Using uncertainty of Penman and Penman-Monteith methods in combined satellite and ground-based evapotranspiration estimates

Satellite data are often used for their ability to fill in temporal and spatial patterns in data-sparse regions. It is also known that global satellite products generally contain more noise than ground-based estimates. Data validation of satellite data often treats ground-based estimates as the 'gold standard': without error or uncertainty. In the estimation of evapotranspiration (ET) however, ground-based estimates have considerable uncertainty, caused by the input components of the ET equations. This research presents an analysis of uncertainty of reference ET (ET0) caused by these input components. A dataset of correlated random variables is generated for a country with a diverse climate and diverse density of ground observations: New Zealand. The uncertainty analysis shows that: ET0 is most sensitive to temperature, followed by solar radiation, relative humidity, and cloudiness ratio; and that uncertainty varies between 10% and 40% of ET0, and depends on the ET0 value. Using this uncertainty analysis, a set of correlated random variables, and a Monte-Carlo fitting approach, MOD16 satellite PET data becomes a 'soft interpolator' between ground-based ET0 estimates. The resulting 1 km × 1 km monthly nation-wide dataset has the advantage of: taking into account land cover and vegetation characteristics through the use of satellite data; still abiding to local climate diversity and locally used standards through the use of ground-based estimates; and containing an uncertainty estimate. Further comparison suggests that original MOD16 satellite PET could estimate real PET better than using ground-based estimates of ET0. Further research recommends combination with other existing gridded ET estimates, and further validation of real PET estimates.