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Julia Marrs (Fed)

Julia Marrs is a National Research Council postdoctoral research associate in the Remote Sensing Group in Gaithersburg, MD. She is interested in hyperspectral sensor characterization and field instrumentation standardization for validating space-based measurements of biospheric greenhouse gas fluxes.

She is currently making field measurements of solar-induced chlorophyll fluorescence (SIF) and other narrow-band optical vegetation indices at the Forested Optical Reference for Evaluating Sensor Technology (FOREST) and Turf for the Urban Respiration Fraction (TURF) sites, to examine controls on the relationship between SIF and biogenic carbon fluxes. She is working to quantify stray light in VIS/NIR spectrometer systems using the laser line tuneable filter (LLTF) system, as well as other sources of uncertainty that can hinder accurate measurements the faint and dynamic SIF signal. This synthesis of field and laboratory measurements serves the goal of creating a best practices guide for SIF instrumentation and characterization protocols traceable to SI scales, and framework for relating field-based optical measurements to satellite SIF data.

She received her Ph.D. in Geography from Boston University, where she used tower-based SIF sensors to study leaf-level partitioning of absorbed light by plants across spatial and temporal scales.

Selected publications

Marrs, J.K., Jones, T.S., Allen, D.W., Hutyra, L.R., 2021. Instrumentation sensitivities for tower-based solar-induced fluorescence measurements. Remote Sensing of Environment 259, 112413. https://doi.org/10.1016/j.rse.2021.112413

Marrs, J.K., Reblin, J.S., Logan, B.A., Allen, D.W., Reinmann, A.B., Bombard, D.M., Tabachnik, D., Hutyra, L.R., 2020. Solar-induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophysical Research Letters 47, 1–11. https://doi.org/10.1029/2020GL087956

Marrs, J.K., Hutyra, L.R., Allen, D.W., 2019. Solar-induced fluorescence retrievals in the context of physiological, environmental, and hardware-based sources of uncertainty, in: Proceedings of SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV. pp. 1–14. https://doi.org/10.1117/12.2520457

Marrs, J., Ni-Meister, W., 2019. Machine learning techniques for tree species classification using co-registered LiDAR and hyperspectral data. Remote Sensing 11, 1–18. https://doi.org/10.3390/rs11070819

Smith, I.A., Hutyra, L.R., Reinmann, A.B., Marrs, J.K., Thompson, J.R., 2018. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Frontiers in Ecology and Environment 16, 213–221. https://doi.org/10.1002/fee.1793

Created March 5, 2019, Updated May 9, 2022