A spatiotemporal analysis of errors in InSAR SWE measurements caused by non-snow phase changes

Abstract. Spatially distributed measurements of snow water equivalent (SWE) in mountainous terrain are not currently feasible from existing satellite platforms. The NISAR satellite has the potential to provide high resolution (80 m) SWE measurements on a 12-day orbit cycle over many of Earth's snowy regions, which would represent a new era of spaceborne snow monitoring. The most promising approach for NISAR SWE measurements uses interferometric synthetic aperture radar (InSAR) techniques to derive the 12-day change in SWE (ΔSWE) from the change in phase between two SAR acquisitions. However, many non-snow factors can also change in this 12-day period which subsequently modulate the SAR phase. These non-snow factors can vary differently in both space and time, and in turn introduce spatially and temporally variable errors into InSAR-derived ΔSWE measurements. Here we explore the effects of six non-snow factors that can affect InSAR phase: electron content of the ionosphere, atmospheric water vapor, atmospheric pressure, soil permittivity, vegetation permittivity, and surface deformation. We show how these factors affect phase-based SWE measurements at 13 SNOTEL stations across the western US, as well as regionally across North America. We consider errors resulting from a individual 12-day baselines, as well as the cumulative effects of these errors when a timeseries of ΔSWE measurements are integrated to derive peak seasonal SWE.

The ionospheric effect results in the largest cumulative error at all stations, with changes in the total electron content resulting in phase changes equivalent to 0.271–0.414 m of SWE, or more than 500 % larger than the median April 1 SWE at some shallow snow stations. When ionospheric effects are removed, the remaining cumulative error ranges from -0.074–0.022 m of SWE, equivalent to 0–89 % of April 1 SWE, with results affected primarily by differences in peak SWE rather than differences in absolute error values. Individual error components can show offsetting effects, where positive and negative biases partially cancel out to result in a lower total cumulative error. For a randomly selected 12-day baseline, exceedance probability analysis shows that there is a 50 % chance the ionospheric component introduces an error larger than 0.211 m into the overall ΔSWE measurement, while the remaining five components have a 50 % exceedance probability of 0.031 m. Accurate ΔSWE measurements using NISAR data will not be possible unless ionospheric effects can be appropriately addressed. Removal of other error sources requires careful consideration of the SWE monitoring application: for tracking total SWE accumulation in areas with deeper snowpacks, correcting some errors but not others may actually decrease accuracy by removing offsetting cumulative effects. For individual 12-day baselines, removing as many errors as possible will generally lead to improved accuracy.