M. T. Glasscoe

848 total citations
36 papers, 207 citations indexed

About

M. T. Glasscoe is a scholar working on Geophysics, Global and Planetary Change and Artificial Intelligence. According to data from OpenAlex, M. T. Glasscoe has authored 36 papers receiving a total of 207 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Geophysics, 12 papers in Global and Planetary Change and 10 papers in Artificial Intelligence. Recurrent topics in M. T. Glasscoe's work include earthquake and tectonic studies (15 papers), Seismology and Earthquake Studies (9 papers) and Flood Risk Assessment and Management (9 papers). M. T. Glasscoe is often cited by papers focused on earthquake and tectonic studies (15 papers), Seismology and Earthquake Studies (9 papers) and Flood Risk Assessment and Management (9 papers). M. T. Glasscoe collaborates with scholars based in United States, France and United Kingdom. M. T. Glasscoe's co-authors include Donald L. Turcotte, Andrea Donnellan, Jay Parker, Marlon Pierce, Jun Wang, John B. Rundle, G. A. Lyzenga, K. F. Tiampo, Yu Ma and Geoffrey Fox and has published in prestigious journals such as SHILAP Revista de lepidopterología, Tectonophysics and Remote Sensing.

In The Last Decade

M. T. Glasscoe

36 papers receiving 204 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. T. Glasscoe United States 8 72 64 38 31 27 36 207
Brenda K. Jones United States 6 15 0.2× 120 1.9× 29 0.8× 75 2.4× 52 1.9× 10 273
Hook Hua United States 8 53 0.7× 81 1.3× 27 0.7× 82 2.6× 138 5.1× 32 307
Kazuya Kaku Japan 5 15 0.2× 135 2.1× 39 1.0× 76 2.5× 65 2.4× 7 341
Jan Kučera Italy 5 12 0.2× 120 1.9× 23 0.6× 64 2.1× 45 1.7× 13 271
Laércio Massaru Namikawa Brazil 8 91 1.3× 50 0.8× 33 0.9× 80 2.6× 7 0.3× 16 310
Akhmad Solikhin France 10 85 1.2× 31 0.5× 44 1.2× 66 2.1× 51 1.9× 19 261
Mokhamad Nur Cahyadi Indonesia 10 297 4.1× 15 0.2× 46 1.2× 20 0.6× 96 3.6× 76 481
Diego Polli Italy 10 61 0.8× 29 0.5× 43 1.1× 69 2.2× 57 2.1× 21 325
Amrey Krause United Kingdom 12 33 0.5× 21 0.3× 50 1.3× 20 0.6× 5 0.2× 38 367
Jens-Uwe Klügel Switzerland 9 233 3.2× 13 0.2× 48 1.3× 9 0.3× 23 0.9× 32 403

Countries citing papers authored by M. T. Glasscoe

Since Specialization
Citations

This map shows the geographic impact of M. T. Glasscoe'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 M. T. Glasscoe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. T. Glasscoe more than expected).

Fields of papers citing papers by M. T. Glasscoe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. T. Glasscoe. 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 M. T. Glasscoe. The network helps show where M. T. Glasscoe may publish in the future.

