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Inversion of snow parameters by neural network with iterative inversionThe inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense media multiple scattering model. A constrained iterative inversion scheme is used. Inversion of four parameters is performed from five brightness temperatures. The four parameters are: mean grain size of ice particles in snow, snow density, snow temperature, and snow depth. The five brightness temperatures are that of 19-GHz vertical polarization, 19-GHz horizontal polarization, 22-GHz vertical polarization, 37-GHz vertical polarization, and 37-GHz horizontal polarization. Based on the neural network constrained iterative inversion algorithm, synthetic mapping of the terrain is performed. The retrieval of synthetic mapping has been achieved. The incorporation of ground truth information is considered.
Document ID
19930063842
Document Type
Conference Paper
Authors
Chen, Zhengxiao (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Davis, Daniel (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Tsang, Leung (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Hwang, Jenq-Neng (Washington Univ. Seattle, United States)
Chang, A. T. C. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 2 (A93-47551 20-43)
Subject Category
EARTH RESOURCES AND REMOTE SENSING
Distribution Limits
Public
Copyright
Other

Related Records

IDRelationTitle19930063554Analytic PrimaryIGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vols. 1 & 2