Josh Gray

4.9k total citations · 4 hit papers
41 papers, 3.6k citations indexed

About

Josh Gray is a scholar working on Ecology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Josh Gray has authored 41 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Ecology, 26 papers in Global and Planetary Change and 13 papers in Environmental Engineering. Recurrent topics in Josh Gray's work include Remote Sensing in Agriculture (31 papers), Land Use and Ecosystem Services (17 papers) and Species Distribution and Climate Change (12 papers). Josh Gray is often cited by papers focused on Remote Sensing in Agriculture (31 papers), Land Use and Ecosystem Services (17 papers) and Species Distribution and Climate Change (12 papers). Josh Gray collaborates with scholars based in United States, Australia and Canada. Josh Gray's co-authors include M. A. Friedl, Andrew D. Richardson, E. K. Melaas, Damien Sulla‐Menashe, S. P. Abercrombie, Trevor F. Keenan, John O’Keefe, Steve Frolking, Minkyu Moon and Koen Hufkens and has published in prestigious journals such as Nature, Journal of Clinical Oncology and PLoS ONE.

In The Last Decade

Josh Gray

40 papers receiving 3.5k citations

Hit Papers

Net carbon uptake has increased through warming-induced c... 2014 2026 2018 2022 2014 2019 2018 2020 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Josh Gray United States 22 2.5k 2.4k 1.1k 977 695 41 3.6k
Alemu Gonsamo Canada 44 3.0k 1.2× 3.3k 1.4× 1.3k 1.2× 887 0.9× 1.0k 1.5× 99 4.8k
Dirk Pflugmacher Germany 37 2.7k 1.1× 2.4k 1.0× 1.6k 1.5× 546 0.6× 573 0.8× 56 4.2k
Rogier de Jong Switzerland 26 2.0k 0.8× 2.4k 1.0× 747 0.7× 571 0.6× 836 1.2× 43 3.4k
Koen Hufkens United States 32 2.9k 1.2× 2.7k 1.1× 1.2k 1.1× 1.5k 1.5× 763 1.1× 67 4.6k
Kamel Soudani France 25 2.3k 0.9× 1.8k 0.7× 952 0.9× 562 0.6× 497 0.7× 50 3.0k
E. K. Melaas United States 21 2.0k 0.8× 1.5k 0.6× 932 0.9× 1.0k 1.1× 328 0.5× 29 2.6k
Wenquan Zhu China 29 1.8k 0.7× 1.9k 0.8× 534 0.5× 433 0.4× 603 0.9× 122 2.8k
J.C.F. Hodges United States 8 3.2k 1.3× 2.9k 1.2× 1.5k 1.4× 832 0.9× 1.2k 1.7× 13 4.5k
Matthew L. Clark United States 29 2.1k 0.9× 1.8k 0.7× 1.2k 1.1× 649 0.7× 330 0.5× 53 3.8k
Nikolay V. Shabanov United States 30 3.4k 1.3× 3.0k 1.2× 1.8k 1.7× 596 0.6× 1.0k 1.5× 47 4.6k

Countries citing papers authored by Josh Gray

Since Specialization
Citations

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

Fields of papers citing papers by Josh Gray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Josh Gray. 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 Josh Gray. The network helps show where Josh Gray may publish in the future.

Co-authorship network of co-authors of Josh Gray

This figure shows the co-authorship network connecting the top 25 collaborators of Josh Gray. A scholar is included among the top collaborators of Josh Gray 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 Josh Gray. Josh Gray 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.
McGregor, Ian R., Grant M. Connette, & Josh Gray. (2024). A multi-source change detection algorithm supporting user customization and near real-time deforestation detections. Remote Sensing of Environment. 308. 114195–114195. 7 indexed citations
2.
Thorp, Kelly R., Mirela G. Tulbure, Josh Gray, et al.. (2024). Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. PLoS ONE. 19(12). e0309982–e0309982. 1 indexed citations
3.
Solomon, Divya, Asif Ishtiaque, Josh Gray, et al.. (2024). The role of rural circular migration in shaping weather risk management for smallholder farmers in India, Nepal, and Bangladesh. Global Environmental Change. 89. 102937–102937. 4 indexed citations
4.
Richardson, Andrew D., et al.. (2024). Thermal Forcing Versus Chilling? Misspecification of Temperature Controls in Spring Phenology Models. Global Ecology and Biogeography. 33(12). 6 indexed citations
5.
Ardón, Marcelo, et al.. (2024). Detecting Trajectories of Regime Shifts and Loss of Resilience in Coastal Wetlands using Remote Sensing. Ecosystems. 27(8). 1060–1075. 1 indexed citations
7.
Gray, Josh, et al.. (2023). Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes. Environmental Modelling & Software. 167. 105758–105758. 8 indexed citations
9.
Wang, Haoyu, David F. Fouhey, Weiqi Zhou, et al.. (2022). Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries. Remote Sensing. 14(13). 3046–3046. 20 indexed citations
10.
Gray, Josh, et al.. (2022). Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data. Journal of Data Science. 545–565. 1 indexed citations
11.
Moorman, Christopher E., et al.. (2020). Predictors of fire-tolerant oak and fire-sensitive hardwood distribution in a fire-maintained longleaf pine ecosystem. Forest Ecology and Management. 477. 118468–118468. 2 indexed citations
12.
Singh, Kunwar K. & Josh Gray. (2020). Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery. Photogrammetric Engineering & Remote Sensing. 86(8). 509–518. 13 indexed citations
13.
Bolton, Douglas K., Josh Gray, E. K. Melaas, et al.. (2020). Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment. 240. 111685–111685. 305 indexed citations breakdown →
14.
Richardson, Andrew D., Koen Hufkens, Tom Milliman, et al.. (2018). Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data. 5(1). 180028–180028. 356 indexed citations breakdown →
15.
Singh, Kunwar K., Marguerite Madden, Josh Gray, & Ross K. Meentemeyer. (2018). The managed clearing: An overlooked land-cover type in urbanizing regions?. PLoS ONE. 13(2). e0192822–e0192822. 7 indexed citations
16.
Pickard, Brian, Josh Gray, & Ross K. Meentemeyer. (2017). Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models. Land. 6(3). 52–52. 38 indexed citations
17.
Senthilnath, J., et al.. (2016). Validation of VIIRS Land Surface Phenology using Field Observations, PhenoCam Imagery, and Landsat data. AGU Fall Meeting Abstracts. 2016. 1 indexed citations
18.
Melaas, E. K., Damien Sulla‐Menashe, Josh Gray, & M. A. Friedl. (2015). Using three decades of Landsat data to characterize changes and vulnerability of temperate and boreal forest phenology to climate change. AGU Fall Meeting Abstracts. 2015. 1 indexed citations
19.
Klosterman, Stephen, Koen Hufkens, Josh Gray, et al.. (2014). Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences. 11(16). 4305–4320. 291 indexed citations
20.
Gray, Josh, Steve Frolking, E. A. Kort, et al.. (2014). Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature. 515(7527). 398–401. 105 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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