Improvement of Snow Albedo Simulation Considering Water Content.
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| Title: | Improvement of Snow Albedo Simulation Considering Water Content. |
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| Authors: | Li, Fengyu1 (AUTHOR), Wu, Kun1 (AUTHOR) wukun@nuist.edu.cn |
| Source: | Remote Sensing. Dec2025, Vol. 17 Issue 23, p3899. 16p. |
| Subjects: | Albedo, Moisture, Mie scattering, Radiative transfer, Grain size |
| Abstract: | Highlights: What are the main findings? Developed a snow albedo model that explicitly accounts for liquid water content (LWC) by integrating the Maxwell–Garnett mixing rule, Mie scattering theory, and a four-stream discrete ordinates adding method. The LWC on the surface of snow has a stronger impact on albedo, and snow with smaller particle sizes is more sensitive to changes in LWC. What are the implications of the main findings? Achieved improved accuracy in albedo simulations under certain conditions when compared with observations. Demonstrated a strong ability to express physical mechanisms and maintain stable performance in complex environments, making it applicable to wet snow containing impurities. By combining the Maxwell–Garnett mixing rule, Mie scattering, and the four-stream discrete ordinates adding method, a snow albedo model with explicit consideration of water content was constructed, and the influence of snow water content on snow albedo simulation was systematically analyzed. The results indicate that liquid water content is the key factor contributing to significant changes in albedo in the near-infrared band. The albedo of snow with small particle sizes is more sensitive to water content. The water content in the surface layer of snow has a more pronounced effect on reducing albedo. The actual measurement cases at the stations on the Tibetan Plateau, Xinjiang, and Northeast China show that the model established here provides a good simulation of albedo accuracy, with a bias of −0.0069 and a Root Mean Square Error (RMSE) of 0.0583 compared to the observations. This indicates that the model has a strong ability to express physical mechanisms and performs stably in complex environments, thereby demonstrating good regional applicability. This model can also be applied to wet snow containing impurities in the future. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Developed a snow albedo model that explicitly accounts for liquid water content (LWC) by integrating the Maxwell–Garnett mixing rule, Mie scattering theory, and a four-stream discrete ordinates adding method. The LWC on the surface of snow has a stronger impact on albedo, and snow with smaller particle sizes is more sensitive to changes in LWC. What are the implications of the main findings? Achieved improved accuracy in albedo simulations under certain conditions when compared with observations. Demonstrated a strong ability to express physical mechanisms and maintain stable performance in complex environments, making it applicable to wet snow containing impurities. By combining the Maxwell–Garnett mixing rule, Mie scattering, and the four-stream discrete ordinates adding method, a snow albedo model with explicit consideration of water content was constructed, and the influence of snow water content on snow albedo simulation was systematically analyzed. The results indicate that liquid water content is the key factor contributing to significant changes in albedo in the near-infrared band. The albedo of snow with small particle sizes is more sensitive to water content. The water content in the surface layer of snow has a more pronounced effect on reducing albedo. The actual measurement cases at the stations on the Tibetan Plateau, Xinjiang, and Northeast China show that the model established here provides a good simulation of albedo accuracy, with a bias of −0.0069 and a Root Mean Square Error (RMSE) of 0.0583 compared to the observations. This indicates that the model has a strong ability to express physical mechanisms and performs stably in complex environments, thereby demonstrating good regional applicability. This model can also be applied to wet snow containing impurities in the future. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17233899 |
