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Title: ANN technique for the evaluation of soil moisture over bare and vegetated fields from microwave radiometer data
Authors: Dharanibai, G
Alex, Z C
Keywords: Remote sensing;L-band microwave radiometry;Dielectric permittivity;Artificial neural network;Multi layer perceptron;Feed-forward neural network;Soil moisture
Issue Date: Oct-2009
Publisher: CSIR
PACS No.: 92.40.Lg; 84.40.-x
Abstract: Retrieving information from remotely sensed data is an important task. In the present work, data of L band microwave radiometer has been used to collect the brightness temperature over bare and vegetated fields in two polarizations at different moisture levels. Artificial neural network (ANN) trained with Levenberg-Marquardt algorithm has been used to determine soil moisture from brightness temperatures measured by microwave radiometry. ANN are trained to evaluate the moisture content in the range 0 - 36% from different sets of data of bare and vegetated fields. Properly trained feed-forward neural network with Levenberg-Marquardt algorithm predicted soil moisture content with less mean absolute error
Page(s): 283-288
ISSN: 0975-105X (Online); 0367-8393 (Print)
Appears in Collections:IJRSP Vol.38(5) [October 2009]

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