By Topic

Spatial characterization of remotely sensed soil moisture data using self organizing feature maps

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

2 Author(s)
Kothari, R. ; Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA ; Islam, S.

Compact characterization of soil moisture at a given scale using self-organizing feature maps is presented. The authors find that as few as 49 neurons capture the spatial structure of remotely sensed soil moisture images from the southern Great Plains. Average latent heat flux computed from the original image of 21204 pixels and from 49 neurons are comparable

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:37 ,  Issue: 2 )