Cart (Loading....) | Create Account
Close category search window
 

N-parameter retrievals from L-band microwave observations acquired over a variety of crop fields

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

7 Author(s)
Parde, M. ; Ecologie Fonctionnelle et Phys. de I''Environnement, Inst. Nat. de la Recherche Agronomique, Villenave D''Omon, France ; Wigneron, J.-P. ; Waldteufel, P. ; Kerr, Y.H.
more authors

A number of studies have shown the feasibility of estimating surface soil moisture from L-band passive microwave measurements. Such measurements should be acquired in the near future by the Soil Moisture and Ocean Salinity (SMOS) mission. The SMOS measurements will be done at many incidence angles and two polarizations. This multiconfiguration capability could be very useful in soil moisture retrieval studies for decoupling between the effects of soil moisture and of the various surface parameters that also influence the surface emission (surface temperature, vegetation attenuation, soil roughness, etc.). The possibility to implement N-parameter (N-P) retrieval methods (where N = 2, 3, 4, ..., corresponds to the number of parameters that are retrieved) was investigated in this study based on experimental datasets acquired over a variety of crop fields. A large number of configurations of the N-P retrievals were studied, using several initializations of the model input parameters that were considered to be fixed or free. The best general configuration using no ancillary information (same configuration for all datasets) provided an rms error of about 0.059 m3/m3 in the soil moisture retrievals. If a priori information was available on soil roughness and at least one vegetation model parameter, the rms error decreased to 0.049 m3/m3. Using specific retrieval configurations for each dataset, the rms error was generally lower than 0.04 m3/m3.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:42 ,  Issue: 6 )

Date of Publication:

June 2004

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.