S. W. Running

14.8k total citations · 3 hit papers
51 papers, 4.9k citations indexed

About

S. W. Running is a scholar working on Global and Planetary Change, Ecology and Atmospheric Science. According to data from OpenAlex, S. W. Running has authored 51 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Global and Planetary Change, 22 papers in Ecology and 14 papers in Atmospheric Science. Recurrent topics in S. W. Running's work include Plant Water Relations and Carbon Dynamics (21 papers), Remote Sensing in Agriculture (20 papers) and Climate variability and models (9 papers). S. W. Running is often cited by papers focused on Plant Water Relations and Carbon Dynamics (21 papers), Remote Sensing in Agriculture (20 papers) and Climate variability and models (9 papers). S. W. Running collaborates with scholars based in United States, United Kingdom and Mexico. S. W. Running's co-authors include Stith T. Gower, Ramakrishna Nemani, David P. Turner, Maosheng Zhao, Warren B. Cohen, Lars L. Pierce, William D. Ritts, Peter S. Bakwin, Dennis Baldocchi and Kathy Hibbard and has published in prestigious journals such as Nature, Journal of Geophysical Research Atmospheres and PLoS ONE.

In The Last Decade

S. W. Running

48 papers receiving 4.5k citations

Hit Papers

Evaluation of MODIS NPP and GPP products across m... 1991 2026 2002 2014 2006 1999 1991 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. W. Running United States 29 3.8k 2.2k 1.2k 1.1k 743 51 4.9k
Faith Ann Heinsch United States 13 4.4k 1.2× 2.5k 1.1× 1.1k 0.9× 833 0.8× 529 0.7× 29 5.3k
J. H. McCaughey Canada 39 4.0k 1.1× 1.6k 0.7× 894 0.7× 1.5k 1.4× 808 1.1× 84 4.9k
Kazuhito Ichii Japan 32 3.5k 0.9× 1.9k 0.9× 931 0.8× 1.1k 1.0× 398 0.5× 93 4.5k
Xiang Zhao China 37 4.0k 1.1× 2.3k 1.0× 1.5k 1.2× 1.7k 1.5× 654 0.9× 169 5.8k
Ronggao Liu China 31 3.0k 0.8× 2.1k 1.0× 1.0k 0.8× 1.1k 1.0× 381 0.5× 92 4.2k
Laurent Kergoat France 36 3.3k 0.9× 2.0k 0.9× 1.1k 0.9× 1.3k 1.2× 333 0.4× 100 4.8k
Natascha Kljun United Kingdom 33 4.1k 1.1× 1.6k 0.7× 1.2k 1.0× 1.7k 1.6× 705 0.9× 98 5.3k
James Cleverly Australia 41 3.8k 1.0× 2.0k 0.9× 874 0.7× 1.1k 1.0× 676 0.9× 100 4.9k
David P. Turner United States 37 4.5k 1.2× 3.4k 1.5× 2.0k 1.6× 923 0.8× 1.3k 1.8× 78 6.4k
J. T. Morisette United States 13 3.2k 0.9× 2.4k 1.1× 1.2k 1.0× 1.1k 1.0× 270 0.4× 24 4.5k

Countries citing papers authored by S. W. Running

Since Specialization
Citations

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

Fields of papers citing papers by S. W. Running

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. W. Running. 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 S. W. Running. The network helps show where S. W. Running may publish in the future.

