Preprints
https://doi.org/10.5194/tc-2023-95
https://doi.org/10.5194/tc-2023-95
03 Jul 2023
 | 03 Jul 2023
Status: this preprint is currently under review for the journal TC.

Snow Water Equivalent Retrieval Over Idaho, Part A: Using Sentinel-1 Repeat-Pass Interferometry

Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall

Abstract. Snow Water Equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and~passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the optimal radar frequencies and temporal repeat intervals have not been available until recently. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of Sentinel-1 data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent measurements of snow depth and SWE. The Pearson correlation and RSME between retrieved SWE change observations and in situ stations measurements are 0.82, and 0.76 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows SWE error less than 2 cm for the 16 in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5. Low temporal coherence is the main reason for degrading the performance of SWE retrieval using InSAR data. Low temporal coherence also causes the degradation of phase unwrapping algorithms.

Shadi Oveisgharan et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2023-95', Yang Lei, 07 Jul 2023
    • AC1: 'Reply on CC1', Shadi Oveisgharan, 07 Jul 2023
      • CC2: 'Reply on AC1', Yang Lei, 10 Jul 2023
        • AC3: 'Reply on CC2', Shadi Oveisgharan, 23 Sep 2023
  • RC1: 'Comment on tc-2023-95', Anonymous Referee #1, 11 Aug 2023
    • AC2: 'Reply on RC1', Shadi Oveisgharan, 23 Sep 2023
  • RC2: 'Comment on tc-2023-95', Jorge Jorge Ruiz, 25 Sep 2023

Shadi Oveisgharan et al.

Shadi Oveisgharan et al.

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Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Snow is the primary source of water for river channel discharge. Large scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing phase change to estimate the SWE change between two measurement. The error in estimated SWE change is less than 2 cm for in situ stations.