Uncertainty and sensitivity analysis of a remote-sensing-based Penman-Monteith model to meteorological and land surface input variables

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dc.contributor.author Majozi, Nobuhle
dc.contributor.author Mannaerts, Chris M.
dc.contributor.author Ramoelo, Abel
dc.contributor.author Mathieu, Renaud
dc.contributor.author Verhoef, Wouter
dc.date.accessioned 2022-05-24T11:25:43Z
dc.date.available 2022-05-24T11:25:43Z
dc.date.issued 2021-02
dc.description.abstract 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. en_US
dc.description.department Geography, Geoinformatics and Meteorology en_US
dc.description.librarian pm2022 en_US
dc.description.sponsorship Council for Scientific and Industrial Research under the project Natural Resources and Environment Parliamentary Grant and Young Researcher Establishment Fund. en_US
dc.description.uri http://www.mdpi.com/journal/remotesensing en_US
dc.identifier.citation Majozi, N.P.; Mannaerts, C.M.; Ramoelo, A.; Mathieu, R.; Verhoef, W. Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman–Monteith Model to Meteorological and Land Surface Input Variables. Remote Sensing 2021, 13, 882. https://doi.org/10.3390/rs13050882. en_US
dc.identifier.issn 2072-4292 (online)
dc.identifier.other 10.3390/ rs13050882
dc.identifier.uri https://repository.up.ac.za/handle/2263/85661
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.rights © 2021 by the authors. Licensee: MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_US
dc.subject Sensitivity analysis en_US
dc.subject Uncertainty analysis en_US
dc.subject Remote sensing en_US
dc.subject Evapotranspiration en_US
dc.subject Absolute uncertainty en_US
dc.subject Relative uncertainty en_US
dc.subject Penman–Monteith (PM-Mu) en_US
dc.title Uncertainty and sensitivity analysis of a remote-sensing-based Penman-Monteith model to meteorological and land surface input variables en_US
dc.type Article en_US


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