Improved quantification of Arctic soil and permafrost properties.
We developed an approach to improve the estimation of ice-wedge dimension and other permafrost characteristics by integrating various geophysical imaging techniques including Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT).
Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-thaw dynamics require advances in quantifying soil and snow properties This work enables a better understanding and quantification of the morphology and physical properties of ice-wedges and permafrost present in Arctic tundra.
We document for the first time that GPR data collected during the frozen season, when conditions lead to improved GPR signal-to-noise ratio, can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.
Stan D. Wullschleger
Oak Ridge National Laboratory
Lawrence Berkeley National Laboratory
The Next-Generation Ecosystem Experiments (NGEE Arctic) project is supported by the Office of Biological and Environmental Research in the DOE Office of Science. This NGEE-Arctic research is supported through contract number DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory.
Léger, E., B. Dafflon, F. Soom, J. Peterson, C. Ulrich, and S. Hubbard. 2017. “Quantification of Arctic Soil and Permafrost Properties Using Ground-Penetrating Radar and Electrical Resistivity Tomography Datasets,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-12. DOI: 10.1109/JSTARS.2017.2694447
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