A method to improve MODIS AOD values: Application to South America

Autores
Lanzaco, Bethania Luz; Olcese, Luis Eduardo; Palancar, Gustavo Gerardo; Toselli, Beatriz Margarita
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs. The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R2, ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation. The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed. Considering the results as a whole, this method showed to be a valuable tool to enhance MODIS AOD retrievals, especially for locations with systematic deviations.
Fil: Lanzaco, Bethania Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Fil: Olcese, Luis Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Fil: Palancar, Gustavo Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Fil: Toselli, Beatriz Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Materia
AERONET
AOD SATELLITE RETRIEVAL
ARTIFICIAL NEURAL NETWORKS
MODIS AOD BIAS CORRECTION
SUPPORT VECTOR REGRESSION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/54894
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/54894
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A method to improve MODIS AOD values: Application to South AmericaLanzaco, Bethania LuzOlcese, Luis EduardoPalancar, Gustavo GerardoToselli, Beatriz MargaritaAERONETAOD SATELLITE RETRIEVALARTIFICIAL NEURAL NETWORKSMODIS AOD BIAS CORRECTIONSUPPORT VECTOR REGRESSIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1We present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs. The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R2, ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation. The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed. Considering the results as a whole, this method showed to be a valuable tool to enhance MODIS AOD retrievals, especially for locations with systematic deviations.Fil: Lanzaco, Bethania Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Olcese, Luis Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Palancar, Gustavo Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Toselli, Beatriz Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaTaiwan Assoc Aerosol Res-taar2016-06-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/54894Lanzaco, Bethania Luz; Olcese, Luis Eduardo; Palancar, Gustavo Gerardo; Toselli, Beatriz Margarita; A method to improve MODIS AOD values: Application to South America; Taiwan Assoc Aerosol Res-taar; Aerosol And Air Quality Research; 16; 6; 27-6-2016; 1509-15221680-85842071-1409CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://aaqr.org/Doi.php?id=16_AAQR-15-05-OA-0375info:eu-repo/semantics/altIdentifier/doi/10.4209/aaqr.2015.05.0375info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:14:58Zoai:ri.conicet.gov.ar:11336/54894instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:14:59.161CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A method to improve MODIS AOD values: Application to South America
title A method to improve MODIS AOD values: Application to South America
spellingShingle A method to improve MODIS AOD values: Application to South America
Lanzaco, Bethania Luz
AERONET
AOD SATELLITE RETRIEVAL
ARTIFICIAL NEURAL NETWORKS
MODIS AOD BIAS CORRECTION
SUPPORT VECTOR REGRESSION
title_short A method to improve MODIS AOD values: Application to South America
title_full A method to improve MODIS AOD values: Application to South America
title_fullStr A method to improve MODIS AOD values: Application to South America
title_full_unstemmed A method to improve MODIS AOD values: Application to South America
title_sort A method to improve MODIS AOD values: Application to South America
dc.creator.none.fl_str_mv Lanzaco, Bethania Luz
Olcese, Luis Eduardo
Palancar, Gustavo Gerardo
Toselli, Beatriz Margarita
author Lanzaco, Bethania Luz
author_facet Lanzaco, Bethania Luz
Olcese, Luis Eduardo
Palancar, Gustavo Gerardo
Toselli, Beatriz Margarita
author_role author
author2 Olcese, Luis Eduardo
Palancar, Gustavo Gerardo
Toselli, Beatriz Margarita
author2_role author
author
author
dc.subject.none.fl_str_mv AERONET
AOD SATELLITE RETRIEVAL
ARTIFICIAL NEURAL NETWORKS
MODIS AOD BIAS CORRECTION
SUPPORT VECTOR REGRESSION
topic AERONET
AOD SATELLITE RETRIEVAL
ARTIFICIAL NEURAL NETWORKS
MODIS AOD BIAS CORRECTION
SUPPORT VECTOR REGRESSION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs. The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R2, ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation. The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed. Considering the results as a whole, this method showed to be a valuable tool to enhance MODIS AOD retrievals, especially for locations with systematic deviations.
Fil: Lanzaco, Bethania Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Fil: Olcese, Luis Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Fil: Palancar, Gustavo Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
Fil: Toselli, Beatriz Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina
description We present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs. The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R2, ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation. The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed. Considering the results as a whole, this method showed to be a valuable tool to enhance MODIS AOD retrievals, especially for locations with systematic deviations.
publishDate 2016
dc.date.none.fl_str_mv 2016-06-27
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/54894
Lanzaco, Bethania Luz; Olcese, Luis Eduardo; Palancar, Gustavo Gerardo; Toselli, Beatriz Margarita; A method to improve MODIS AOD values: Application to South America; Taiwan Assoc Aerosol Res-taar; Aerosol And Air Quality Research; 16; 6; 27-6-2016; 1509-1522
1680-8584
2071-1409
CONICET Digital
CONICET
url http://hdl.handle.net/11336/54894
identifier_str_mv Lanzaco, Bethania Luz; Olcese, Luis Eduardo; Palancar, Gustavo Gerardo; Toselli, Beatriz Margarita; A method to improve MODIS AOD values: Application to South America; Taiwan Assoc Aerosol Res-taar; Aerosol And Air Quality Research; 16; 6; 27-6-2016; 1509-1522
1680-8584
2071-1409
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://aaqr.org/Doi.php?id=16_AAQR-15-05-OA-0375
info:eu-repo/semantics/altIdentifier/doi/10.4209/aaqr.2015.05.0375
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taiwan Assoc Aerosol Res-taar
publisher.none.fl_str_mv Taiwan Assoc Aerosol Res-taar
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781576785952768
score 12.982451

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