A Dynamic Snow Depth Inversion Algorithm Derived From AMSR2 Passive Microwave Brightness Temperature

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论文题目:	A Dynamic Snow Depth Inversion Algorithm Derived From AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China
英文论文题目:	A Dynamic Snow Depth Inversion Algorithm Derived From AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China
第一作者:	Wei, Yanlin
英文第一作者:	Wei, Yanlin
联系作者:	李晓峰
英文联系作者:	X. F. Li
外单位作者单位:
英文外单位作者单位:
发表年度:	2021
卷:	14
期:
页码:	5123-5136
摘要:	Snow cover plays an important role in climate, hydrology, and ecosystem. At present, passive microwave remote sensing is the most effective method for monitoring global and regional snow depth (SD). The traditional SD inversion algorithms use empirical or semiempirical methods to establish a fixed relationship between the SD and brightness temperature difference, given snow particle size and snow density. However, the snow characteristics present large temporal heterogeneity in Northeast China, and it leads to the inadaptability of the SD retrieval algorithm; using a fixed empirical coefficient will lead to large errors in SD inversion. In this study, a novel dynamic method was proposed to retrieve SD based on AMSR2 brightness temperature data. A snow survey experiment was designed to collect snow characteristics in different periods in Northeast China, and the microwave emission model of layered snowpacks was applied to simulate brightness temperature with varying snow characteristics to determine the dynamic coefficients in the SD retrieval algorithm. The validation results at 98 meteorological stations demonstrate that the novel dynamic SD inversion algorithm achieved better stability in the long-term sequence, its RMSE, bias, and R are 7.79 cm, 1.07 cm, and 0.61, respectively. Furthermore, compared with Che SD products, Chang algorithm, and AMSR2 SD products, the novel algorithm can obtain specific dynamic coefficients considering the snow metamorphism and has a higher accuracy of SD inversion in the whole winter. In conclusion, this novel SD inversion algorithm is more applicable and accurate than the existing SD inversion products in Northeast China.
英文摘要:	Snow cover plays an important role in climate, hydrology, and ecosystem. At present, passive microwave remote sensing is the most effective method for monitoring global and regional snow depth (SD). The traditional SD inversion algorithms use empirical or semiempirical methods to establish a fixed relationship between the SD and brightness temperature difference, given snow particle size and snow density. However, the snow characteristics present large temporal heterogeneity in Northeast China, and it leads to the inadaptability of the SD retrieval algorithm; using a fixed empirical coefficient will lead to large errors in SD inversion. In this study, a novel dynamic method was proposed to retrieve SD based on AMSR2 brightness temperature data. A snow survey experiment was designed to collect snow characteristics in different periods in Northeast China, and the microwave emission model of layered snowpacks was applied to simulate brightness temperature with varying snow characteristics to determine the dynamic coefficients in the SD retrieval algorithm. The validation results at 98 meteorological stations demonstrate that the novel dynamic SD inversion algorithm achieved better stability in the long-term sequence, its RMSE, bias, and R are 7.79 cm, 1.07 cm, and 0.61, respectively. Furthermore, compared with Che SD products, Chang algorithm, and AMSR2 SD products, the novel algorithm can obtain specific dynamic coefficients considering the snow metamorphism and has a higher accuracy of SD inversion in the whole winter. In conclusion, this novel SD inversion algorithm is more applicable and accurate than the existing SD inversion products in Northeast China.
刊物名称:	Ieee Journal of Selected Topics in Applied Earth Observations and Remote SensingIeee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
英文刊物名称:	Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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参与作者:	Y. L. Wei, X. F. Li, L. J. Gu, X. M. Zheng, T. Jiang, X. J. Li and X. K. Wan
英文参与作者:	Y. L. Wei, X. F. Li, L. J. Gu, X. M. Zheng, T. Jiang, X. J. Li and X. K. Wan

A Dynamic Snow Depth Inversion Algorithm Derived From AMSR2 Passive Microwave Brightness Temperature

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