2014Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Determining snow depth using Ku-band interferometric synthetic aperture radar (InSAR)

Jack R. Evans, Fred A. Kruse, D. L. Bickel, Ralf Dunkel

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Abstract

Monitoring seasonal snow accumulation is important for evaluation of snow models, for short- and long-term snow cover monitoring, and for both military and civilian activities in cold climates. Improved spatial analysis of snow depth and volume can help decision makers plan for future events and mitigate risk. Current snow depth measurement methods fall short of operational requirements. This research explored a new approach for determining snow depth using Ku-band multi-pass (monostatic) airborne interferometric synthetic aperture radar (InSAR). A perturbation method that isolated and compared high frequency terrain phase to elevation was used to generate Snow-Off and Snow-On DEMs from the InSAR phase data. Differencing the InSAR DEMs determined elevation change caused by accumulated snow. Comparison of InSAR snow depths to manual snow depth measurements indicated average InSAR snow depth errors of -8cm, 95cm, -49cm, 176cm, 87cm, and 42cm for six SAR pairs. The source of these errors appears to be mostly related to uncorrected slope and tilt in fitted low frequency planes. Results show that this technique has promise but accuracy could be substantially improved by the use of bistatic SAR systems, which would allow for more stable and measurable interferometric baselines.

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Monitoring seasonal snow accumulation is important for evaluation of snow models, for short- and long-term snow cover monitoring, and for both military and civilian activities in cold climates. Improved spatial analysis of snow depth and volume can help decision makers plan for future events and mitigate risk. Current snow depth measurement methods fall short of operational requirements. This research explored a new approach for determining snow depth using Ku-band multi-pass (monostatic) airborne interferometric synthetic aperture radar (InSAR). A perturbation method that isolated and compared high frequency terrain phase to elevation was used to generate Snow-Off and Snow-On DEMs from the InSAR phase data. Differencing the InSAR DEMs determined elevation change caused by accumulated snow. Comparison of InSAR snow depths to manual snow depth measurements indicated average InSAR snow depth errors of -8cm, 95cm, -49cm, 176cm, 87cm, and 42cm for six SAR pairs. The source of these errors appears to be mostly related to uncorrected slope and tilt in fitted low frequency planes. Results show that this technique has promise but accuracy could be substantially improved by the use of bistatic SAR systems, which would allow for more stable and measurable interferometric baselines.

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Available abstract

Monitoring seasonal snow accumulation is important for evaluation of snow models, for short- and long-term snow cover monitoring, and for both military and civilian activities in cold climates. Improved spatial analysis of snow depth and volume can help decision makers plan for future events and mitigate risk. Current snow depth measurement methods fall short of operational requirements. This research explored a new approach for determining snow depth using Ku-band multi-pass (monostatic) airborne interferometric synthetic aperture radar (InSAR). A perturbation method that isolated and compared high frequency terrain phase to elevation was used to generate Snow-Off and Snow-On DEMs from the InSAR phase data. Differencing the InSAR DEMs determined elevation change caused by accumulated snow. Comparison of InSAR snow depths to manual snow depth measurements indicated average InSAR snow depth errors of -8cm, 95cm, -49cm, 176cm, 87cm, and 42cm for six SAR pairs. The source of these errors appears to be mostly related to uncorrected slope and tilt in fitted low frequency planes. Results show that this technique has promise but accuracy could be substantially improved by the use of bistatic SAR systems, which would allow for more stable and measurable interferometric baselines.

Key concepts: Interferometric synthetic aperture radar, Snow, Remote sensing, Elevation (ballistics), Synthetic aperture radar, Digital elevation model, Interferometry, Geology

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