A Dynamic Snow Depth Inversion Algorithm Derived from AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China

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). Traditional SD inversion algorithms use empirical or semi-empirical methods to establish a fixed rela...

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Vydané v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 14; s. 1
Hlavní autori: Wei, Yanlin, Li, Xiaofeng, Gu, Lingjia, Zheng, Ming xing, Jiang, Tao, Li, Xiaojie, Wan, Xiangkun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Attenuation
Brightness
Brightness temperature
Coefficients
Dynamic Algorithm
Dynamic stability
Forestry
Heterogeneity
Heuristic algorithms
Hydrology
Metamorphism
Microwave emission
Microwave theory and techniques
Monitoring methods
Northeast China
Passive Microwave
Remote sensing
Retrieval
Snow
Snow cover
Snow density
Snow depth
Snow Depth (SD)
Snowpack
Surface radiation temperature
Surveying
Temperature data
Temperature differences
Temperature gradients
Temperature measurement
Weather stations
China
ISSN:1939-1404, 2151-1535
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Abstract 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). Traditional SD inversion algorithms use empirical or semi-empirical methods to establish a fixed relationship between SD and brightness temperature difference (TBD) given snow particle size and snow density. However, the snow characteristics present large temporal heterogeneity in Northeast China, 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 (MEMLS) 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 are7.79 cm, 1.06 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 existing SD inversion products in Northeast China.
AbstractList 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.
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). Traditional SD inversion algorithms use empirical or semi-empirical methods to establish a fixed relationship between SD and brightness temperature difference (TBD) given snow particle size and snow density. However, the snow characteristics present large temporal heterogeneity in Northeast China, 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 (MEMLS) 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 are7.79 cm, 1.06 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 existing SD inversion products in Northeast China.
Author Wan, Xiangkun
Gu, Lingjia
Wei, Yanlin
Li, Xiaofeng
Jiang, Tao
Zheng, Ming xing
Li, Xiaojie
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Snippet Snow cover plays an important role in climate, hydrology, and ecosystem. At present, passive microwave remote sensing is the most effective method for...
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SubjectTerms Algorithms
Attenuation
Brightness
Brightness temperature
Coefficients
Dynamic Algorithm
Dynamic stability
Forestry
Heterogeneity
Heuristic algorithms
Hydrology
Metamorphism
Microwave emission
Microwave theory and techniques
Monitoring methods
Northeast China
Passive Microwave
Remote sensing
Retrieval
Snow
Snow cover
Snow density
Snow depth
Snow Depth (SD)
Snowpack
Surface radiation temperature
Surveying
Temperature data
Temperature differences
Temperature gradients
Temperature measurement
Weather stations
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Title A Dynamic Snow Depth Inversion Algorithm Derived from AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China
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https://www.proquest.com/docview/2536868836
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Volume 14
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