Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
<p>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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-01-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf |
| _version_ | 1798035190376824832 |
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| author | J. Meloche J. Meloche A. Langlois A. Langlois N. Rutter A. Royer A. Royer J. King B. Walker P. Marsh E. J. Wilcox |
| author_facet | J. Meloche J. Meloche A. Langlois A. Langlois N. Rutter A. Royer A. Royer J. King B. Walker P. Marsh E. J. Wilcox |
| author_sort | J. Meloche |
| collection | DOAJ |
| description | <p>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 parameterized by a log-normal distribution with mean (<span class="inline-formula"><i>μ</i><sub>sd</sub></span>)
values and coefficients of variation (CV<span class="inline-formula"><sub>sd</sub></span>). Snow depth variability
(CV<span class="inline-formula"><sub>sd</sub></span>) was found to increase as a function of the area measured by a
remotely piloted aircraft system (RPAS). Distributions of snow specific
surface 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 in Cambridge Bay (CB), where TVC is at a
lower latitude with a subarctic shrub tundra compared to CB, which is a
graminoid tundra. DHFs 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 (CV<span class="inline-formula"><sub>sd</sub></span>) is proposed
as an effective parameter to account for sub-pixel variability in PMW
emission, improving simulation by 8 K. SMRT simulations using a CV<span class="inline-formula"><sub>sd</sub></span> of
0.9 best matched CV<span class="inline-formula"><sub>sd</sub></span> observations from spatial datasets for areas <span class="inline-formula"><i>></i></span> 3 km<span class="inline-formula"><sup>2</sup></span>, 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.</p> |
| first_indexed | 2024-04-11T20:54:48Z |
| format | Article |
| id | doaj.art-7a77930e49e441108763a89525d070f7 |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-04-11T20:54:48Z |
| publishDate | 2022-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-7a77930e49e441108763a89525d070f72022-12-22T04:03:43ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242022-01-01168710110.5194/tc-16-87-2022Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievalsJ. Meloche0J. Meloche1A. Langlois2A. Langlois3N. Rutter4A. Royer5A. Royer6J. King7B. Walker8P. Marsh9E. J. Wilcox10Centre d'Applications et de Recherche en Télédétection, Université de Sherbrooke, Sherbrooke, J1K 2R1, CanadaCentre d'études Nordiques, Université Laval, Québec, G1V 0A6, CanadaCentre d'Applications et de Recherche en Télédétection, Université de Sherbrooke, Sherbrooke, J1K 2R1, CanadaCentre d'études Nordiques, Université Laval, Québec, G1V 0A6, CanadaDepartment of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UKCentre d'Applications et de Recherche en Télédétection, Université de Sherbrooke, Sherbrooke, J1K 2R1, CanadaCentre d'études Nordiques, Université Laval, Québec, G1V 0A6, CanadaEnvironment and Climate Change Canada, Climate Research Division, Toronto, M3H 5T4, CanadaCold Regions Research Centre, Wilfrid Laurier University, Waterloo, N2L 3C5, CanadaCold Regions Research Centre, Wilfrid Laurier University, Waterloo, N2L 3C5, CanadaCold Regions Research Centre, Wilfrid Laurier University, Waterloo, N2L 3C5, Canada<p>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 parameterized by a log-normal distribution with mean (<span class="inline-formula"><i>μ</i><sub>sd</sub></span>) values and coefficients of variation (CV<span class="inline-formula"><sub>sd</sub></span>). Snow depth variability (CV<span class="inline-formula"><sub>sd</sub></span>) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface 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 in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs 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 (CV<span class="inline-formula"><sub>sd</sub></span>) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CV<span class="inline-formula"><sub>sd</sub></span> of 0.9 best matched CV<span class="inline-formula"><sub>sd</sub></span> observations from spatial datasets for areas <span class="inline-formula"><i>></i></span> 3 km<span class="inline-formula"><sup>2</sup></span>, 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.</p>https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf |
| spellingShingle | J. Meloche J. Meloche A. Langlois A. Langlois N. Rutter A. Royer A. Royer J. King B. Walker P. Marsh E. J. Wilcox Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals The Cryosphere |
| title | Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals |
| title_full | Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals |
| title_fullStr | Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals |
| title_full_unstemmed | Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals |
| title_short | Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals |
| title_sort | characterizing tundra snow sub pixel variability to improve brightness temperature estimation in satellite swe retrievals |
| url | https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf |
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