Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison

Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some...

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Autores: Galve Romero, Joan Miquel, Sánchez Tomás, Juan Manuel, García Santos, Vicente, González Piqueras, José, Calera Belmonte, Alfonso José, Villodre, Julio
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/42788
Acesso em linha:https://hdl.handle.net/10578/42788
Access Level:acceso abierto
Palavra-chave:Atmospheric correction
Barrax test site
Land surface emissivity
Landsat 8/TIRS
LST
SBAC
Thermal infrared
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spelling Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms IntercomparisonGalve Romero, Joan MiquelSánchez Tomás, Juan ManuelGarcía Santos, VicenteGonzález Piqueras, JoséCalera Belmonte, Alfonso JoséVillodre, JulioAtmospheric correctionBarrax test siteLand surface emissivityLandsat 8/TIRSLSTSBACThermal infraredMonitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018–2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within ±4.0 K for temperatures ranging 300–325 K. Statistics for the L-SBAC result in a RMSE of ±1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystemsMDPI202520252022info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://hdl.handle.net/10578/42788reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésPID2020-113498RB-C21PID2020-118797RB-I00SBPLY/17/180501/000357NEXUS project, grant number 101003632info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/427882026-05-27T07:36:41Z
dc.title.none.fl_str_mv Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
title Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
spellingShingle Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
Galve Romero, Joan Miquel
Atmospheric correction
Barrax test site
Land surface emissivity
Landsat 8/TIRS
LST
SBAC
Thermal infrared
title_short Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
title_full Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
title_fullStr Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
title_full_unstemmed Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
title_sort Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
dc.creator.none.fl_str_mv Galve Romero, Joan Miquel
Sánchez Tomás, Juan Manuel
García Santos, Vicente
González Piqueras, José
Calera Belmonte, Alfonso José
Villodre, Julio
author Galve Romero, Joan Miquel
author_facet Galve Romero, Joan Miquel
Sánchez Tomás, Juan Manuel
García Santos, Vicente
González Piqueras, José
Calera Belmonte, Alfonso José
Villodre, Julio
author_role author
author2 Sánchez Tomás, Juan Manuel
García Santos, Vicente
González Piqueras, José
Calera Belmonte, Alfonso José
Villodre, Julio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Atmospheric correction
Barrax test site
Land surface emissivity
Landsat 8/TIRS
LST
SBAC
Thermal infrared
topic Atmospheric correction
Barrax test site
Land surface emissivity
Landsat 8/TIRS
LST
SBAC
Thermal infrared
description Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018–2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within ±4.0 K for temperatures ranging 300–325 K. Statistics for the L-SBAC result in a RMSE of ±1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystems
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10578/42788
url https://hdl.handle.net/10578/42788
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2020-113498RB-C21
PID2020-118797RB-I00
SBPLY/17/180501/000357
NEXUS project, grant number 101003632
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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