Snow Water Equivalent Retrieval Over Idaho, Part B: Using L-band UAVSAR Repeat-Pass Interferometry. Hoppinen, Z. M., Oveisgharan, S., Marshall, H., Mower, R., Elder, K., & Vuyovich, C. The Cryosphere Discussions, August, 2023. Publisher: Copernicus GmbH![Snow Water Equivalent Retrieval Over Idaho, Part B: Using L-band UAVSAR Repeat-Pass Interferometry [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex \textlessp\textgreater\textlessstrong class="journal-contentHeaderColor"\textgreaterAbstract.\textless/strong\textgreater This study evaluates using interferometry on low frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 days temporal baseline, captured by an L-band aerial platform, NASA's UAVSAR, over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2e6) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5e4). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.\textless/p\textgreater
@article{hoppinen_snow_2023,
title = {Snow {Water} {Equivalent} {Retrieval} {Over} {Idaho}, {Part} {B}: {Using} {L}-band {UAVSAR} {Repeat}-{Pass} {Interferometry}},
shorttitle = {Snow {Water} {Equivalent} {Retrieval} {Over} {Idaho}, {Part} {B}},
url = {https://tc.copernicus.org/preprints/tc-2023-127/},
doi = {10.5194/tc-2023-127},
abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} This study evaluates using interferometry on low frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 days temporal baseline, captured by an L-band aerial platform, NASA's UAVSAR, over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2e6) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5e4). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.{\textless}/p{\textgreater}},
language = {English},
urldate = {2024-01-31},
journal = {The Cryosphere Discussions},
author = {Hoppinen, Zachary Marshall and Oveisgharan, Shadi and Marshall, Hans-Peter and Mower, Ross and Elder, Kelly and Vuyovich, Carrie},
month = aug,
year = {2023},
note = {Publisher: Copernicus GmbH},
keywords = {NALCMS},
pages = {1--24},
}
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We retrieved SWE changes from nine pairs of SAR images, mean 8 days temporal baseline, captured by an L-band aerial platform, NASA's UAVSAR, over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2e6) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5e4). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.\\textless/p\\textgreater","language":"English","urldate":"2024-01-31","journal":"The Cryosphere Discussions","author":[{"propositions":[],"lastnames":["Hoppinen"],"firstnames":["Zachary","Marshall"],"suffixes":[]},{"propositions":[],"lastnames":["Oveisgharan"],"firstnames":["Shadi"],"suffixes":[]},{"propositions":[],"lastnames":["Marshall"],"firstnames":["Hans-Peter"],"suffixes":[]},{"propositions":[],"lastnames":["Mower"],"firstnames":["Ross"],"suffixes":[]},{"propositions":[],"lastnames":["Elder"],"firstnames":["Kelly"],"suffixes":[]},{"propositions":[],"lastnames":["Vuyovich"],"firstnames":["Carrie"],"suffixes":[]}],"month":"August","year":"2023","note":"Publisher: Copernicus GmbH","keywords":"NALCMS","pages":"1–24","bibtex":"@article{hoppinen_snow_2023,\n\ttitle = {Snow {Water} {Equivalent} {Retrieval} {Over} {Idaho}, {Part} {B}: {Using} {L}-band {UAVSAR} {Repeat}-{Pass} {Interferometry}},\n\tshorttitle = {Snow {Water} {Equivalent} {Retrieval} {Over} {Idaho}, {Part} {B}},\n\turl = {https://tc.copernicus.org/preprints/tc-2023-127/},\n\tdoi = {10.5194/tc-2023-127},\n\tabstract = {{\\textless}p{\\textgreater}{\\textless}strong class=\"journal-contentHeaderColor\"{\\textgreater}Abstract.{\\textless}/strong{\\textgreater} This study evaluates using interferometry on low frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. 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Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.{\\textless}/p{\\textgreater}},\n\tlanguage = {English},\n\turldate = {2024-01-31},\n\tjournal = {The Cryosphere Discussions},\n\tauthor = {Hoppinen, Zachary Marshall and Oveisgharan, Shadi and Marshall, Hans-Peter and Mower, Ross and Elder, Kelly and Vuyovich, Carrie},\n\tmonth = aug,\n\tyear = {2023},\n\tnote = {Publisher: Copernicus GmbH},\n\tkeywords = {NALCMS},\n\tpages = {1--24},\n}\n\n\n\n\n\n\n\n","author_short":["Hoppinen, Z. 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