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

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.

An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations

“…It has to be mentioned that the conversion of instantaneous ET to the daily scale is performed for the same day primarily because of (1) the daily revisit period of the MODIS sensor that provides widely applied land products and (2) the possibly decreased validity of the constancy assumption made in the conversion methods over a longer time scale [ Alfieri et al ., ]. The effectiveness of the improved method during subsequent days is beyond the scope of this paper and is therefore not investigated in the current study.…”

Temporal interpolation of land surface fluxes derived from remote sensing – results with an Unmanned Aerial System

“…This study showed that SVEN can be used as a tool to temporally interpolate land surface variables between remote sensing acquisitions with few meteorological data. In the statistical approaches, Alfieri et al (2017) identified that a return interval of remote sensing observations should be no less than 5 days to accurately interpolate daily ET with relative errors less than 20%.…”

“…However, both optical and thermal satellite observations present gaps during cloudy periods, and those gaps may coincide with the time when such information is mostly needed (Westermann et al, 2011), for instance the high frequency of cloudy weather during the crop growing season in monsoonal regimes (García et al, 2013) and high latitude regions (Wang et al, 2018a). Methods are needed to temporally interpolate and upscale the instantaneous records into continuous daily, monthly or annual values (Alfieri et al, 2017;Huang et al, 2016). As one of the most exciting recent advances in near-Earth 10 observation, Unmanned Aerial Systems (UAS) can flexibly fly at a low altitude (< 100-200 m) with favourable revisit times and low cost (Berni et al, 2009;McCabe et al, 2017).…”

“…Ts, Rn, SM, ET and GPP, using statistical interpolation could be challenging with low revisit frequency. For 25 instance, Alfieri et al (2017) found that a return interval of EO observations of no less than 5 days was necessary to statistically interpolate daily ET with relative errors smaller than 20%. To interpolate low persistence variables between remote sensing acquisitions, a dynamic model based interpolation approach considering the dynamics of the land surface energy balance can be better.…”

Correction of table-2

“…(4). In fact, it is clear that some profiles can be parabolic in shape (i.e., demonstrate an inflection point), especially immediately after rain events (dynamic case), or due to sharp changes in soil characteristics (Al-Hamdan and Cruise, 2010;Mishra et al, 2015) (see Fig. A1).…”