Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites
<p>Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with ne...
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| Language: | English |
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Copernicus Publications
2024-11-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/18/5407/2024/tc-18-5407-2024.pdf |
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| author | Z. Hoppinen Z. Hoppinen R. T. Palomaki G. Brencher D. Dunmire D. Dunmire E. Gagliano A. Marziliano J. Tarricone J. Tarricone H.-P. Marshall |
| author_facet | Z. Hoppinen Z. Hoppinen R. T. Palomaki G. Brencher D. Dunmire D. Dunmire E. Gagliano A. Marziliano J. Tarricone J. Tarricone H.-P. Marshall |
| author_sort | Z. Hoppinen |
| collection | DOAJ |
| description | <p>Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient <span class="inline-formula"><i>r</i></span> of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi mathvariant="italic">σ</mi><mi mathvariant="normal">VH</mi></msub><mo>/</mo><msub><mi mathvariant="italic">σ</mi><mi mathvariant="normal">VV</mi></msub></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="e6818905d8dace62fa04bcbb48435b9f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-18-5407-2024-ie00001.svg" width="43pt" height="14pt" src="tc-18-5407-2024-ie00001.png"/></svg:svg></span></span>; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over <span class="inline-formula">∼</span> 1.5 m but that the cross ratio decreases for snow depths less than <span class="inline-formula">∼</span> 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.</p> |
| format | Article |
| id | doaj-art-8a3711e2fe5d4d7c97afdd0e666e4db0 |
| institution | Kabale University |
| issn | 1994-0416 1994-0424 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj-art-8a3711e2fe5d4d7c97afdd0e666e4db02024-11-25T08:12:16ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242024-11-01185407543010.5194/tc-18-5407-2024Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sitesZ. Hoppinen0Z. Hoppinen1R. T. Palomaki2G. Brencher3D. Dunmire4D. Dunmire5E. Gagliano6A. Marziliano7J. Tarricone8J. Tarricone9H.-P. Marshall10Boise State University, Department of Geosciences, 1910 University Drive, Boise, ID 83725, USACold Regions Research and Engineering Laboratory, Engineer Research and Development Center, United States Army, 72 Lyme Road, Hanover, NH 03755, USAInstitute of Arctic and Alpine Research, University of Colorado, 4001 Discovery Dr, Boulder, CO 80303, USACivil and Environmental Engineering Department, University of Washington, Seattle, WA 98195, USADepartment of Earth and Environmental Sciences, KU Leuven, Heverlee, BelgiumDepartment of Atmospheric and Oceanic Sciences, CU Boulder, 4001 Discovery Dr, Boulder, CO 80303, USACivil and Environmental Engineering Department, University of Washington, Seattle, WA 98195, USADepartment of Civil, Construction, and Environmental Engineering, University of New Mexico, Albuquerque, NM 87131, USAHydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20770, USANASA Postdoctoral Program, NASA Goddard Space Flight Center, Greenbelt, MD 20770, USABoise State University, Department of Geosciences, 1910 University Drive, Boise, ID 83725, USA<p>Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient <span class="inline-formula"><i>r</i></span> of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi mathvariant="italic">σ</mi><mi mathvariant="normal">VH</mi></msub><mo>/</mo><msub><mi mathvariant="italic">σ</mi><mi mathvariant="normal">VV</mi></msub></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="e6818905d8dace62fa04bcbb48435b9f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-18-5407-2024-ie00001.svg" width="43pt" height="14pt" src="tc-18-5407-2024-ie00001.png"/></svg:svg></span></span>; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over <span class="inline-formula">∼</span> 1.5 m but that the cross ratio decreases for snow depths less than <span class="inline-formula">∼</span> 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.</p>https://tc.copernicus.org/articles/18/5407/2024/tc-18-5407-2024.pdf |
| spellingShingle | Z. Hoppinen Z. Hoppinen R. T. Palomaki G. Brencher D. Dunmire D. Dunmire E. Gagliano A. Marziliano J. Tarricone J. Tarricone H.-P. Marshall Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites The Cryosphere |
| title | Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites |
| title_full | Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites |
| title_fullStr | Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites |
| title_full_unstemmed | Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites |
| title_short | Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites |
| title_sort | evaluating snow depth retrievals from sentinel 1 volume scattering over nasa snowex sites |
| url | https://tc.copernicus.org/articles/18/5407/2024/tc-18-5407-2024.pdf |
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