Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US
A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods h...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/12/2021 |
| _version_ | 1827714326900768768 |
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| author | Shadi Oveisgharan Daniel Esteban-Fernandez Duane Waliser Randall Friedl Son Nghiem Xubin Zeng |
| author_facet | Shadi Oveisgharan Daniel Esteban-Fernandez Duane Waliser Randall Friedl Son Nghiem Xubin Zeng |
| author_sort | Shadi Oveisgharan |
| collection | DOAJ |
| description | A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius. |
| first_indexed | 2024-03-10T18:55:12Z |
| format | Article |
| id | doaj.art-4a6c060474834dbfbdbeb143abb326ab |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:55:12Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4a6c060474834dbfbdbeb143abb326ab2023-11-20T04:47:21ZengMDPI AGRemote Sensing2072-42922020-06-011212202110.3390/rs12122021Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the USShadi Oveisgharan0Daniel Esteban-Fernandez1Duane Waliser2Randall Friedl3Son Nghiem4Xubin Zeng5Jet Propulsion Lab, Pasadena, CA 91109, USAJet Propulsion Lab, Pasadena, CA 91109, USAJet Propulsion Lab, Pasadena, CA 91109, USAJet Propulsion Lab, Pasadena, CA 91109, USAJet Propulsion Lab, Pasadena, CA 91109, USADepartment of Atmospheric Sciences, University of Arizona, Tucson, AZ 85719, USAA large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.https://www.mdpi.com/2072-4292/12/12/2021SWEretrievalsensitivitydifferential interferometrydual polarization dual frequency |
| spellingShingle | Shadi Oveisgharan Daniel Esteban-Fernandez Duane Waliser Randall Friedl Son Nghiem Xubin Zeng Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US Remote Sensing SWE retrieval sensitivity differential interferometry dual polarization dual frequency |
| title | Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US |
| title_full | Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US |
| title_fullStr | Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US |
| title_full_unstemmed | Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US |
| title_short | Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US |
| title_sort | evaluating the preconditions of two remote sensing swe retrieval algorithms over the us |
| topic | SWE retrieval sensitivity differential interferometry dual polarization dual frequency |
| url | https://www.mdpi.com/2072-4292/12/12/2021 |
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