Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
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....
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2022
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00059811 2024-09-15T18:00:50+00:00 Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals Meloche, Julien Langlois, Alexandre Rutter, Nick Royer, Alain King, Josh Walker, Branden Marsh, Philip Wilcox, Evan J. 2022-01 electronic https://doi.org/10.5194/tc-16-87-2022 https://noa.gwlb.de/receive/cop_mods_00059811 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059460/tc-16-87-2022.pdf https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-87-2022 https://noa.gwlb.de/receive/cop_mods_00059811 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059460/tc-16-87-2022.pdf https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/tc-16-87-2022 2024-06-26T04:34:57Z 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 (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 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 (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT 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. Article in Journal/Newspaper Cambridge Bay Subarctic The Cryosphere Tundra Niedersächsisches Online-Archiv NOA The Cryosphere 16 1 87 101 |
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article Verlagsveröffentlichung Meloche, Julien Langlois, Alexandre Rutter, Nick Royer, Alain King, Josh Walker, Branden Marsh, Philip Wilcox, Evan J. Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals |
topic_facet |
article Verlagsveröffentlichung |
description |
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 (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 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 (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT 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. |
format |
Article in Journal/Newspaper |
author |
Meloche, Julien Langlois, Alexandre Rutter, Nick Royer, Alain King, Josh Walker, Branden Marsh, Philip Wilcox, Evan J. |
author_facet |
Meloche, Julien Langlois, Alexandre Rutter, Nick Royer, Alain King, Josh Walker, Branden Marsh, Philip Wilcox, Evan J. |
author_sort |
Meloche, Julien |
title |
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_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_sort |
characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite swe retrievals |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-87-2022 https://noa.gwlb.de/receive/cop_mods_00059811 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059460/tc-16-87-2022.pdf https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf |
genre |
Cambridge Bay Subarctic The Cryosphere Tundra |
genre_facet |
Cambridge Bay Subarctic The Cryosphere Tundra |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-87-2022 https://noa.gwlb.de/receive/cop_mods_00059811 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059460/tc-16-87-2022.pdf https://tc.copernicus.org/articles/16/87/2022/tc-16-87-2022.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-16-87-2022 |
container_title |
The Cryosphere |
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16 |
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1 |
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87 |
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101 |
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1810438009909149696 |