An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the...

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Authors: Saboori, Mojtaba, Mousivand, Youself, Cristóbal, Jordi, Sha-Hosseini, Reza, Mokhtari, Ali
Format: article
Publication Date:2022
Country:España
Institution:Institut de Recerca i Tecnologia Agroalimentàries (IRTA)
Repository:IRTA Pubpro. Open Digital Archive
OAI Identifier:oai:repositori.irta.cat:20.500.12327/2006
Online Access:http://hdl.handle.net/20.500.12327/2006
https://doi.org/10.3390/rs14246253
Access Level:Open access
Keyword:631
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spelling An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis dataSaboori, MojtabaMousivand, YouselfCristóbal, JordiSha-Hosseini, RezaMokhtari, Ali631The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ETinfo:eu-repo/semantics/publishedVersionMDPIProducció VegetalÚs Eficient de l'Aigua en Agricultura202320232022info:eu-repo/semantics/article30application/pdfhttp://hdl.handle.net/20.500.12327/2006https://doi.org/10.3390/rs14246253reponame:IRTA Pubpro. Open Digital Archiveinstname:Institut de Recerca i Tecnologia Agroalimentàries (IRTA)InglésRemote SensingMC/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/PID2021-127345OR-C31/ES/Enhanced remote sensing ET estimates for agricultural drought monitoring through improvements in ET partitioning and heterogeneous crop biophysical parameters retrieval/MC/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/TED2021-131237B-C21/ES/Evaluation of the digital twin paradigm applied to precision irrigation/DigiSPAChttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.irta.cat:20.500.12327/20062026-06-16T08:51:17Z
dc.title.none.fl_str_mv An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
title An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
spellingShingle An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
Saboori, Mojtaba
631
title_short An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
title_full An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
title_fullStr An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
title_full_unstemmed An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
title_sort An automated and improved methodology to retrieve long-time series of evapotranspiration based on remote sensing and reanalysis data
dc.creator.none.fl_str_mv Saboori, Mojtaba
Mousivand, Youself
Cristóbal, Jordi
Sha-Hosseini, Reza
Mokhtari, Ali
author Saboori, Mojtaba
author_facet Saboori, Mojtaba
Mousivand, Youself
Cristóbal, Jordi
Sha-Hosseini, Reza
Mokhtari, Ali
author_role author
author2 Mousivand, Youself
Cristóbal, Jordi
Sha-Hosseini, Reza
Mokhtari, Ali
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Producció Vegetal
Ús Eficient de l'Aigua en Agricultura
dc.subject.none.fl_str_mv 631
topic 631
description The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12327/2006
https://doi.org/10.3390/rs14246253
url http://hdl.handle.net/20.500.12327/2006
https://doi.org/10.3390/rs14246253
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Remote Sensing
MC/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/PID2021-127345OR-C31/ES/Enhanced remote sensing ET estimates for agricultural drought monitoring through improvements in ET partitioning and heterogeneous crop biophysical parameters retrieval/
MC/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/TED2021-131237B-C21/ES/Evaluation of the digital twin paradigm applied to precision irrigation/DigiSPAC
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 30
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:IRTA Pubpro. Open Digital Archive
instname:Institut de Recerca i Tecnologia Agroalimentàries (IRTA)
instname_str Institut de Recerca i Tecnologia Agroalimentàries (IRTA)
reponame_str IRTA Pubpro. Open Digital Archive
collection IRTA Pubpro. Open Digital Archive
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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