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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Detalles Bibliográficos
Publicado: 2014
Materias:
Aquarius
Artificial Neural Network
Bayesian inference
Markov Chain Monte Carlo
soil moisture
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814799_v_n_p2447_Bruscantini
http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p2447_Bruscantini
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_97814799_v_n_p2447_Bruscantini
record_format dspace
spelling paper:paper_97814799_v_n_p2447_Bruscantini2023-06-08T16:37:46Z SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina) Aquarius Artificial Neural Network Bayesian inference Markov Chain Monte Carlo soil moisture 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. © 2014 IEEE. 2014 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814799_v_n_p2447_Bruscantini http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p2447_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
Artificial Neural Network
Bayesian inference
Markov Chain Monte Carlo
soil moisture
spellingShingle Aquarius
Artificial Neural Network
Bayesian inference
Markov Chain Monte Carlo
soil moisture
SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)
topic_facet Aquarius
Artificial Neural Network
Bayesian inference
Markov Chain Monte Carlo
soil moisture
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, 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. © 2014 IEEE.
title SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)
title_short SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)
title_full SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)
title_fullStr SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)
title_full_unstemmed SAC-D/Aquarius soil moisture product development and evaluation for Pampas Plains (Argentina)
title_sort sac-d/aquarius soil moisture product development and evaluation for pampas plains (argentina)
publishDate 2014
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814799_v_n_p2447_Bruscantini
http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p2447_Bruscantini
_version_ 1768543968246628352

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