Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-87-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-87-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jan 2022
Research article |
| 06 Jan 2022
| 06 Jan 2022
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
Julien Meloche
CORRESPONDING AUTHOR
Centre d'Applications et de Recherche en Télédétection,
Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Centre d'études Nordiques, Université Laval, Québec, G1V
0A6, Canada
Alexandre Langlois
Centre d'Applications et de Recherche en Télédétection,
Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Centre d'études Nordiques, Université Laval, Québec, G1V
0A6, Canada
Nick Rutter
Department of Geography and Environmental Sciences, Northumbria
University, Newcastle upon Tyne, NE1 8ST, UK
Alain Royer
Centre d'Applications et de Recherche en Télédétection,
Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Centre d'études Nordiques, Université Laval, Québec, G1V
0A6, Canada
Josh King
Environment and Climate Change Canada, Climate Research Division,
Toronto, M3H 5T4, Canada
Branden Walker
Cold Regions Research Centre, Wilfrid Laurier University, Waterloo,
N2L 3C5, Canada
Philip Marsh
Cold Regions Research Centre, Wilfrid Laurier University, Waterloo,
N2L 3C5, Canada
Evan J. Wilcox
Cold Regions Research Centre, Wilfrid Laurier University, Waterloo,
N2L 3C5, Canada
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Cited
11 citations as recorded by crossref.
- Multi-physics ensemble modelling of Arctic tundra snowpack properties G. Woolley et al. 10.5194/tc-18-5685-2024
- Improvement of Polar Snow Microwave Brightness Temperature Simulations for Dense Wind Slab and Large Grain J. Meloche et al. 10.1109/TGRS.2024.3428394
- Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation J. Voglimacci-Stephanopoli et al. 10.5194/tc-16-2163-2022
- Characterization of non-Gaussianity in the snow distributions of various landscapes N. Ohara et al. 10.5194/tc-18-5139-2024
- Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data A. Jia et al. 10.1016/j.isprsjprs.2023.01.004
- High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut J. Meloche et al. 10.1002/hyp.14546
- Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow K. Wivell et al. 10.5194/tc-17-4325-2023
- Local variability of a taiga snow cover due to vegetation and microtopography A. Komarov & M. Sturm 10.1080/15230430.2023.2170086
- Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions A. Mavrovic et al. 10.5194/bg-20-2941-2023
- Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels M. Sandells et al. 10.5194/tc-18-3971-2024
- Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE) A. Royer et al. 10.5194/tc-15-5079-2021
10 citations as recorded by crossref.
- Multi-physics ensemble modelling of Arctic tundra snowpack properties G. Woolley et al. 10.5194/tc-18-5685-2024
- Improvement of Polar Snow Microwave Brightness Temperature Simulations for Dense Wind Slab and Large Grain J. Meloche et al. 10.1109/TGRS.2024.3428394
- Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation J. Voglimacci-Stephanopoli et al. 10.5194/tc-16-2163-2022
- Characterization of non-Gaussianity in the snow distributions of various landscapes N. Ohara et al. 10.5194/tc-18-5139-2024
- Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data A. Jia et al. 10.1016/j.isprsjprs.2023.01.004
- High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut J. Meloche et al. 10.1002/hyp.14546
- Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow K. Wivell et al. 10.5194/tc-17-4325-2023
- Local variability of a taiga snow cover due to vegetation and microtopography A. Komarov & M. Sturm 10.1080/15230430.2023.2170086
- Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions A. Mavrovic et al. 10.5194/bg-20-2941-2023
- Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels M. Sandells et al. 10.5194/tc-18-3971-2024
Latest update: 13 Dec 2024
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 incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
To estimate snow water equivalent from space, model predictions of the satellite measurement...
