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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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p1_Bruscantini |
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todo:paper_97814799_v_n_p1_Bruscantini2023-10-03T16:43:38Z A Bayesian approach for a SAC-D/aquarius soil moisture product Bruscantini, C.A. Grings, F. Barber, M. Perna, P. Karszenbaum, H. 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. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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 Bruscantini, C.A. Grings, F. Barber, M. Perna, P. Karszenbaum, H. 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. |
| format |
CONF |
| author |
Bruscantini, C.A. Grings, F. Barber, M. Perna, P. Karszenbaum, H. |
| author_facet |
Bruscantini, C.A. Grings, F. Barber, M. Perna, P. Karszenbaum, H. |
| author_sort |
Bruscantini, C.A. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_97814799_v_n_p1_Bruscantini |
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