A Bayesian approach for a SAC-D/aquarius soil moisture product

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, SCAH and SCAV; Microwave Polarization Difference Algorithm, MPDA) were com...

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Detalles Bibliográficos
Publicado: 2014
Materias:
Aquarius
Bayesian inference
Markov Chain Monte Carlo
soil moisture
Bayesian networks
Inference engines
Microwaves
Remote sensing
Soil moisture
AQUARIUS
Bayesian algorithms
Bayesian approaches
Markov Chain Monte-Carlo
Microwave polarizations
Retrieval algorithms
Single-channel algorithms
Algorithms
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814799_v_n_p1_Bruscantini
http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p1_Bruscantini
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_97814799_v_n_p1_Bruscantini
record_format dspace
spelling paper:paper_97814799_v_n_p1_Bruscantini2023-06-08T16:37:46Z A Bayesian approach for a SAC-D/aquarius soil moisture product Aquarius Bayesian inference Markov Chain Monte Carlo soil moisture Bayesian networks Inference engines Microwaves Remote sensing Soil moisture AQUARIUS Bayesian algorithms Bayesian approaches Bayesian inference Markov Chain Monte-Carlo Microwave polarizations Retrieval algorithms Single-channel algorithms Algorithms 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, SCAH and SCAV; Microwave Polarization Difference Algorithm, MPDA) were computed over Pampas Plains, Argentina. The methodology of a novel Bayesian algorithm developed is also presented, and its results are contrasted with the previous algorithms. Finally, performance metrics for each algorithms were derived using SMOS Level-2 sm and τ as benchmark products. The new Bayesian approach provide the sm retrieval algorithm that exhibited the lowest ubRMSE (0.115m 3/m3), though very close to USDA SCA and SCAV ubRMSE (0.116m3/m3). Nevertheless, some improvements are discussed in Section 4 that might increase significantly the Bayesian algorithm performance. © 2014 IEEE. 2014 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814799_v_n_p1_Bruscantini http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p1_Bruscantini
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Aquarius
Bayesian inference
Markov Chain Monte Carlo
soil moisture
Bayesian networks
Inference engines
Microwaves
Remote sensing
Soil moisture
AQUARIUS
Bayesian algorithms
Bayesian approaches
Bayesian inference
Markov Chain Monte-Carlo
Microwave polarizations
Retrieval algorithms
Single-channel algorithms
Algorithms
spellingShingle Aquarius
Bayesian inference
Markov Chain Monte Carlo
soil moisture
Bayesian networks
Inference engines
Microwaves
Remote sensing
Soil moisture
AQUARIUS
Bayesian algorithms
Bayesian approaches
Bayesian inference
Markov Chain Monte-Carlo
Microwave polarizations
Retrieval algorithms
Single-channel algorithms
Algorithms
A Bayesian approach for a SAC-D/aquarius soil moisture product
topic_facet Aquarius
Bayesian inference
Markov Chain Monte Carlo
soil moisture
Bayesian networks
Inference engines
Microwaves
Remote sensing
Soil moisture
AQUARIUS
Bayesian algorithms
Bayesian approaches
Bayesian inference
Markov Chain Monte-Carlo
Microwave polarizations
Retrieval algorithms
Single-channel algorithms
Algorithms
description 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, SCAH and SCAV; Microwave Polarization Difference Algorithm, MPDA) were computed over Pampas Plains, Argentina. The methodology of a novel Bayesian algorithm developed is also presented, and its results are contrasted with the previous algorithms. Finally, performance metrics for each algorithms were derived using SMOS Level-2 sm and τ as benchmark products. The new Bayesian approach provide the sm retrieval algorithm that exhibited the lowest ubRMSE (0.115m 3/m3), though very close to USDA SCA and SCAV ubRMSE (0.116m3/m3). Nevertheless, some improvements are discussed in Section 4 that might increase significantly the Bayesian algorithm performance. © 2014 IEEE.
title A Bayesian approach for a SAC-D/aquarius soil moisture product
title_short A Bayesian approach for a SAC-D/aquarius soil moisture product
title_full A Bayesian approach for a SAC-D/aquarius soil moisture product
title_fullStr A Bayesian approach for a SAC-D/aquarius soil moisture product
title_full_unstemmed A Bayesian approach for a SAC-D/aquarius soil moisture product
title_sort bayesian approach for a sac-d/aquarius soil moisture product
publishDate 2014
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814799_v_n_p1_Bruscantini
http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p1_Bruscantini
_version_ 1768546278222856192

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