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Inversion of parameters for semiarid regions by a neural networkMicrowave brightness temperatures obtained from a passive radiative transfer model are inverted through use of a neural network. The model is applicable to semiarid regions and produces dual-polarized brightness temperatures for 6.6-, 10.7-, and 37-GHz frequencies. A range of temperatures is generated by varying three geophysical parameters over acceptable ranges: soil moisture, vegetation moisture, and soil temperature. A multilayered perceptron (MLP) neural network is trained with a subset of the generated temperatures, and the remaining temperatures are inverted using a backpropagation method. Several synthetic terrains are devised and inverted by the network under local constraints. All the inversions show good agreement with the original geophysical parameters, falling within 5 percent of the actual value of the parameter range.
Document ID
19930063847
Document Type
Conference Paper
Authors
Zurk, Lisa M. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Davis, Daniel (Washington Univ. Seattle, United States)
Njoku, Eni G. (JPL Pasadena, CA, United States)
Tsang, Leung (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Hwang, Jenq-Neng (Washington Univ. Seattle, 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