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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17193355 |
