ab08a8d661e229f3f8f6d32a7a14fa0a remotesensing_a2022v14n24art6253.pdf 4e2ab0f47700a7fb402409b34a98deafa4a0ab58 remotesensing_a2022v14n24art6253.pdf 556aba173d875f167ec18ac75f16860ad814e49b72f985f7ecceedc688071f2a remotesensing_a2022v14n24art6253.pdf Title: An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data Subject: 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 mmday-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. Keywords: evapotranspiration; SEBAL; endmember selection; Landsat; GLDAS; time series Author: Mojtaba Saboori, Yousef Mousivand, Jordi Cristóbal, Reza Shah-Hosseini and Ali Mokhtari Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Dec 13 10:28:02 2022 CET ModDate: Wed Dec 14 11:30:45 2022 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 30 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 28300503 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- OYGWHX+VnURWPalladioL Type 1 Custom yes yes no 10 0 JWSNBF+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 IVBAUU+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 YHFUIU+URWPalladioL-Ital Type 1 Custom yes yes no 24 0 JIOETK+CMSY10 Type 1 Builtin yes yes no 28 0 SOZKOC+VnURWPalladioL-Bold Type 1 Custom yes 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LADPGO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 333 0 LADPHA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 336 0 LADPFM+PalatinoLinotype TrueType WinAnsi yes yes no 351 0 LADPGO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 354 0 LADPHA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 357 0 LADPFM+PalatinoLinotype TrueType WinAnsi yes yes no 371 0 LADPGO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 374 0 LADPHA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 377 0 LADPFM+PalatinoLinotype TrueType WinAnsi yes yes no 394 0 LADPGO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 397 0 LADPHA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 400 0 LADPFM+PalatinoLinotype TrueType WinAnsi yes yes no 412 0 LADPGO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 415 0 LADPHA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 418 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-01-16 02:42:48 CET RepresentationInformation: remotesensing_a2022v14n24art6253.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-01-15 20:23:17 CET Size: 28300503 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 760 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Material and Methods Destination: section.2 Children: Item: Title: Study Area and Dataset Destination: subsection.2.1 Children: Item: Title: Climate Type Classification Destination: subsubsection.2.1.1 Item: Title: Flux Tower Sites Destination: subsubsection.2.1.2 Item: Title: Remote Sensing, Land Cover and Meteorological Inputs Destination: subsubsection.2.1.3 Item: Title: Methods Destination: subsection.2.2 Children: Item: Title: SEBAL Algorithm: A Brief Description Destination: subsubsection.2.2.1 Item: Title: Automated Endmember Selection Destination: subsubsection.2.2.2 Item: Title: Results Destination: section.3 Children: Item: Title: Evaluation of Endmember Selection Destination: subsection.3.1 Item: Title: Assessment of ET Estimates with the Different Endmember Selection Models Destination: subsection.3.2 Children: Item: Title: Overall Daily ET Evaluation Destination: subsubsection.3.2.1 Item: Title: Evaluation of ET Estimation Results for Climate-Based (Global Aridity Index and Köppen Classification) and Land Cover-Based Trials Destination: subsubsection.3.2.2 Item: Title: Evaluation of ET Estimation When Using Humid/Non-Humid Climate-Based Classifications Destination: subsubsection.3.2.3 Item: Title: Overall Monthly ET Evaluation Destination: subsubsection.3.2.4 Item: Title: Discussion Destination: section.4 Children: Item: Title: Daily ET Evaluation Destination: subsection.4.1 Item: Title: Evaluation of Endmember Statistics Destination: subsection.4.2 Item: Title: Land Cover-Specific ET Assessment Destination: subsection.4.3 Item: Title: Land Cover-Climate ET Analysis Destination: subsection.4.4 Item: Title: Humid Climate- and Landscape-Based ET Assessment Destination: subsection.4.5 Item: Title: Monthly ET Evaluation Destination: subsection.4.6 Item: Title: Comparison with Previous Studies Destination: subsection.4.7 Item: Title: Conclusions Destination: section.5 Item: Title: Appendix A Destination: section.A. Item: Title: Appendix B Destination: section.B. Item: Title: Appendix C Destination: section.C. Item: Title: References Destination: figure.caption.29 Info: Title: An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data Author: Mojtaba Saboori, Yousef Mousivand, Jordi Cristóbal, Reza Shah-Hosseini and Ali Mokhtari Subject: 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 mmday-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. 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