Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1485-2021
© Author(s) 2021. 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-15-1485-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Mar 2021
Research article |
| 24 Mar 2021
| 24 Mar 2021
Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States
Jennifer M. Jacobs
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of New
Hampshire, Durham, NH 03824, USA
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Adam G. Hunsaker
Department of Civil and Environmental Engineering, University of New
Hampshire, Durham, NH 03824, USA
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Franklin B. Sullivan
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Michael Palace
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Department of Earth Sciences, University of New Hampshire, Durham, NH
03824, USA
Elizabeth A. Burakowski
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Christina Herrick
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Eunsang Cho
Department of Civil and Environmental Engineering, University of New
Hampshire, Durham, NH 03824, USA
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
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This preprint is open for discussion and under review for The Cryosphere (TC).
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We developed a simple method to turn a few airborne snow-mapping flights and one daily snow record into continuous maps showing how snow depth changes each day. Tested in Idaho and New Hampshire, the approach works well in both deep and shallow snow regions and helps plan when and how often to fly lidar surveys for the best results.
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Images from Unoccupied aerial systems (UAS) allow us to map snow more accurately, especially where snow is shallow and does not last long. We used UAS with visible imagery and tested different methods of mapping snow (including a few machine learning methods) in open areas in southern New Hampshire, USA. We found that using the full color information gives the most reliable results. We showed how UAS surveys can be planned to create accurate snow maps, helping track snow in changing conditions.
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Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively map high-resolution snow depths. Our study found that UAS lidar outperformed SfM, particularly in capturing stable snow distribution patterns. Vegetation type was the primary factor influencing snow depth across forest and field areas, reflecting soil variables, such as organic matter. When analyzed separately, slope and forest canopy shadowing played key roles.
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This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
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While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
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Manuscript not accepted for further review
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This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
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Short summary
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We developed a simple method to turn a few airborne snow-mapping flights and one daily snow record into continuous maps showing how snow depth changes each day. Tested in Idaho and New Hampshire, the approach works well in both deep and shallow snow regions and helps plan when and how often to fly lidar surveys for the best results.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5557, https://doi.org/10.5194/egusphere-2025-5557, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study evaluated how different parameterization schemes in the Noah-MP land surface model affect snow and runoff processes in the Missouri River Basin. By comparing simulations with USGS streamflow and University of Arizona snow data across 50 subbasins, we found that alternative schemes improve groundwater, evapotranspiration, frozen soil, and snowmelt-runoff representation, supporting better flood prediction and water management.
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This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Images from Unoccupied aerial systems (UAS) allow us to map snow more accurately, especially where snow is shallow and does not last long. We used UAS with visible imagery and tested different methods of mapping snow (including a few machine learning methods) in open areas in southern New Hampshire, USA. We found that using the full color information gives the most reliable results. We showed how UAS surveys can be planned to create accurate snow maps, helping track snow in changing conditions.
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Short summary
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Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively map high-resolution snow depths. Our study found that UAS lidar outperformed SfM, particularly in capturing stable snow distribution patterns. Vegetation type was the primary factor influencing snow depth across forest and field areas, reflecting soil variables, such as organic matter. When analyzed separately, slope and forest canopy shadowing played key roles.
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Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere, 17, 3435–3442, https://doi.org/10.5194/tc-17-3435-2023, https://doi.org/10.5194/tc-17-3435-2023, 2023
Short summary
Short summary
This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
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Short summary
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to...
