Assessing the Sensitivity of Snow Depth Retrieval Algorithms to Inter-Sensor Brightness Temperature Differences.
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| Title: | Assessing the Sensitivity of Snow Depth Retrieval Algorithms to Inter-Sensor Brightness Temperature Differences. |
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| Authors: | Liu, Guangjin1 (AUTHOR), Jiang, Lingmei1,2 (AUTHOR) jiang@bnu.edu.cn, Cui, Huizhen1,2 (AUTHOR), Pan, Jinmei2 (AUTHOR), Yang, Jianwei1 (AUTHOR), Wu, Min1 (AUTHOR) |
| Source: | Remote Sensing. Oct2025, Vol. 17 Issue 19, p3355. 20p. |
| Subjects: | Brightness temperature, Sensitivity analysis, Hydrology, Climatology, Microwave radiometers, Remote sensing, Snow accumulation |
| Abstract: | Highlights: What are the main findings? We analyzed the sensitivity of seven snow depth retrieval algorithms (Chang, SPD, Foster, AMSR2, WESTDC, FY-3B, and FY-3D) to brightness temperature differences (TBDs) between passive microwave sensors (SSMIS, AMSR2, and MWRI). The SPD, WESTDC, FY-3B, and FY-3D algorithms exhibit relatively low sensitivity to TBDs, while the Foster algorithm demonstrates high sensitivity, especially in forested areas. What is the implication of the main finding? Algorithms with low sensitivity to TBDs improve the consistency of multi-sensor snow depth retrievals and lay the foundation for developing more stable retrieval methods in the future. These findings contribute to building passive microwave virtual constellations and ensuring reliable long-term snow depth records for climatology and hydrology. Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188675778 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing the Sensitivity of Snow Depth Retrieval Algorithms to Inter-Sensor Brightness Temperature Differences. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Guangjin%22">Liu, Guangjin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Lingmei%22">Jiang, Lingmei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jiang@bnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cui%2C+Huizhen%22">Cui, Huizhen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Jinmei%22">Pan, Jinmei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jianwei%22">Yang, Jianwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Min%22">Wu, Min</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Oct2025, Vol. 17 Issue 19, p3355. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrology%22">Hydrology</searchLink><br /><searchLink fieldCode="DE" term="%22Climatology%22">Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Microwave+radiometers%22">Microwave radiometers</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Snow+accumulation%22">Snow accumulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? We analyzed the sensitivity of seven snow depth retrieval algorithms (Chang, SPD, Foster, AMSR2, WESTDC, FY-3B, and FY-3D) to brightness temperature differences (TBDs) between passive microwave sensors (SSMIS, AMSR2, and MWRI). The SPD, WESTDC, FY-3B, and FY-3D algorithms exhibit relatively low sensitivity to TBDs, while the Foster algorithm demonstrates high sensitivity, especially in forested areas. What is the implication of the main finding? Algorithms with low sensitivity to TBDs improve the consistency of multi-sensor snow depth retrievals and lay the foundation for developing more stable retrieval methods in the future. These findings contribute to building passive microwave virtual constellations and ensuring reliable long-term snow depth records for climatology and hydrology. Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17193355 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 3355 Subjects: – SubjectFull: Brightness temperature Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Hydrology Type: general – SubjectFull: Climatology Type: general – SubjectFull: Microwave radiometers Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Snow accumulation Type: general Titles: – TitleFull: Assessing the Sensitivity of Snow Depth Retrieval Algorithms to Inter-Sensor Brightness Temperature Differences. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Guangjin – PersonEntity: Name: NameFull: Jiang, Lingmei – PersonEntity: Name: NameFull: Cui, Huizhen – PersonEntity: Name: NameFull: Pan, Jinmei – PersonEntity: Name: NameFull: Yang, Jianwei – PersonEntity: Name: NameFull: Wu, Min IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 19 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
