By Topic

A robust statistical-based estimator for soil moisture retrieval from radar measurements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Dawson, M.S. ; Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA ; Fung, A.K. ; Manry, M.T.

The authors examine the use of a robust statistical inversion approach to the estimation of soil moisture and roughness statistics from backscatter measurements. Two sets of basis functions are examined; the first is a set of basis functions from multinomial combinations of the inputs (termed the MBF) while the second is a set of basis functions generated by a multilayer perceptron referred to as MLPBF. The authors discuss potential sources of training patterns upon which to base these estimators, including empirical forward models and more rigorous theoretical scattering models such as the IEM. These estimators are applied to a set of measured POLARSCAT data from Oh et al. [1992]. Comparisons are made with other inversion methods including neural networks

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:35 ,  Issue: 1 )