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Remote Sensing of Global Snow Water Equivalent: Monitoring Snow Water Equivalent from Space

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 6837

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Special Issue Editors

Prof. Dr. Ana P. Barros

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Guest Editor

Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA

Prof. Dr. Paul Houser

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Guest Editor

Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
Special Issues, Collections and Topics in MDPI journals

Dr. Edward Kim

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NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

Dr. Carrie Vuyovich

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NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

Dr. Dohyuk “DK” Kang

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Associate Guest Editor

Research Scientist
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
University of Maryland, College Park, MD 20742, USA

Dr. Rhae Sung Kim

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Associate Guest Editor

Associate Scientist
NASA Goddard Space Flight Center, Greenbelt MD 20771, USA
Universities Space Research Association, Columbia, MD 21046, USA

Dr. Melissa Wrzesien

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Associate Guest Editor

Associate Scientist
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Universities Space Research Association, Columbia, MD 21046, USA

Special Issue Information

Dear Colleagues,

This Special Issue invites manuscripts on all aspects of remote sensing of snow, focusing on measurements and models toward achieving quantitative estimation of snow water equivalent from local to global scales. We are looking for manuscripts describing novel approaches to operational monitoring of SWE, novel uses of currently available satellite observations, including development and assessment of retrieval algorithms, ground validation and uncertainty characterization of SWE products at multiple spatial resolutions, demonstrations of new technologies for operational snow remote sensing, novel computational frameworks, model development and applications including and coupled physical-radiative transfer models over heterogeneous domains, and data assimilation. End-to-end studies, including OSS (Observing System Simulator) trade studies, examine alternative remote-sensing measurement architectures and their impact on estimation errors and the integration of snow observations into Weather, Climate, and Earth System models are encouraged.

Prof. Dr. Ana P. Barros
Prof. Dr. Paul Houser
Dr. Edward Kim
Dr. Carrie Vuyovich
Guest Editors
Dr. Dohyuk “DK” Kang
Dr. Rhae Sung Kim
Dr. Melissa Wrzesien
Associate Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote Sensing
Snow Water Equivalent
Global Snow
Snow Depth
Climate Change
Water Resources
Snow Ecology
Microwave
Lidar
Integration
Forest Snow
Maritime Snow
Tundra Snow
Prairie Snow

Published Papers (3 papers)

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24 pages, 6072 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

by Randall Bonnell, Daniel McGrath, Keith Williams, Ryan Webb, Steven R. Fassnacht and Hans-Peter Marshall

Remote Sens. 2021, 13(21), 4223; https://doi.org/10.3390/rs13214223 - 21 Oct 2021

Cited by 8 | Viewed by 2454

Abstract

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE. Full article

(This article belongs to the Special Issue Remote Sensing of Global Snow Water Equivalent: Monitoring Snow Water Equivalent from Space)

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2 pages, 667 KiB

Open AccessCorrection

Correction: Webb et al. In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications. Remote Sens. 2021, 13, 4617

by Ryan W. Webb, Adrian Marziliano, Daniel McGrath, Randall Bonnell, Tate G. Meehan, Carrie Vuyovich and Hans-Peter Marshall

Remote Sens. 2022, 14(17), 4407; https://doi.org/10.3390/rs14174407 - 5 Sep 2022

Viewed by 991

Abstract

In the original article [...] Full article

(This article belongs to the Special Issue Remote Sensing of Global Snow Water Equivalent: Monitoring Snow Water Equivalent from Space)

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13 pages, 3062 KiB

Open AccessTechnical Note

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

by Ryan W. Webb, Adrian Marziliano, Daniel McGrath, Randall Bonnell, Tate G. Meehan, Carrie Vuyovich and Hans-Peter Marshall

Remote Sens. 2021, 13(22), 4617; https://doi.org/10.3390/rs13224617 - 16 Nov 2021

Cited by 12 | Viewed by 2525 | Correction

Abstract

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r² values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L. Full article

(This article belongs to the Special Issue Remote Sensing of Global Snow Water Equivalent: Monitoring Snow Water Equivalent from Space)

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