13 Sep 2023
 | 13 Sep 2023
Status: this preprint is open for discussion.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic-aperture-radar demonstration using airborne SnowSAR in SnowEx17

Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros

Abstract. A physical-statistical framework to estimate Snow Water Equivalent (SWE) and snow depth from SAR measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx’2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce noise to signal ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent single- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals, defined by absolute convergence backscatter errors ≤ 1.2 dB and local SnowSAR incidence angles between 30° and 45° for X- and Ku-band VV-pol backscatter measurements, were achieved for 75 % to 87 % for all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations showing strong skill in deep snow with average absolute SWE residuals of 5–7 % (15–18 %) for the two-layer (single-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis LIDAR estimates have Bhattacharya Coefficients above 94 % (90 %) for grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

Siddharth Singh et al.

Status: open (until 25 Oct 2023)

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  • AC1: 'Comment on egusphere-2023-1987', Siddharth Singh, 28 Sep 2023 reply

Siddharth Singh et al.

Siddharth Singh et al.


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Short summary
Seasonal snowfall accumulation (snowpack) plays a critical role in climate. The water stored in it is measured by the Snow Water Equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical-statistical framework to estimate SWE from airborne X/Ku-band SAR backscatter measurements constrained by physically based snow hydrology and radar models. We explored different spatial resolutions and vertical structures that agree well with ground observation.