Preprints
https://doi.org/10.5194/tc-2021-156
https://doi.org/10.5194/tc-2021-156

  28 May 2021

28 May 2021

Review status: this preprint is currently under review for the journal TC.

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Julien Meloche1,2, Alexandre Langlois1,2, Nick Rutter3, Alain Royer1,2, Josh King4, and Branden Walker5 Julien Meloche et al.
  • 1Centre d’Applications et de Recherche en Télédétection, Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
  • 2Centre d’études Nordiques, Université Laval, Québec, G1V 0A6, Canada
  • 3Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
  • 4Environment and Climate Change Canada, Climate Research Division, Toronto, M3H 5T4, Canada
  • 5Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, N2L 3C5, Canada

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties, but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parametrized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a Remotely Piloted Aircraft System (RPAS). Distributions of snow specific area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than Cambridge Bay (CB) where TVC is at a lower latitude with a sub-arctic shrub tundra compared to CB which is a graminoid tundra. DHF were fitted with a gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. Snow depth simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE) grid 2.0 enhanced resolution at 37 GHz.

Julien Meloche et al.

Status: open (until 23 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-156', Matthew Sturm, 17 Jun 2021 reply

Julien Meloche et al.

Data sets

RPAS snow depth map Branden Walker, Evan Wilcox and Phil Marsh https://doi.org/10.5683/SP2/PWSKKG

Model code and software

Gaussian Process with SMRT simulation Julien Meloche https://github.com/JulienMeloche/Gaussian_process_smrt_simulation

Julien Meloche et al.

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Short summary
To estimate Snow Water Equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed to incorporate variability of snow in these model has it is not taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.