Evapotranspiration Estimation in the Sahel Using a New Ensemble-Contextual Method

Abstract

In many tropical areas, evapotranspiration is the most important but least known component of the water cycle. An innovative method, named E3S (for EVASPA S-SEBI Sahel), was developed to provide spatially-distributed estimates of daily actual evapotranspiration (ETd) from remote sensing data in the Sahel. This new method combines the strengths of a contextual approach that is used to estimate the evaporative fraction (EF) from surface temperature vs. albedo scatterograms and of an ensemble approach that derives ETd estimates from a weighted average of evapotranspiration estimated from several EF methods. In this work, the two combined approaches were derived from the simplified surface energy balance index (S-SEBI) model and the EVapotranspiration Assessment from SPAce (EVASPA) tool. Main innovative aspects concern (i) ensemble predictions of ETd through the implementation of a dynamic weighting scheme of several evapotranspiration estimations, (ii) epistemic uncertainty of the estimation of ETd from the analysis of the variability of evapotranspiration estimates, and (iii) a new cloud filtering method that significantly improves the detection of cloud edges that negatively affect EF determination. E3S was applied to MODIS/TERRA and AQUA datasets acquired during the 2005–2008 period over the mesoscale AMMA-CATCH (Analyse Multidisciplinaire de la Mousson Africaine—Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique) observatory in South-West Niger. E3S estimates of instantaneous and daily available energy, evaporative fraction, and evapotranspiration were evaluated at a local scale based on two field-monitored plots representing the two main ecosystem types in the area—a millet crop and a fallow savannah bush. In addition to these ground-based observations, the local scale evaluation was performed against continuous simulations by a locally-calibrated soil-vegetation-atmosphere transfer model for the two plots. The RMSE (root mean square error) from this comparison for E3S’s ETd estimates from combined AQUA/TERRA sources was 0.5 mm·day−1, and the determination coefficient was 0.90. E3S significantly improved representation of the evapotranspiration seasonality, compared with a classical implementation of S-SEBI or with the original EVASPA’s non-weighted ensemble scheme. At the mesoscale, ETd estimates were obtained with an average epistemic uncertainty of 0.4 mm·day−1. Comparisons with the reference 0.25°-resolution GLEAM (global land evaporation Amsterdam model) product showed good agreement. These results suggested that E3S could be used to produce reliable continuous regional estimations at a kilometric resolution, consistent with land and water management requirements in the Sahel. Moreover, all these innovations could be easily transposed to other contextual approaches.

Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain

“…Nevertheless, they pointed out that a locally derived surface albedobased G model improved the G component estimates. The limitations of the empirical formulation used to derive G from remote sensing have already been emphasized in other works [23,24]. Furthermore, roughness length is also required in SEBS model computation, in addition to other meteorological inputs, such as air temperature, humidity, and wind speed measured at a reference height, which sustains the superiority hypothesis of the S-SEBI in terms of simplicity.…”

An evapotranspiration model driven by remote sensing data for assessing groundwater resource in karst watershed

“…For example, the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on board of the Terra and the Aqua satellites are viewing the entire Earth's surface every day, acquiring data that are used to monitor vegetation and ET at spatial resolutions of 1 km [e.g. Allies et al, 2020, Mu et al, 2007. In particular, the combination of the bands specific to the plant activity allows the calculation of vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) [Huete et al, 2010[Huete et al, , 2002[Huete et al, , 1994 and thus the monitoring of vegetation.…”

Evaluation of Multiple Methods for the Production of Continuous Evapotranspiration Estimates from TIR Remote Sensing

“…At 10:30, lower NI were obtained (data not shown). When considering the remote sensing dataset, RMSE ranged between 0.42 and 1.42 mm day −1 , which is also in the range of published evaluation of remote sensing estimates of ET (e.x., [36,39]). The use of remote-sensing based instantaneous estimates to reconstruct daily ET increased RMSE for 12/20 sites, in average by 0.45 mm day −1 .…”

“…It should be noted that interpolation at the time of satellite acquisition considers sun-synchronous satellite such as Landsat platform, and that it would not be fully adapted to other space systems such as MODIS on Terra and Aqua with variable acquisition time from day to day (see [39]). In these cases, it would be required to interpolate integrated daily value of ET rather than instantaneous values (e.x., [28,29,42]).…”