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Sudhanshu Pandey

Photo of Sudhanshu Pandey

Address:

4800 Oak Grove Drive

Pasadena, CA 91109

Curriculum Vitae:

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Member of:

Tropospheric Composition

Scientist

Biography

I am a Scientist at NASA’s Jet Propulsion Laboratory, where I serve as Methane Modeling Lead for the Atmospheric Application and Research Initiative (AARI). My research combines satellite remote sensing with atmospheric modeling to quantify greenhouse gas emissions from individual point sources to the global carbon cycle. I developed methods to estimate the growth rate of atmospheric CO₂ from satellite observations. I led the first satellite detection of an unreported extreme methane leak, helping catalyze remote-sensing-based methane plume detection. I am also developing AI tools for atmospheric and Earth science: atmospheric tracer transport emulators and LLM-based agentic systems to accelerate climate and atmospheric research.

Education

  • Ph.D., Physics, Utrecht University, 2017
  • BS-MS, Earth Sciences - Indian Institute of Science Education & Research, Kolkata, India, 2012

Professional Experience

  • Scientist — NASA Jet Propulsion Laboratory (2022 – Present)
  • Scientist — SRON Netherlands Institute for Space Research (2016 – 2022)

Community Service

  • Reviewer for scientific journals: Nature, Science Advances, Nature Climate Change, Atmospheric Measurement Techniques, Atmospheric Chemistry and Physics, Geophysical Research Letters and others.
  • Proposal Review: Scientific research proposals for NOAA and NASA.
  • Poster Judge: EGU and AGU annual meetings.
  • Mentoring students and interns at JPL and SRON Leiden, drawn from Caltech, USC, Cal Poly Pomona, Harvard, Delft, and Utrecht.

Research Interests

  • Remote sensing: Observing atmospheric gases with satellite-based instruments.
  • Plume detection: Detecting and quantifying emissions from individual sources using satellite observations.
  • Atmospheric transport modeling: Investigating trace-gas transport processes in the atmosphere.
  • Air quality and atmospheric dynamics: Using machine learning to find out what satellite observations of air pollutants tell us about winds and atmospheric mixing.
  • CO₂ growth-rate monitoring: Estimating whole-atmosphere CO₂ growth rates and diagnosing the global carbon budget using satellite and in situ observations.
  • Data assimilation: Integrating observations into models using Bayesian statistical methods.
  • Machine learning: Applying convolutional neural networks to identify and quantify emission sources.
  • AI agents for scientific discovery: Building LLM-based agentic systems that automate and accelerate climate and atmospheric research.

Selected Awards

  • JPL North Star Award, 2026
  • NASA Group Achievement Award, Greenhouse Gas Center Team, 2026
  • NASA ROSES Early Career Investigator Program in Earth Science (ECIP-ES) Grant, 2023
  • Best Oral Presentation Award, SRON Netherlands Science Day, 2019
  • INSPIRE Fellowship, Department of Science & Technology (DST), India, 2008

Selected Publications

(First-author papers and select high-impact multi-author papers. Reverse chronological. Full publication list in attached CV.)

  1. Friedlingstein, P., et al. (2026). Global Carbon Budget 2025. Earth System Science Data, 18, 3211–3288. https://doi.org/10.5194/essd-18-3211-2026
  2. Pandey, S. (2025). Taking Earth’s carbon pulse from space. AGU Advances. https://doi.org/10.1029/2025AV002085
  3. Pandey, S., et al. (2025). Reduction in Earth’s carbon budget imbalance. Nature Communications, 16, 6818. https://doi.org/10.1038/s41467-025-61588-2
  4. Pandey, S., et al. (2025). Relating multi-scale plume detection and area estimates of methane emissions: A theoretical and empirical analysis. Environmental Science & Technology, 59(16), 7931–7947. https://doi.org/10.1021/acs.est.4c07415
  5. Pandey, S., et al. (2024). Toward low-latency estimation of atmospheric CO₂ growth rates using satellite observations: Evaluating sampling errors of satellite and in situ observing approaches. AGU Advances, 5, e2023AV001145. https://doi.org/10.1029/2023AV001145
  6. Varon, D. J., et al. (2024). Quantifying NOₓ point sources with Landsat and Sentinel-2 satellite observations of NO₂ plumes. PNAS, 121, e2317077121. https://doi.org/10.1073/pnas.2317077121
  7. Byrne, B., et al. (2024). Carbon emissions from the 2023 Canadian wildfires. Nature, 633, 835–839. https://doi.org/10.1038/s41586-024-07878-z
  8. Pandey, S., et al. (2023). Daily detection and quantification of methane leaks using Sentinel-3: A tiered satellite observation approach with Sentinel-2 and Sentinel-5P. Remote Sensing of Environment, 296, 113716. https://doi.org/10.1016/j.rse.2023.113716
  9. Maasakkers, J. D., et al. (2022). Using satellites to uncover large methane emissions from landfills. Science Advances, 8. https://doi.org/10.1126/sciadv.abn9683
  10. Pandey, S., et al. (2022). Order-of-magnitude wall-time improvement of variational methane inversions by physical parallelization. Geoscientific Model Development, 15, 4555–4567. https://doi.org/10.5194/gmd-15-4555-2022
  11. Pandey, S., et al. (2021). Using satellite data to identify the methane emission controls of South Sudan’s wetlands. Biogeosciences, 18, 557–572. https://doi.org/10.5194/bg-18-557-2021
  12. Zhang, Y., et al. (2020). Quantifying methane emissions from the largest oil-producing basin in the United States from space. Science Advances. https://doi.org/10.1126/sciadv.aaz5120
  13. Pandey, S., et al. (2019). Satellite observations reveal extreme methane leakage from a natural gas well blowout. PNAS, 116(52), 26376–26381. https://doi.org/10.1073/pnas.1908712116
  14. Pandey, S., et al. (2019). Influence of atmospheric transport on estimates of variability in the global methane burden. Geophysical Research Letters, 46, 2302–2311. https://doi.org/10.1029/2018GL081092
  15. Worden, J. R., et al. (2017). Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget. Nature Communications, 8(1), 2227. https://doi.org/10.1038/s41467-017-02246-0
  16. Pandey, S., et al. (2017). Enhanced methane emissions from tropical wetlands during the 2011 La Niña. Scientific Reports, 7, 45759. https://doi.org/10.1038/srep45759
  17. Pandey, S., et al. (2016). Inverse modeling of GOSAT-retrieved ratios of total column CH₄ and CO₂ for 2009 and 2010. Atmospheric Chemistry and Physics, 16(8), 5043–5062. https://doi.org/10.5194/acp-16-5043-2016
  18. Pandey, S., et al. (2015). On the use of satellite-derived CH₄:CO₂ columns in a joint inversion of CH₄ and CO₂ fluxes. Atmospheric Chemistry and Physics, 15(15), 8615–8629. https://doi.org/10.5194/acp-15-8615-2015

Projects

OCO-2 - Orbiting Carbon Observatory
CMS Flux (Carbon Monitoring System Flux)
OCO-3 - Orbiting Carbon Observatory 3
TROPESS