Co-authorship network of co-authors of S. W. Running

This figure shows the co-authorship network connecting the top 25 collaborators of S. W. Running. A scholar is included among the top collaborators of S. W. Running 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 S. W. Running. S. W. Running 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.
He, Mingzhu, John S. Kimball, Yonghong Yi, et al.. (2019). Impacts of the 2017 flash drought in the US Northern plains informed by satellite-based evapotranspiration and solar-induced fluorescence. Environmental Research Letters. 14(7). 74019–74019. 58 indexed citations
2.
Kimball, Heather, Paul C. Selmants, Álvaro Moreno‐Martínez, S. W. Running, & Christian P. Giardina. (2017). Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems. PLoS ONE. 12(9). e0184466–e0184466. 15 indexed citations
3.
Tallis, Heather, Harold A. Mooney, Sandy Andelman, et al.. (2012). A Global System for Monitoring Ecosystem Service Change. BioScience. 62(11). 977–986. 124 indexed citations
4.
Mildrexler, David J., Meng Zhao, & S. W. Running. (2011). Detection of Terrestrial Ecosystem Disturbances Using Aqua/MODIS Land Surface Temperature and Enhanced Vegetation Index. AGU Fall Meeting Abstracts. 2011. 1 indexed citations
5.
Zhao, Maosheng, et al.. (2010). Description of the Improvements on MODIS Global Terrestrial Evapotranspiration. AGU Fall Meeting Abstracts. 2010. 1 indexed citations
6.
Woessner, William, et al.. (2009). Ecohydrologic Process Modeling of Mountain Block Groundwater Recharge. Ground Water. 47(6). 774–785. 20 indexed citations
7.
Running, S. W.. (2006). Trends and constraints in global terrestrial primary productivity. AGUSM. 2007. 1 indexed citations
8.
Hoffman, Forrest M., Inez Fung, Peter Thornton, et al.. (2006). Preliminary Results from the CCSM Carbon-Land Model Intercomparison Project (C- LAMP). AGUFM. 2006. 2 indexed citations
9.
Heinsch, Faith Ann, et al.. (2006). Evaluation of satellite-based evapotranspiration using fluxnet data. AGUSM. 2007. 1 indexed citations
10.
Huete, Alfredo, S. W. Running, & Ranga B. Myneni. (2006). Monitoring Rainforest Dynamics in the Amazon with MODIS Land Products. 263–265. 5 indexed citations
11.
Kimball, John S., et al.. (2005). Pan-Arctic Freeze/Thaw Algorithm Development Using AMSR-E Data and Satellite Remote Sensing Technique. AGU Fall Meeting Abstracts. 2005. 1 indexed citations
12.
Zhao, Maosheng, et al.. (2004). Sensitivity Of MODIS Global Terrestrial Primary Production To The Accuracy Of Meteorological Data Sets. AGUFM. 2004. 1 indexed citations
13.
Reichstein, Markus, et al.. (2003). Evaluation of MODIS-driven estimates of vegetation productivity at European FLUXNET sites. EGS - AGU - EUG Joint Assembly. 5608. 2 indexed citations
14.
Nemani, Ramakrishna, et al.. (2003). Development of an evapotranspiration index from aqua/MODIS for monitoring surface moisture status. IEEE Transactions on Geoscience and Remote Sensing. 41(2). 493–501. 130 indexed citations
15.
Churkina, Galina, S. W. Running, & Annette L. Schloss. (1999). Comparing global models of terrestrial net primary productivity (NPP): The importance of water availability to primary productivity. Global Change Biology. 3 indexed citations
16.
Running, S. W., J. Way, K. C. McDonald, et al.. (1999). Radar remote sensing proposed for monitoring freeze‐thaw transitions in boreal regions. Eos. 80(19). 213–221. 40 indexed citations
17.
Kimball, John S., Peter Thornton, Michael A. White, & S. W. Running. (1997). Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region. Tree Physiology. 17(8-9). 589–599. 155 indexed citations
18.
Keane, Robert E., Kevin C. Ryan, & S. W. Running. (1996). Simulating effects of fire on northern Rocky Mountain landscapes with the ecological process model FIRE-BGC. Tree Physiology. 16(3). 319–331. 79 indexed citations
20.
Running, S. W. & Stith T. Gower. (1991). FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiology. 9(1-2). 147–160. 494 indexed citations breakdown →

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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