Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region

Autores
Bayala, Martín Ignacio; Rivas, Raúl Eduardo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Land Surface Temperature (LST) is a key parameter in the energy balance model. However, the spatial resolution of the retrieved LST from sensors with high temporal resolution is not accurate enough to be used in local-scale studies. To explore the LST–Normalised Difference Vegetation Index relationship potential and obtain thermal images with high spatial resolution, six enhanced image sharpening techniques were assessed: the disaggregation procedure for radiometric surface temperatures (TsHARP), the Dry Edge Quadratic Function, the Difference of Edges (Ts * DL) and three models supported by the relationship of surface temperature and water stress of vegetation (Normalised Difference Water Index, Normalised Difference Infrared Index and Soil wetness index). Energy Balance Station data and in situ measurements were used to validate the enhanced LST images over a mixed agricultural landscape in the sub-humid Pampean Region of Argentina (PRA), during 2006–2010. Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (EOS-MODIS) thermal datasets were assessed for different spatial resolutions (e.g., 960, 720 and 240 m) and the performances were compared with global and local TsHARP procedures. Results suggest that the Ts * DL technique is the most adequate for simulating LST to high spatial resolution over the heterogeneous landscape of a sub-humid region, showing an average root mean square error of less than 1 K
Materia
Oceanografía, Hidrología, Recursos Hídricos
EOS-MODIS
Landsat TM
Land Surface Temperature (LST)
Sharpening models
Data validation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/7115

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oai_identifier_str oai:digital.cic.gba.gob.ar:11746/7115
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid regionBayala, Martín IgnacioRivas, Raúl EduardoOceanografía, Hidrología, Recursos HídricosEOS-MODISLandsat TMLand Surface Temperature (LST)Sharpening modelsData validationLand Surface Temperature (LST) is a key parameter in the energy balance model. However, the spatial resolution of the retrieved LST from sensors with high temporal resolution is not accurate enough to be used in local-scale studies. To explore the LST–Normalised Difference Vegetation Index relationship potential and obtain thermal images with high spatial resolution, six enhanced image sharpening techniques were assessed: the disaggregation procedure for radiometric surface temperatures (TsHARP), the Dry Edge Quadratic Function, the Difference of Edges (Ts * DL) and three models supported by the relationship of surface temperature and water stress of vegetation (Normalised Difference Water Index, Normalised Difference Infrared Index and Soil wetness index). Energy Balance Station data and in situ measurements were used to validate the enhanced LST images over a mixed agricultural landscape in the sub-humid Pampean Region of Argentina (PRA), during 2006–2010. Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (EOS-MODIS) thermal datasets were assessed for different spatial resolutions (e.g., 960, 720 and 240 m) and the performances were compared with global and local TsHARP procedures. Results suggest that the Ts * DL technique is the most adequate for simulating LST to high spatial resolution over the heterogeneous landscape of a sub-humid region, showing an average root mean square error of less than 1 KNational Authority Remote Sensing and Space Sciences (NARSS)2014-05-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/7115enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ejrs.2014.05.00info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:11Zoai:digital.cic.gba.gob.ar:11746/7115Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:11.479CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
title Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
spellingShingle Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
Bayala, Martín Ignacio
Oceanografía, Hidrología, Recursos Hídricos
EOS-MODIS
Landsat TM
Land Surface Temperature (LST)
Sharpening models
Data validation
title_short Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
title_full Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
title_fullStr Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
title_full_unstemmed Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
title_sort Enhanced sharpening procedures on edge difference and water stress index basis over heterogeneous landscape of sub-humid region
dc.creator.none.fl_str_mv Bayala, Martín Ignacio
Rivas, Raúl Eduardo
author Bayala, Martín Ignacio
author_facet Bayala, Martín Ignacio
Rivas, Raúl Eduardo
author_role author
author2 Rivas, Raúl Eduardo
author2_role author
dc.subject.none.fl_str_mv Oceanografía, Hidrología, Recursos Hídricos
EOS-MODIS
Landsat TM
Land Surface Temperature (LST)
Sharpening models
Data validation
topic Oceanografía, Hidrología, Recursos Hídricos
EOS-MODIS
Landsat TM
Land Surface Temperature (LST)
Sharpening models
Data validation
dc.description.none.fl_txt_mv Land Surface Temperature (LST) is a key parameter in the energy balance model. However, the spatial resolution of the retrieved LST from sensors with high temporal resolution is not accurate enough to be used in local-scale studies. To explore the LST–Normalised Difference Vegetation Index relationship potential and obtain thermal images with high spatial resolution, six enhanced image sharpening techniques were assessed: the disaggregation procedure for radiometric surface temperatures (TsHARP), the Dry Edge Quadratic Function, the Difference of Edges (Ts * DL) and three models supported by the relationship of surface temperature and water stress of vegetation (Normalised Difference Water Index, Normalised Difference Infrared Index and Soil wetness index). Energy Balance Station data and in situ measurements were used to validate the enhanced LST images over a mixed agricultural landscape in the sub-humid Pampean Region of Argentina (PRA), during 2006–2010. Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (EOS-MODIS) thermal datasets were assessed for different spatial resolutions (e.g., 960, 720 and 240 m) and the performances were compared with global and local TsHARP procedures. Results suggest that the Ts * DL technique is the most adequate for simulating LST to high spatial resolution over the heterogeneous landscape of a sub-humid region, showing an average root mean square error of less than 1 K
description Land Surface Temperature (LST) is a key parameter in the energy balance model. However, the spatial resolution of the retrieved LST from sensors with high temporal resolution is not accurate enough to be used in local-scale studies. To explore the LST–Normalised Difference Vegetation Index relationship potential and obtain thermal images with high spatial resolution, six enhanced image sharpening techniques were assessed: the disaggregation procedure for radiometric surface temperatures (TsHARP), the Dry Edge Quadratic Function, the Difference of Edges (Ts * DL) and three models supported by the relationship of surface temperature and water stress of vegetation (Normalised Difference Water Index, Normalised Difference Infrared Index and Soil wetness index). Energy Balance Station data and in situ measurements were used to validate the enhanced LST images over a mixed agricultural landscape in the sub-humid Pampean Region of Argentina (PRA), during 2006–2010. Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (EOS-MODIS) thermal datasets were assessed for different spatial resolutions (e.g., 960, 720 and 240 m) and the performances were compared with global and local TsHARP procedures. Results suggest that the Ts * DL technique is the most adequate for simulating LST to high spatial resolution over the heterogeneous landscape of a sub-humid region, showing an average root mean square error of less than 1 K
publishDate 2014
dc.date.none.fl_str_mv 2014-05-17
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 https://digital.cic.gba.gob.ar/handle/11746/7115
url https://digital.cic.gba.gob.ar/handle/11746/7115
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ejrs.2014.05.00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv National Authority Remote Sensing and Space Sciences (NARSS)
publisher.none.fl_str_mv National Authority Remote Sensing and Space Sciences (NARSS)
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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