E. J. Coopersmith

1.1k total citations
23 papers, 689 citations indexed

About

E. J. Coopersmith is a scholar working on Environmental Engineering, Water Science and Technology and Civil and Structural Engineering. According to data from OpenAlex, E. J. Coopersmith has authored 23 papers receiving a total of 689 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Environmental Engineering, 11 papers in Water Science and Technology and 9 papers in Civil and Structural Engineering. Recurrent topics in E. J. Coopersmith's work include Soil Moisture and Remote Sensing (13 papers), Hydrology and Watershed Management Studies (11 papers) and Soil and Unsaturated Flow (9 papers). E. J. Coopersmith is often cited by papers focused on Soil Moisture and Remote Sensing (13 papers), Hydrology and Watershed Management Studies (11 papers) and Soil and Unsaturated Flow (9 papers). E. J. Coopersmith collaborates with scholars based in United States, Australia and Austria. E. J. Coopersmith's co-authors include Murugesu Sivapalan, Mary Yaeger, Lei Cheng, Michael H. Cosh, Barbara Minsker, Alberto Viglione, Jesse E. Bell, Brian Gilmore, Craig S. T. Daughtry and M. Sivapalan and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Hydrology and earth system sciences.

In The Last Decade

E. J. Coopersmith

23 papers receiving 665 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
E. J. Coopersmith United States 13 391 329 285 207 111 23 689
Nan Shan China 14 220 0.6× 450 1.4× 61 0.2× 97 0.5× 20 0.2× 28 602
Wolfgang Korres Germany 10 136 0.3× 175 0.5× 326 1.1× 128 0.6× 117 1.1× 19 510
Russell J. Qualls United States 17 243 0.6× 617 1.9× 167 0.6× 265 1.3× 55 0.5× 34 686
Pierre Defourny Belgium 10 129 0.3× 161 0.5× 419 1.5× 202 1.0× 43 0.4× 23 632
Ansoumana Bodian Senegal 18 499 1.3× 777 2.4× 250 0.9× 157 0.8× 38 0.3× 55 1.0k
Victor Coelho Brazil 13 204 0.5× 295 0.9× 179 0.6× 217 1.0× 45 0.4× 38 560
Alison C. Rudd United Kingdom 18 416 1.1× 572 1.7× 154 0.5× 169 0.8× 20 0.2× 30 753
Vincent Rivalland France 15 178 0.5× 496 1.5× 349 1.2× 213 1.0× 108 1.0× 36 767
Sara Sadri United States 9 253 0.6× 462 1.4× 208 0.7× 185 0.9× 35 0.3× 14 680
David Grimsley United States 5 74 0.2× 241 0.7× 235 0.8× 264 1.3× 59 0.5× 6 544

Countries citing papers authored by E. J. Coopersmith

Since Specialization
Citations

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

Fields of papers citing papers by E. J. Coopersmith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. J. Coopersmith. 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 E. J. Coopersmith. The network helps show where E. J. Coopersmith may publish in the future.

Co-authorship network of co-authors of E. J. Coopersmith

This figure shows the co-authorship network connecting the top 25 collaborators of E. J. Coopersmith. A scholar is included among the top collaborators of E. J. Coopersmith 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 E. J. Coopersmith. E. J. Coopersmith 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.
Coopersmith, E. J., et al.. (2017). Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions. GeoHealth. 1(1). 51–63. 33 indexed citations
2.
Coopersmith, E. J., Michael H. Cosh, Jesse E. Bell, et al.. (2016). Deploying temporary networks for upscaling of sparse network stations. International Journal of Applied Earth Observation and Geoinformation. 52. 433–444. 10 indexed citations
3.
Coopersmith, E. J., Michael H. Cosh, Jesse E. Bell, & Wade T. Crow. (2016). Multi‐Profile Analysis of Soil Moisture within the US Climate Reference Network. Vadose Zone Journal. 15(1). 1–8. 17 indexed citations
4.
Coopersmith, E. J., Jesse E. Bell, & Michael H. Cosh. (2015). Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN). Advances in Water Resources. 79. 80–90. 17 indexed citations
5.
Coopersmith, E. J., Michael H. Cosh, Walt Petersen, John H. Prueger, & J. J. Niemeier. (2015). Soil Moisture Model Calibration and Validation: An ARS Watershed on the South Fork Iowa River. Journal of Hydrometeorology. 16(3). 1087–1101. 53 indexed citations
6.
Bell, Jesse E., et al.. (2015). Evaluation of the 2012 Drought with a Newly Established National Soil Monitoring Network. Vadose Zone Journal. 14(11). 1–7. 14 indexed citations
7.
Coopersmith, E. J., Michael H. Cosh, Rajat Bindlish, & Jesse E. Bell. (2015). Comparing AMSR-E soil moisture estimates to the extended record of the U.S. Climate Reference Network (USCRN). Advances in Water Resources. 85. 79–85. 14 indexed citations
8.
Coopersmith, E. J., Barbara Minsker, & Murugesu Sivapalan. (2014). Using similarity of soil texture and hydroclimate to enhance soil moisture estimation. Hydrology and earth system sciences. 18(8). 3095–3107. 12 indexed citations
9.
Coopersmith, E. J., Barbara Minsker, & Murugesu Sivapalan. (2014). Using hydro-climatic and edaphic similarity to enhance soil moisture prediction. 3 indexed citations
10.
Coopersmith, E. J., Barbara Minsker, & Murugesu Sivapalan. (2014). Patterns of regional hydroclimatic shifts: An analysis of changing hydrologic regimes. Water Resources Research. 50(3). 1960–1983. 47 indexed citations
11.
Coopersmith, E. J., et al.. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture. 104. 93–104. 62 indexed citations
12.
Coopersmith, E. J., Michael H. Cosh, & Craig S. T. Daughtry. (2014). Field-scale moisture estimates using COSMOS sensors: A validation study with temporary networks and Leaf-Area-Indices. Journal of Hydrology. 519. 637–643. 46 indexed citations
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Cheng, Lei, et al.. (2012). Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses. Hydrology and earth system sciences. 16(11). 4435–4446. 89 indexed citations
17.
Coopersmith, E. J., et al.. (2012). Exploring the physical controls of regional patterns of flow duration curves – Part 3: A catchment classification system based on regime curve indicators. Hydrology and earth system sciences. 16(11). 4467–4482. 84 indexed citations
18.
Yaeger, Mary, et al.. (2012). Exploring the physical controls of regional patterns of flow duration curves – Part 2: Role of seasonality, the regime curve, and associated process controls. Hydrology and earth system sciences. 16(11). 4447–4465. 70 indexed citations
19.
Coopersmith, E. J., Barbara Minsker, & Paul A. Montagna. (2010). Understanding and forecasting hypoxia using machine learning algorithms. Journal of Hydroinformatics. 13(1). 64–80. 12 indexed citations
20.
Coopersmith, E. J., Barbara Minsker, David R. Maidment, et al.. (2007). An Environmental Information System for Hypoxia in Corpus Christi Bay: A WATERS Network Testbed. World Environmental and Water Resources Congress 2007. 107. 1–14. 3 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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