Preprints
https://doi.org/10.5194/tc-2022-224
https://doi.org/10.5194/tc-2022-224
 
16 Nov 2022
16 Nov 2022
Status: this preprint is currently under review for the journal TC.

Estimating snow accumulation and ablation with L-band InSAR

Jack Tarricone1, Ryan W. Webb3, Hans-Peter Marshall4, Anne W. Nolin2,1, and Franz J. Meyer5 Jack Tarricone et al.
  • 1Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, NV, USA
  • 2Department of Geography, University of Nevada, Reno, Reno, NV, USA
  • 3Department of Civil & Environmental Engineering & Construction Management, University of Wyoming, Laramie, WY, USA
  • 4Department of Geosciences, Boise State University, Boise, ID, USA
  • 5Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA

Abstract. Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. To confront these challenges, NASA initiated the SnowEx program, a multi-year effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the UAVSAR team acquired an L-band Interferometric Synthetic Aperture Radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR data for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide snow mass change estimates, allowing the snow community to leverage the upcoming NASA-ISRO SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez River Basin, NM, between 12–26 February 2020. We developed an end-to-end UAVSAR InSAR processing workflow for snow applications. This open-source approach employs a novel data fusion method that merges optical snow covered area (SCA) information with InSAR data. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow covered pixels. For all InSAR pairs, we converted phase change values to SWE change estimates between the three data acquisition dates. We then evaluated InSAR-derived retrievals using a combination of optical snow cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

Jack Tarricone et al.

Status: open (until 11 Jan 2023)

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Jack Tarricone et al.

Model code and software

Estimating snow accumulation and ablation with L-band InSAR: R and Python code for analysis and figure creation Jack Tarricone https://doi.org/10.5281/zenodo.7199791

Jack Tarricone et al.

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Short summary
Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently monitor how much water is in mountain snowpack from satellites. In this research, we test the ability of an experimental remote sensing technique to monitor snow from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found the method worked better than expected in estimating important snowpack properties.