Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/189268 |
Resumo: | Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates. |
id |
UFRGS-2_d17dbdb33ae07fe34f9910d6648b155f |
---|---|
oai_identifier_str |
oai:www.lume.ufrgs.br:10183/189268 |
network_acronym_str |
UFRGS-2 |
network_name_str |
Repositório Institucional da UFRGS |
repository_id_str |
|
spelling |
Tavares, Matheus HenriqueCunha, Augusto Hugo Farias daMarques, David Manuel Lelinho da MottaRuhoff, Anderson LuisCavalcanti, José Rafael de AlbuquerqueFragoso Júnior, Carlos RubertoBravo, Juan MartínMunar Samboní, Andrés MauricioFan, Fernando MainardiRodrigues, Lúcia Helena Ribeiro2019-03-08T02:31:10Z20192073-4441http://hdl.handle.net/10183/189268001088847Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.application/pdfengWater. Basel, Switzerland. Vol.11, n.1 (jan. 2019), 168, 21 f.Sensoriamento remotoLandsatLagosModerate Resolution Imaging Spectroradiometer (MODIS)Temperatura da águaWater-surface temperatureLakesRemote sensingThermal infraredLandsatComparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern BrazilEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001088847.pdf.txt001088847.pdf.txtExtracted Texttext/plain69527http://www.lume.ufrgs.br/bitstream/10183/189268/2/001088847.pdf.txtcbb513f164ea4e87a37dced1f200bd94MD52ORIGINAL001088847.pdfTexto completo (inglês)application/pdf748483http://www.lume.ufrgs.br/bitstream/10183/189268/1/001088847.pdf2fb6421ec7025a3a7dcc95a65d70a77eMD5110183/1892682019-06-21 02:34:24.749772oai:www.lume.ufrgs.br:10183/189268Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2019-06-21T05:34:24Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
title |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
spellingShingle |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil Tavares, Matheus Henrique Sensoriamento remoto Landsat Lagos Moderate Resolution Imaging Spectroradiometer (MODIS) Temperatura da água Water-surface temperature Lakes Remote sensing Thermal infrared Landsat |
title_short |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
title_full |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
title_fullStr |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
title_full_unstemmed |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
title_sort |
Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil |
author |
Tavares, Matheus Henrique |
author_facet |
Tavares, Matheus Henrique Cunha, Augusto Hugo Farias da Marques, David Manuel Lelinho da Motta Ruhoff, Anderson Luis Cavalcanti, José Rafael de Albuquerque Fragoso Júnior, Carlos Ruberto Bravo, Juan Martín Munar Samboní, Andrés Mauricio Fan, Fernando Mainardi Rodrigues, Lúcia Helena Ribeiro |
author_role |
author |
author2 |
Cunha, Augusto Hugo Farias da Marques, David Manuel Lelinho da Motta Ruhoff, Anderson Luis Cavalcanti, José Rafael de Albuquerque Fragoso Júnior, Carlos Ruberto Bravo, Juan Martín Munar Samboní, Andrés Mauricio Fan, Fernando Mainardi Rodrigues, Lúcia Helena Ribeiro |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Tavares, Matheus Henrique Cunha, Augusto Hugo Farias da Marques, David Manuel Lelinho da Motta Ruhoff, Anderson Luis Cavalcanti, José Rafael de Albuquerque Fragoso Júnior, Carlos Ruberto Bravo, Juan Martín Munar Samboní, Andrés Mauricio Fan, Fernando Mainardi Rodrigues, Lúcia Helena Ribeiro |
dc.subject.por.fl_str_mv |
Sensoriamento remoto Landsat Lagos Moderate Resolution Imaging Spectroradiometer (MODIS) Temperatura da água |
topic |
Sensoriamento remoto Landsat Lagos Moderate Resolution Imaging Spectroradiometer (MODIS) Temperatura da água Water-surface temperature Lakes Remote sensing Thermal infrared Landsat |
dc.subject.eng.fl_str_mv |
Water-surface temperature Lakes Remote sensing Thermal infrared Landsat |
description |
Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-03-08T02:31:10Z |
dc.date.issued.fl_str_mv |
2019 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/189268 |
dc.identifier.issn.pt_BR.fl_str_mv |
2073-4441 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001088847 |
identifier_str_mv |
2073-4441 001088847 |
url |
http://hdl.handle.net/10183/189268 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Water. Basel, Switzerland. Vol.11, n.1 (jan. 2019), 168, 21 f. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
instname_str |
Universidade Federal do Rio Grande do Sul (UFRGS) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Repositório Institucional da UFRGS |
collection |
Repositório Institucional da UFRGS |
bitstream.url.fl_str_mv |
http://www.lume.ufrgs.br/bitstream/10183/189268/2/001088847.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/189268/1/001088847.pdf |
bitstream.checksum.fl_str_mv |
cbb513f164ea4e87a37dced1f200bd94 2fb6421ec7025a3a7dcc95a65d70a77e |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS) |
repository.mail.fl_str_mv |
|
_version_ |
1815447683075145728 |