Kun Yang
- Atmospheric Science top 0.02%
- Cryospheric studies and observations 166
- Meteorological Phenomena and Simulations 103
- Climate change and permafrost 94
- Precipitation Measurement and Analysis 51
- Global and Planetary Change top 0.05%
- Climate variability and models 133
- Plant Water Relations and Carbon Dynamics 51
- Environmental Engineering top 0.05%
- Soil Moisture and Remote Sensing 83
- Water Science and Technology top 0.1%
- Hydrology and Watershed Management Studies 61
- Oceanography top 0.5%
- Journals
- Journal of Geophysical Research Atmospheres (25 papers)Remote Sensing of Environment (18 papers)Journal of Hydrology (15 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Kun Yang
461 papers receiving 21.2k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Atmospheric Science 13.8k
- Global and Planetary Change 12.4k
- Environmental Engineering 4.7k
- Water Science and Technology 4.4k
- Oceanography 1.4k
Countries citing papers authored by Kun Yang
This map shows the geographic impact of Kun Yang'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 Kun Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kun Yang more than expected).
Fields of papers citing papers by Kun Yang
This network shows the impact of papers produced by Kun Yang. 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 Kun Yang. The network helps show where Kun Yang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kun Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 2 | |
| 9 | 2023 | 12 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 39 | |
| 12 | 2023 | 43 | |
| 13 | 2023 | 3 | |
| 14 | 2023 | 2 | |
| 15 | 2023 | 1 | |
| 16 | 2022 | 30 | |
| 17 | The first high-resolution meteorological forcing dataset for land process studies over Chinabreakdown → | 2020 | 1018 |
| 18 | 2020 | 35 | |
| 19 | 2018 | 2 | |
| 20 | 2018 | 15 |
About Kun Yang
Kun Yang is a scholar working on Atmospheric Science, Global and Planetary Change, Environmental Engineering, Water Science and Technology and Oceanography, having authored 494 papers that have together received 21.7k indexed citations. Recurring topics across this work include Cryospheric studies and observations (166 papers), Climate variability and models (133 papers), Meteorological Phenomena and Simulations (103 papers), Climate change and permafrost (94 papers), Soil Moisture and Remote Sensing (83 papers), Hydrology and Watershed Management Studies (61 papers), Precipitation Measurement and Analysis (51 papers) and Plant Water Relations and Carbon Dynamics (51 papers). The work is most often cited by research in Atmospheric Science (13.8k citations), Global and Planetary Change (12.4k citations), Environmental Engineering (4.7k citations), Water Science and Technology (4.4k citations) and Oceanography (1.4k citations). Kun Yang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Jun Qin, Wenjun Tang, Yingying Chen, Toshio Koike, Hui Lü, Xin Li, Changgui Lin, Jie He, Lei Wang and Deliang Chen. Their work appears in journals such as Journal of Geophysical Research Atmospheres, Remote Sensing of Environment, Journal of Hydrology, Geophysical Research Letters and Science China Earth Sciences.
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.