Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables
This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and l...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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MDPI
2021
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| Acceso en línea: | https://hdl.handle.net/10568/164331 |
| _version_ | 1855522655899222016 |
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| author | Majozi, Nobuhle Mannaerts, Chris Ramoelo, Abel Mathieu, Renaud Verhoef, Wouter |
| author_browse | Majozi, Nobuhle Mannaerts, Chris Mathieu, Renaud Ramoelo, Abel Verhoef, Wouter |
| author_facet | Majozi, Nobuhle Mannaerts, Chris Ramoelo, Abel Mathieu, Renaud Verhoef, Wouter |
| author_sort | Majozi, Nobuhle |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and land surface input variables and parameters of the PM-Mu model. Uncertainties of important intermediate data components (i.e., net radiation and aerodynamic and surface resistances) were also assessed. To estimate the instrument measurement uncertainties of the in situ meteorological input variables, we used the reported accuracies of the manufacturers. Observational accuracies of the remote sensing input variables (land surface temperature (LST), land surface emissivity (εs), leaf area index (LAI), land surface albedo (α)) were derived from peer-reviewed satellite sensor validation reports to compute their uncertainties. The input uncertainties were propagated to the final model’s evapotranspiration estimation uncertainty. Our analysis indicated relatively high uncertainties associated with relative humidity (RH), and hence all the intermediate variables associated with RH, like vapour pressure deficit (VPD) and the surface and aerodynamic resistances. This is in contrast to other studies, which reported LAI uncertainty as the most influential. The semi-arid conditions and seasonality of the regional South African climate and high temporal frequency of the variations in VPD, air and land surface temperatures could explain the uncertainties observed in this study. The results also showed the ET algorithm to be most sensitive to the air-land surface temperature difference. An accurate assessment of those in situ and remotely sensed variables is required to achieve reliable evapotranspiration model estimates in these generally dry regions and climates. A significant advantage of the remote-sensing-based ET method remains its full area coverage in contrast to classic-point (station)-based ET estimates. |
| format | Journal Article |
| id | CGSpace164331 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1643312024-12-22T05:44:50Z Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables Majozi, Nobuhle Mannaerts, Chris Ramoelo, Abel Mathieu, Renaud Verhoef, Wouter remote sensing climatic factors land use This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and land surface input variables and parameters of the PM-Mu model. Uncertainties of important intermediate data components (i.e., net radiation and aerodynamic and surface resistances) were also assessed. To estimate the instrument measurement uncertainties of the in situ meteorological input variables, we used the reported accuracies of the manufacturers. Observational accuracies of the remote sensing input variables (land surface temperature (LST), land surface emissivity (εs), leaf area index (LAI), land surface albedo (α)) were derived from peer-reviewed satellite sensor validation reports to compute their uncertainties. The input uncertainties were propagated to the final model’s evapotranspiration estimation uncertainty. Our analysis indicated relatively high uncertainties associated with relative humidity (RH), and hence all the intermediate variables associated with RH, like vapour pressure deficit (VPD) and the surface and aerodynamic resistances. This is in contrast to other studies, which reported LAI uncertainty as the most influential. The semi-arid conditions and seasonality of the regional South African climate and high temporal frequency of the variations in VPD, air and land surface temperatures could explain the uncertainties observed in this study. The results also showed the ET algorithm to be most sensitive to the air-land surface temperature difference. An accurate assessment of those in situ and remotely sensed variables is required to achieve reliable evapotranspiration model estimates in these generally dry regions and climates. A significant advantage of the remote-sensing-based ET method remains its full area coverage in contrast to classic-point (station)-based ET estimates. 2021-02-26 2024-12-19T12:53:44Z 2024-12-19T12:53:44Z Journal Article https://hdl.handle.net/10568/164331 en Open Access MDPI Majozi, Nobuhle; Mannaerts, Chris; Ramoelo, Abel; Mathieu, Renaud and Verhoef, Wouter. 2021. Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables. Remote Sensing, Volume 13 no. 5 p. 882 |
| spellingShingle | remote sensing climatic factors land use Majozi, Nobuhle Mannaerts, Chris Ramoelo, Abel Mathieu, Renaud Verhoef, Wouter Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables |
| title | Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables |
| title_full | Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables |
| title_fullStr | Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables |
| title_full_unstemmed | Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables |
| title_short | Uncertainty and sensitivity analysis of a remote-sensing-based penman–monteith model to meteorological and land surface input variables |
| title_sort | uncertainty and sensitivity analysis of a remote sensing based penman monteith model to meteorological and land surface input variables |
| topic | remote sensing climatic factors land use |
| url | https://hdl.handle.net/10568/164331 |
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