Deriving daily evapotranspiration from remotely sensed instantaneous evaporative fraction over olive orchard in semi-arid Morocco

Abstract. Four upscaling methods for estimating daytime actual evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods assume self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggested that at-surface solar radiation was the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux yielded higher bias than the solar radiation method, although resulting errors showed similar lack of seasonal dependence. Finally, the commonly used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied. In general, the proposed methodology clearly showed the added value of an intercomparison of different upscaling methods under scenarios that account for the uncertainty in eddy covariance flux measurements due to closure errors.

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications

Cammalleri

Anderson

Kustas

2014

“…However, a number of recent studies indicate the assumption of selfpreservation is only approximate for these quantities. For example, both Gentine et al (2007) and Hoedjes et al (2008) showed that the self-preservation of evaporative fraction is sensitive to soil moisture conditions and fractional vegetation cover. Similarly, Lhomme and Elguero (1999) and later Van Niel et al (2012) showed that the degree of self-preservation can be influenced by cloud cover.…”

Section: Introductionmentioning

confidence: 99%

Effect of the revisit interval and temporal upscaling methods on the accuracy of remotely sensed evapotranspiration estimates

Alfieri

Anderson

Kustas

et al. 2017

Abstract. Accurate spatially distributed estimates of actual evapotranspiration (ET) derived from remotely sensed data are critical to a broad range of practical and operational applications. However, due to lengthy return intervals and cloud cover, data acquisition is not continuous over time, particularly for satellite sensors operating at medium ( ∼ 100 m) or finer resolutions. To fill the data gaps between clear-sky data acquisitions, interpolation methods that take advantage of the relationship between ET and other environmental properties that can be continuously monitored are often used. This study sought to evaluate the accuracy of this approach, which is commonly referred to as temporal upscaling, as a function of satellite revisit interval. Using data collected at 20 Ameriflux sites distributed throughout the contiguous United States and representing four distinct land cover types (cropland, grassland, forest, and open-canopy) as a proxy for perfect retrievals on satellite overpass dates, this study assesses daily ET estimates derived using five different reference quantities (incident solar radiation, net radiation, available energy, reference ET, and equilibrium latent heat flux) and three different interpolation methods (linear, cubic spline, and Hermite spline). Not only did the analyses find that the temporal autocorrelation, i.e., persistence, of all of the reference quantities was short, it also found that those land cover types with the greatest ET exhibited the least persistence. This carries over to the error associated with both the various scaled quantities and flux estimates. In terms of both the root mean square error (RMSE) and mean absolute error (MAE), the errors increased rapidly with increasing return interval following a logarithmic relationship. Again, those land cover types with the greatest ET showed the largest errors. Moreover, using a threshold of 20 % relative error, this study indicates that a return interval of no more than 5 days is necessary for accurate daily ET estimates. It also found that the spline interpolation methods performed erratically for long return intervals and should be avoided.

“…One common approach is based on surface temperature, which can be inferred from thermal radiation and is partly governed by energy partitioning into sensible and latent heat. Most studies following this approach aimed at estimating evapotranspiration more or less solely from remotely sensed data; their comparisons with ground measurements show correlations, but typically high noise levels (Moran et al, 1994;Kite and Droogers, 2000;GaratuzaPayan et al, 2001;Jiang and Islam, 2001;Jacobs et al, 2004;Patel et al, 2006;Wloczyk, 2007;Hoedjes et al, 2008;Galleguillos et al, 2011). Bastiaanssen et al (1998a,b) invented the SEBAL to account for many error sources by taking the coolest ("wet") and the warmest ("dry") pixel of a scan as calibration basis.…”

Section: The Remote Sensing Approachmentioning

confidence: 99%

Three perceptions of the evapotranspiration landscape: comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances

Conradt

Wechsung

Bronstert

2013

Abstract.A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km 2 ) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to NashSutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling.