDC-WASP Linkage Model. Journal of The Korean Society of Agricultural Engineers. 60(5). 29–40. 2 indexed citations
9.
Lee, Yongwoon, et al.. (2010). Development of a Method for Estimating Non-Point Pollutant Delivery Load of Each Reference Flow with Combination of BASINS/HSPF. Journal of Korean Society of Environmental Engineers. 32(2). 175–184. 2 indexed citations
10.
Jung, Jae‐Woon, et al.. (2008). Analysis of Purification Capacity of Paddy Fields Using Nutrient Balance. 10(3). 1–7. 2 indexed citations
11.
Choi, Woo‐Jung, et al.. (2006). Available Organic Carbon Controls Nitrification and Immobilization of Ammonium in an Acid Loam-Textured Soil. Journal of Applied Biological Chemistry. 49(1). 28–32. 13 indexed citations
12.
Yoon, Kwang‐Sik, et al.. (2006). WATER MANAGEMENT AND N, P LOSSES FROM PADDY FIELDS IN SOUTHERN KOREA1. JAWRA Journal of the American Water Resources Association. 42(5). 1205–1216. 34 indexed citations
13.
Yoon, Chun Gyeong, et al.. (2005). The Comparison of Water Budget and Nutrient Loading from Paddy Field According to the Irrigation Methods. Journal of Ecology and Environment. 38(1). 118–127. 3 indexed citations
14.
Choi, Jin‐Yong, Bernard A. Engel, & Kwang‐Sik Yoon. (2003). GIS AND WEB-BASED DSS FOR PRELIMINARY TMDL DEVELOPMENT. 4(1). 19–30. 2 indexed citations
15.
Jeon, Ji‐Hong, et al.. (2003). Water Quality Model Development for Loading Estimates from Paddy Field. Journal of Ecology and Environment. 36(3). 344–355. 2 indexed citations
16.
Yoon, Kwang‐Sik, et al.. (2002). Water and Nutrient Balance of Paddy Field Irrigated from a Pumping Station during Cropping Period. Journal of Korean Society of Rural Planning. 8(1). 15–25. 6 indexed citations
17.
Yoon, Kwang‐Sik, et al.. (2002). Flow Weighted Mean Concentration and Runoff-Mass Load Relationship of Pollutants Derived from Intensively Sampled Water Quality Data of a Paddy Field. Journal of The Korean Society of Agricultural Engineers. 44(5). 127–135. 1 indexed citations
18.
Yoon, Kwang‐Sik, et al.. (2002). Effect of Tillage Management of Paddy Field on Runoff and Nutrient Losses during Non-Cropping Season. 44(7). 53–63. 1 indexed citations
19.
Yoon, Kwang‐Sik, et al.. (2001). Prediction of the Pollutant Loading into Estuary Lake according to Non-cultivation and Cultivation conditions of Reclaimed Tidal Land. Journal of Korean Society of Rural Planning. 7(1). 27–36. 2 indexed citations
20.
J, Kim, et al.. (1999). Occupational Cancer Surveillance System Using Data Linkage Analysis in Korea.. 21(2). 276–282. 1 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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