Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Evaluation of MODIS NPP and GPP products across multiple biomes
2006538 citationsS. W. Running, Maosheng Zhao et al.profile →
A Global Terrestrial Monitoring Network Integrating Tower Fluxes, Flask Sampling, Ecosystem Modeling and EOS Satellite Data
1999525 citationsS. W. Running, Stith T. Gower et al.profile →
FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets
1991494 citationsS. W. Running, Stith T. GowerTree Physiologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
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).
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.
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
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
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
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.