Abstract
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.
Highlights
As one component of a complex network of interconnected processes, evapotranspiration (ET) is influenced by numerous factors such as available energy, soil moisture, vegetation density, and humidity (Farquhar and Sharkey, 1982; Van de Griend and Owe, 1994; Alves and Pereira, 2000; Alfieri et al, 2007)
The available energy is controlled by surface properties, such as the type and density of vegetation cover and soil moisture, which influence the surface albedo and emissivity (Wittich, 1997; Asner et al, 1998; Myneni et al, 1989; Song et al, 1999; Lobell and Asner, 2002), and impact the amount of energy conducted into the ground (Friedl and Davis, 1994; Kustas et al, 2000; Abu-Hamdeh, 2003; Santanell and Friedl, 2003)
The results of this study indicate that the day-to-day persistence of χ typically used in the temporal upscaling of satellite-based ET retrievals is quite limited
Summary
As one component of a complex network of interconnected processes, evapotranspiration (ET) is influenced by numerous factors such as available energy, soil moisture, vegetation density, and humidity (Farquhar and Sharkey, 1982; Van de Griend and Owe, 1994; Alves and Pereira, 2000; Alfieri et al, 2007). The available energy is controlled by surface properties, such as the type and density of vegetation cover and soil moisture, which influence the surface albedo and emissivity (Wittich, 1997; Asner et al, 1998; Myneni et al, 1989; Song et al, 1999; Lobell and Asner, 2002), and impact the amount of energy conducted into the ground (Friedl and Davis, 1994; Kustas et al, 2000; Abu-Hamdeh, 2003; Santanell and Friedl, 2003). One example of this, which has been pointed out by Williams et al (2003), Scott et al (2014), and others, is the rapid and often persistent change in ET in response to a rain event.
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