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.
