DocumentCode :
3024718
Title :
Ensemble of regressors for soil moisture retrieval in agricultural fields
Author :
Notarnicola, Claudia
Author_Institution :
EURAC-Inst. for Appl. Remote Sensing, Bolzano, Italy
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
723
Lastpage :
726
Abstract :
This paper presents an approach to improve the capability to retrieve soil moisture information from SAR data. More in details the proposed approach consider different inversion approaches and outlines a procedure how to combine the results derived from these regressors with the main aim to improve the accuracy in the estimation of the target variables. The approach was tested in the case of fully polarimetric AirSAR images acquired over agricultural fields covered with soybean and corn crops. The single regressors were an empirical and a Bayesian approach. The approaches were applied to C and L band images and also to a combination of both frequencies. The results indicate that when the retrieved information from the regressors are properly combined based on the select figures of merit such as R2 and RMSE the accuracy can improve up to around 30%.
Keywords :
Bayes methods; crops; hydrological techniques; moisture; radar polarimetry; regression analysis; remote sensing by radar; soil; synthetic aperture radar; Bayesian approach; C band images; L band images; SAR data; agricultural fields; corn crop; empirical approach; fully polarimetric AirSAR images; inversion approaches; regressor ensemble; soil moisture retrieval; soybean crop; target variable estimation; Accuracy; Backscatter; Bayes methods; Remote sensing; Soil moisture; Vegetation mapping; SAR; retrieval; soil moisture;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6721259
Filename :
6721259
Link To Document :
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