An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa

Abstract

Knowledge of evapotranspiration (ET) is essential for enhancing our understanding of the hydrological cycle, as well as for managing water resources, particularly in semi-arid regions. Remote sensing offers a comprehensive means of monitoring this phenomenon at different spatial and temporal intervals. Currently, several satellite methods exist and are used to assess ET at various spatial and temporal resolutions with various degrees of accuracy and precision. This research investigated the performance of three satellite-based ET algorithms and two global products, namely land surface temperature/vegetation index (TsVI), Penman-Monteith (PM), and the Meteosat Second Generation ET (MET) and the Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM) global products, in two eco-regions of South Africa. Daily ET derived from the eddy covariance system from Skukuza, a sub-tropical, savanna biome, and large aperture boundary layer scintillometer system in Elandsberg, a Mediterranean, fynbos biome, during the dry and wet seasons, were used to evaluate the models. Low coefficients of determination (R 2 ) of between 0 and 0.45 were recorded on both sites, during both seasons. Although PM performed best during periods of high ET at both sites, results show it was outperformed by other models during low ET times. TsVI and MET were similarly accurate in the dry season in Skukuza, as GLEAM was the most accurate in Elandsberg during the wet season. The conclusion is that none of the models performed well, as shown by low R 2 and high errors in all the models. In essence, our results conclude that further investigation of the PM model is possible to improve its estimation of low ET measurements.

Synthesis of global actual evapotranspiration from 1982 to 2019

“…GLEAM v3.3a spans from 1980 to 2018 and relies on reanalysis radiation and air temperature, a combination of gauge-based, reanalysis, and satellite-based precipitation, and satellite-based vegetation optical depth, while v3.3b spans from 2003 to 2018, and its forcing factors are the same as v3.3a except for radiation and air temperature which are based on remotely sensed data. Both v3.3a and v3.3b estimates are provided at a monthly temporal resolution and a 0.25 • × 0.25 • (≈ 25 km) spatial resolution (Miralles et al, 2011a, b;Martens et al, 2017).…”

Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman–Monteith Model to Meteorological and Land Surface Input Variables

“…A previous study by Majozi, et al [31] reported a distinct reasonable performance of the PM model as modified by Mu, et al [32], Mu, et al [33] (PM-Mu) during high ET periods. Consequently, this study investigated the sensitivity of this model to its input variables, i.e., both meteorological and land surface characteristics, in situ and remote sensing.…”

“…The Penman-Monteith model as modified by Mu et al [33,34] was assessed in this study. The latent heat flux was estimated as:…”

Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons

“…This increasing vegetation water-use e ciency may, to some extent, alleviate the increasing water de cits of tropical ecosystems 52,53 . However, this effect is currently absent in available empirically-or physically-based E products 54 , and thus cannot be explicitly considered in this study. Although the leaf-to ecosystem-level increase of water-use e ciency under higher CO 2 have been well-studied with laboratory and eld experiments 13,52,55,56 , the effects of this process on surface water availability at broader spatial scales remains unknown 53,57 .…”