Disciplines: 

lithosphere

Mentor (JPL): 

Simon Hook (321H), simon.j.hook@jpl.nasa.gov

Mentor (UCLA): 

Gilles Peltzer (EPSS), peltzer@epss.ucla.edu

Project Description:

This project explores the use of multispectral and hyperspectral thermal infrared data to detect and monitor critical energy resources, including mineral deposits and geothermal anomalies. The intern will work with data from HyTES (Hyperspectral Thermal Emission Spectrometer) and ECOSTRESS to map spectral signatures of silicate minerals and geothermal anomalies . The work sits at the intersection of geological remote sensing and applied Earth observation, with relevance to energy resource monitoring and the environmental assessment of extraction sites. The intern will gain hands-on experience in thermal data processing, spectral unmixing, and geospatial analysis.

Preferred Background:

Geology, Earth systems science, computer science, data science

References: 

Rabuffi, F., Hulley, G., Hook, S. J., Cawse-Nicholson, K., Ramsey, M. S., Thompson, J. O., ... & La, T. T. (2025). Surface Mineralogy using Thermal InfraRed Spectroscopy Data from ECOSTRESS and ASTER. IEEE Geoscience and Remote Sensing Letters.

Student Requirements: 

Minimum cumulative 3.00 GPA

Work Location: 

JPL/UCLA

Disciplines: 

biosphere

Mentor (JPL): 

Junjie Liu (321/biosphere group), junjie.liu@jpl.nasa.gov

Mentor (UCLA): 

Yue Li, yli@geog.ucla.edu

Project Description:

This project will quantify fire-related carbon emissions constrained by CO observations spanning more than two decades and investigate the drivers of emission variability across major biomes.

Preferred Background:

Earth systems science, ecology, wildfires, data science

References: 

Byrne, B., Liu, J., Bowman, K. W., Pascolini-Campbell, M., Chatterjee, A., Pandey, S., ... & Sinha, S. (2024). Carbon emissions from the 2023 Canadian wildfires. Nature, 633(8031), 835-839.  and Virkkala, A. M., Rogers, B. M., Watts, J. D., Arndt, K. A., Potter, S., Wargowsky, I., ... & Natali, S. M. (2025). Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake. Nature Climate Change, 15(2), 188-195.

Student Requirements: 

Minimum cumulative 3.00 GPA

Work Location: 

JPL/UCLA

Disciplines: 

hydrosphere, lithosphere, cryosphere, biosphere

Mentor (JPL): 

Anthony Bloom (321E), alexis.a.bloom@jpl.nasa.gov

Mentor (UCLA): 

Eren Bilir, tebilir@ucla.edu 

Project Description:

Update geological heating routines in CARDAMOM for more accurate high latitude energy balance. This would involve implementing spatially varying geological heating constraints from existing datasets, but could have room for model process development in a range of related modules (heat transfer physics, cryosphere representation, surface energy balance) if there's interest.

If the applicant would also like to explore other land biosphere model-data integration directions using CARDAMOM—including model process development, dynamical system analysis, advances in CARDAMOM's Bayesian inference algorithm, or technical advances in the code infrastructure among other possible directions—proposals for alternative directions are welcome.

Preferred Background:

Data assimilation, computer science, terrestrial-atmosphere-ocean interactions, Earth systems science, math, statistics 

References: 

Worden, M. A., Bilir, T. E., Bloom, A. A., Fang, J., Klinek, L. P., Konings, A. G., ... & Zhu, S. (2025). Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling. Global Change Biology, 31(8), e70462.

Student Requirements: 

Minimum cumulative 3.00 GPA

Work Location: 

JPL/UCLA

Disciplines: 

atmosphere

Mentor (JPL): 

Madeleine Pascolini-Campbell (Water & Eco), madeleine.a.pascolini-campbell@jpl.nasa.gov

Mentor (UCLA): 

Janine Baijnath-Rodino, janinebr@g.ucla.edu

Project Description:

This project leverages multiple remote sensing data (thermal, hyperspectral, SAR, gravity-based measurements) and machine learning techniques to produce seasonal to sub-seasonal predictions of fire severity to inform fire management. We will generate predictions at different timescales ranging from weekly to seasonal. We will explore the best set of predictors at different timescales. Results will be used to inform pre-fire season fuels management and mitigation activities.

Preferred Background:

Earth systems science, ecology, atmosphere, wildfires, data science 

References: 

Li, S., Baijnath-Rodino, J.A., York, R.A. et al. Temporal and spatial pattern analysis of escaped prescribed fires in California from 1991 to 2020. fire ecol 21, 3 (2025). https://doi.org/10.1186/s42408-024-00342-3 and Pascolini-Campbell, M., Fisher, J. B., Cawse-Nicholson, K., Lee, C. M., & Stavros, N. (2025). Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models. Scientific Reports, 15(1), 21504.

Student Requirements: 

Minimum cumulative 3.00 GPA

Work Location: 

JPL/UCLA

Disciplines: 

Mentor (JPL): 

Qing Yue (329E), qyue@jpl.nasa.gov

Mentor (UCLA): 

Gang Chen, gchenpu@ucla.edu

Project Description:

Preferred Background:

Earth systems science, atmosphere, data science 

References: 

Student Requirements: 

The student should have some basic knowledge of atmospheric sciences.  Proficiency in Jupyter Notebook (Python), MATLAB, or other programming language is required.

Work Location: 

UCLA