Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA
Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE...
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ftdoajarticles:oai:doaj.org/article:cd7151a43dbc4dbeb26b1be28e1253ef 2024-09-09T20:11:39+00:00 Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA T. G. Meehan A. Hojatimalekshah H.-P. Marshall E. J. Deeb S. O'Neel D. McGrath R. W. Webb R. Bonnell M. S. Raleigh C. Hiemstra K. Elder 2024-07-01T00:00:00Z https://doi.org/10.5194/tc-18-3253-2024 https://doaj.org/article/cd7151a43dbc4dbeb26b1be28e1253ef EN eng Copernicus Publications https://tc.copernicus.org/articles/18/3253/2024/tc-18-3253-2024.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-18-3253-2024 1994-0416 1994-0424 https://doaj.org/article/cd7151a43dbc4dbeb26b1be28e1253ef The Cryosphere, Vol 18, Pp 3253-3276 (2024) Environmental sciences GE1-350 Geology QE1-996.5 article 2024 ftdoajarticles https://doi.org/10.5194/tc-18-3253-2024 2024-08-05T17:48:53Z Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m −3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km 2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval. Article in Journal/Newspaper The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 18 7 3253 3276 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 T. G. Meehan A. Hojatimalekshah H.-P. Marshall E. J. Deeb S. O'Neel D. McGrath R. W. Webb R. Bonnell M. S. Raleigh C. Hiemstra K. Elder Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m −3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km 2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval. |
format |
Article in Journal/Newspaper |
author |
T. G. Meehan A. Hojatimalekshah H.-P. Marshall E. J. Deeb S. O'Neel D. McGrath R. W. Webb R. Bonnell M. S. Raleigh C. Hiemstra K. Elder |
author_facet |
T. G. Meehan A. Hojatimalekshah H.-P. Marshall E. J. Deeb S. O'Neel D. McGrath R. W. Webb R. Bonnell M. S. Raleigh C. Hiemstra K. Elder |
author_sort |
T. G. Meehan |
title |
Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA |
title_short |
Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA |
title_full |
Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA |
title_fullStr |
Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA |
title_full_unstemmed |
Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA |
title_sort |
spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at grand mesa, colorado, usa |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/tc-18-3253-2024 https://doaj.org/article/cd7151a43dbc4dbeb26b1be28e1253ef |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 18, Pp 3253-3276 (2024) |
op_relation |
https://tc.copernicus.org/articles/18/3253/2024/tc-18-3253-2024.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-18-3253-2024 1994-0416 1994-0424 https://doaj.org/article/cd7151a43dbc4dbeb26b1be28e1253ef |
op_doi |
https://doi.org/10.5194/tc-18-3253-2024 |
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The Cryosphere |
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18 |
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7 |
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3253 |
op_container_end_page |
3276 |
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