Co-authorship network of co-authors of M. T. Glasscoe

This figure shows the co-authorship network connecting the top 25 collaborators of M. T. Glasscoe. A scholar is included among the top collaborators of M. T. Glasscoe 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 M. T. Glasscoe. M. T. Glasscoe 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.
Chen, Zhiqiang, Jun Wang, Bandana Kar, et al.. (2025). Optical Remote Sensing for Global Flood Disaster Mapping: A Critical Review Towards Operational Readiness. Remote Sensing. 17(11). 1886–1886. 2 indexed citations
2.
Kar, Bandana, et al.. (2024). Assessing a Model-of-Models Approach for Global Flood Forecasting and Alerting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 9641–9650. 1 indexed citations
3.
Glasscoe, M. T., et al.. (2022). High‐Resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake Using 3D Finite Element Modeling. Journal of Geophysical Research Solid Earth. 127(9). 4 indexed citations
4.
Kruczkiewicz, Andrew, Fabio Cian, Irene Monasterolo, et al.. (2022). Multiform flood risk in a rapidly changing world: what we do not do, what we should and why it matters. Environmental Research Letters. 17(8). 81001–81001. 20 indexed citations
5.
Granat, Robert, Andrea Donnellan, M. B. Heflin, et al.. (2021). Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults. Earth and Space Science. 8(11). e2021EA001680–e2021EA001680. 17 indexed citations
6.
Donnellan, Andrea, Jay Parker, M. B. Heflin, et al.. (2021). Improving access to geodetic imaging crustal deformation data using GeoGateway. Earth Science Informatics. 15(3). 1513–1525. 4 indexed citations
7.
Kar, Bandana, Jun Wang, Guy Schumann, et al.. (2020). INTEGRATED MODEL OF MODELS FOR GLOBAL FLOOD ALERTING. WIT transactions on the built environment. 1. 73–83. 3 indexed citations
8.
Virapongse, Arika, et al.. (2020). Ten rules to increase the societal value of earth observations. Earth Science Informatics. 13(2). 233–247. 10 indexed citations
9.
Donnellan, Andrea, Marlon Pierce, Jun Wang, et al.. (2019). The Quakes Concept for Observing and Mitigating Natural Disasters. 5347–5350. 1 indexed citations
10.
Donnellan, Andrea, Jay Parker, Christopher Milliner, et al.. (2018). UAVSAR and Optical Analysis of the Thomas Fire Scar and Montecito Debris Flows: Case Study of Methods for Disaster Response Using Remote Sensing Products. Earth and Space Science. 5(7). 339–347. 10 indexed citations
11.
Donnellan, Andrea, Jay Parker, M. T. Glasscoe, et al.. (2016). GeoGateway: A system for analysis of UAVSAR data products. 75. 210–213. 2 indexed citations
12.
Glasscoe, M. T., et al.. (2014). Disaster Response and Decision Support in Partnership with the California Earthquake Clearinghouse. AGU Fall Meeting Abstracts. 2014. 1 indexed citations
13.
Donnellan, Andrea, M. T. Glasscoe, Jay Parker, et al.. (2013). Integrating remotely sensed and ground observations for modeling, analysis, and decision support. 5. 1–12. 2 indexed citations
14.
Wang, Jun, Marlon Pierce, Yu Ma, et al.. (2012). Using Service-Based GIS to Support Earthquake Research and Disaster Response. Computing in Science & Engineering. 14(5). 21–30. 23 indexed citations
15.
Glasscoe, M. T., Rolf Blom, G. W. Bawden, et al.. (2011). E-DECIDER: Earthquake Disaster Decision Support and Response Tools - Development and Experiences. AGUFM. 2011. 1 indexed citations
16.
Yoder, Mark R., et al.. (2010). A forest-fire model with natural fire resistance. AGU Fall Meeting Abstracts. 2010. 1 indexed citations
17.
Donnellan, Andrea, Jay Parker, M. T. Glasscoe, et al.. (2009). Understanding earthquake fault systems using QuakeSim analysis and data assimilation tools. 75. 1–9. 1 indexed citations
18.
Donnellan, Andrea, Jay Parker, Charles D. Norton, et al.. (2007). QuakeSim: Enabling Model Interactions in Solid Earth Science Sensor Webs. 120. 1–8. 3 indexed citations
19.
Turcotte, Donald L. & M. T. Glasscoe. (2004). A damage model for the continuum rheology of the upper continental crust. Tectonophysics. 383(1-2). 71–80. 28 indexed citations
20.
Glasscoe, M. T., Andrea Donnellan, L. H. Kellogg, & G. A. Lyzenga. (2004). Evidence of Strain Partitioning Between the Sierra Madre Fault and the Los Angeles Basin, Southern California from Numerical Models. Pure and Applied Geophysics. 161(11-12). 2343–2357. 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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