SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)

In this work, several retrieval algorithms were implemented to retrieve soil moisture (sm) and optical depth (τ) from Aquarius/SAC-D observations. Currently used sm retrieval algorithms (H- and V-pol Single Channel Algorithm, Microwave Polarization Difference Algorithm) were computed over Pampas Pla...

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Bibliographic Details
Main Authors: Bruscantini, Cintia, Grings, Francisco, Carballo, Federico, Barber, Matias, Perna, Pablo, Karszenbaum, Haydee
Format: Conference Proceeding
Language:English
Subjects:
Aquarius
Artificial Neural Network
Artificial neural networks
Bayes methods
Bayesian inference
Markov Chain Monte Carlo
Measurement
Optical sensors
Soil moisture
Training
Vegetation mapping
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Summary:In this work, several retrieval algorithms were implemented to retrieve soil moisture (sm) and optical depth (τ) from Aquarius/SAC-D observations. Currently used sm retrieval algorithms (H- and V-pol Single Channel Algorithm, Microwave Polarization Difference Algorithm) were computed over Pampas Plains, Argentina. The methodology of a novel Bayesian algorithm developed was also presented, and its results were contrasted with the previous algorithms. Furthermore, an Artificial Neural Network (ANN) approach to retrieve sm from Aquarius brightness temperature was implemented and trained using SMOS Level-2 sm product. Finally, performance metrics for each algorithm were derived using SMOS L2 sm as benchmark product.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2014.6946967