Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales

Autores
Piñuela, F.; Niclós, R.; Sánchez Tomás, J.M.; Coll, C.; Degano, María Florencia; Rivas, Raúl Eduardo; Bayala, Martín Ignacio
Año de publicación
2018
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión aceptada
Descripción
During the last years, both the technological development and the greater availability of geospatial information have led to the emergence of new application areas for remote sensing techniques. This is also relevant in the case of thermal remote sensing. Applications such as crop tracking require a greater availability of thermal information with spatial resolutions appropriate for a more local level scope. However, and despite the increasing availability of remote sensing products that have appeared and are expected to appear in the coming years, thermal infrared data continue to be available at lower spatial resolutions than the visible and nearinfrared data. Numerous authors have developed or tested methods to extract information at the sub-pixel level by using complementary remote sensing products with suitable results for using in applications at higher scales. Most of these methods are based on correlations between some vegetation indexes, such as NDVI, and radiative temperatures for a given cover. They are based on traditional mathematical models, such as linear or quadratic regression. Despite newer analysis tools like Support Vector Machines (SVM) or Neural Networks (NN) have become relevant in the last decade, their application on thermal remote sensing is in an relatively early stage of research and the use of traditional methods remains nowadays. The objective of this study is carrying out a comparison of these methods. A downscaling process from a MODIS temperature product scene has been developed using different methodologies. The results have been evaluated using “in situ” (ground-truth) temperature measurements showing an estimate of the accuracy and the potential of two different techniques.
Materia
Oceanografía, Hidrología, Recursos Hídricos
remote sensing techniques
thermal infrared data
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/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:11746/8942

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spelling Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scalesPiñuela, F.Niclós, R.Sánchez Tomás, J.M.Coll, C.Degano, María FlorenciaRivas, Raúl EduardoBayala, Martín IgnacioOceanografía, Hidrología, Recursos Hídricosremote sensing techniquesthermal infrared dataDuring the last years, both the technological development and the greater availability of geospatial information have led to the emergence of new application areas for remote sensing techniques. This is also relevant in the case of thermal remote sensing. Applications such as crop tracking require a greater availability of thermal information with spatial resolutions appropriate for a more local level scope. However, and despite the increasing availability of remote sensing products that have appeared and are expected to appear in the coming years, thermal infrared data continue to be available at lower spatial resolutions than the visible and nearinfrared data. Numerous authors have developed or tested methods to extract information at the sub-pixel level by using complementary remote sensing products with suitable results for using in applications at higher scales. Most of these methods are based on correlations between some vegetation indexes, such as NDVI, and radiative temperatures for a given cover. They are based on traditional mathematical models, such as linear or quadratic regression. Despite newer analysis tools like Support Vector Machines (SVM) or Neural Networks (NN) have become relevant in the last decade, their application on thermal remote sensing is in an relatively early stage of research and the use of traditional methods remains nowadays. The objective of this study is carrying out a comparison of these methods. A downscaling process from a MODIS temperature product scene has been developed using different methodologies. The results have been evaluated using “in situ” (ground-truth) temperature measurements showing an estimate of the accuracy and the potential of two different techniques.2018info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/8942enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:09Zoai:digital.cic.gba.gob.ar:11746/8942Institucionalhttp://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 13:40:09.534CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
title Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
spellingShingle Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
Piñuela, F.
Oceanografía, Hidrología, Recursos Hídricos
remote sensing techniques
thermal infrared data
title_short Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
title_full Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
title_fullStr Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
title_full_unstemmed Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
title_sort Analysis of disaggregation techiques applied to satellite images for the estimation of surface termal parameters at different scales
dc.creator.none.fl_str_mv Piñuela, F.
Niclós, R.
Sánchez Tomás, J.M.
Coll, C.
Degano, María Florencia
Rivas, Raúl Eduardo
Bayala, Martín Ignacio
author Piñuela, F.
author_facet Piñuela, F.
Niclós, R.
Sánchez Tomás, J.M.
Coll, C.
Degano, María Florencia
Rivas, Raúl Eduardo
Bayala, Martín Ignacio
author_role author
author2 Niclós, R.
Sánchez Tomás, J.M.
Coll, C.
Degano, María Florencia
Rivas, Raúl Eduardo
Bayala, Martín Ignacio
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Oceanografía, Hidrología, Recursos Hídricos
remote sensing techniques
thermal infrared data
topic Oceanografía, Hidrología, Recursos Hídricos
remote sensing techniques
thermal infrared data
dc.description.none.fl_txt_mv During the last years, both the technological development and the greater availability of geospatial information have led to the emergence of new application areas for remote sensing techniques. This is also relevant in the case of thermal remote sensing. Applications such as crop tracking require a greater availability of thermal information with spatial resolutions appropriate for a more local level scope. However, and despite the increasing availability of remote sensing products that have appeared and are expected to appear in the coming years, thermal infrared data continue to be available at lower spatial resolutions than the visible and nearinfrared data. Numerous authors have developed or tested methods to extract information at the sub-pixel level by using complementary remote sensing products with suitable results for using in applications at higher scales. Most of these methods are based on correlations between some vegetation indexes, such as NDVI, and radiative temperatures for a given cover. They are based on traditional mathematical models, such as linear or quadratic regression. Despite newer analysis tools like Support Vector Machines (SVM) or Neural Networks (NN) have become relevant in the last decade, their application on thermal remote sensing is in an relatively early stage of research and the use of traditional methods remains nowadays. The objective of this study is carrying out a comparison of these methods. A downscaling process from a MODIS temperature product scene has been developed using different methodologies. The results have been evaluated using “in situ” (ground-truth) temperature measurements showing an estimate of the accuracy and the potential of two different techniques.
description During the last years, both the technological development and the greater availability of geospatial information have led to the emergence of new application areas for remote sensing techniques. This is also relevant in the case of thermal remote sensing. Applications such as crop tracking require a greater availability of thermal information with spatial resolutions appropriate for a more local level scope. However, and despite the increasing availability of remote sensing products that have appeared and are expected to appear in the coming years, thermal infrared data continue to be available at lower spatial resolutions than the visible and nearinfrared data. Numerous authors have developed or tested methods to extract information at the sub-pixel level by using complementary remote sensing products with suitable results for using in applications at higher scales. Most of these methods are based on correlations between some vegetation indexes, such as NDVI, and radiative temperatures for a given cover. They are based on traditional mathematical models, such as linear or quadratic regression. Despite newer analysis tools like Support Vector Machines (SVM) or Neural Networks (NN) have become relevant in the last decade, their application on thermal remote sensing is in an relatively early stage of research and the use of traditional methods remains nowadays. The objective of this study is carrying out a comparison of these methods. A downscaling process from a MODIS temperature product scene has been developed using different methodologies. The results have been evaluated using “in situ” (ground-truth) temperature measurements showing an estimate of the accuracy and the potential of two different techniques.
publishDate 2018
dc.date.none.fl_str_mv 2018
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