Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

Abstract

Currently, thermal remote sensing-based evapotranspiration (ET) models can only calculate instantaneous ET at the time of satellite overpass. Five temporal upscaling methods, namely, constant evaporative fraction (ConEF), corrected ConEF (CorEF), diurnal evaporative fraction (DiEF), constant solar radiation ratio (SolRad), and constant reference evaporative fraction (ConETrF), were selected to upscale the instantaneous ET to daily values. Moreover, five temporal reconstruction approaches, namely, data assimilation (ET_EnKF and ET_SCE_UA), surface resistance (ET_SR), reference evapotranspiration (ET_ETrF), and harmonic analysis of time series (ET_HANTS), were used to produce continuous daily ET with discrete clear-sky daily ET values. For clear-sky daily ET generation, SolRad and ConETrF produced the best estimates. In contrast, ConEF usually underestimated the daily ET. The optimum method, however, was found by combining SolRad and ConETrF, which produced the lowest root-mean-square error (RMSE) values. OPEN ACCESSRemote Sens. 2015, 7 3401For continuous daily ET production, ET_ETrF and ET_SCE_UA performed the best, whereas the ET_SR and ET_HANTS methods had large errors. The annual ET distributions over the Beijing area were calculated with these methods. The spatial ET distributions from ET_ETrF and ET_SCE_UA had the same trend as ETWatch products, and had a smaller RMSE when compared with ET observations derived from the water balance method.

Estimating Daily Evapotranspiration From Remotely Sensed Instantaneous Observations With Simplified Derivations of a Theoretical Model

“…This method, which simply assumes that the instantaneous EF equals the daily average, is perhaps the most widely used scheme for the conversion of instantaneous ET to daily values. However, due to the concave‐up shape of the daytime EF on clear‐sky or constant‐cloud days, many authors have reported that the constant EF method tends to significantly underestimate the daily ET (Cammalleri et al, ; Chávez et al, ; Delogu et al, ; Tang et al, ; Tang, Li, & Sun, ; Tang & Li, ; Xu et al, ) and the underestimation can even reach as large as 34% (Van Niel et al, ). Similar to the constant EF method that makes use of surface available energy as the conversion variable, some other schemes (Chávez et al, ; Ryu et al, ; Tang et al, ; Tang et al, ; Trezza, ; Van Niel et al, ) have also attempted to assume a temporally stable ratio of ET to surface downward solar radiation, surface net radiation, extraterrestrial solar radiation, or reference grass/alfalfa ET in a diurnal cycle.…”

Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions

“…Additionally, the self-preservation assumption of the EF is not always valid under wet conditions. During the day time, the EF of farmlands curve is concave upward shows that the EF is higher in the early morning and later afternoon [65]. Using the EF around noon to estimate daily ET may be another reason for the underestimation of daily ET.…”

Temporal upscaling of instantaneous evapotranspiration on clear‐sky days using the constant reference evaporative fraction method with fixed or variable surface resistances at two cropland sites

“…In the upscaling of instantaneous ET, both the constant EFr method and the constant global solar radiation ratio method outperform the constant evaporative fraction method, the constant extraterrestrial solar radiation ratio method, and the sinusoidal function method, although there is no agreement on which of the former two methods performs better [ Xu et al ., ; Tang et al ., , ; Cammalleri et al ., ]. To investigate whether the use of a variable surface resistance in the reference ET estimation from the PM equation, as proposed by Todorovic [], can improve the daily upscaled ET on clear‐sky days from the constant EFr method, half‐hourly near‐surface meteorological variables and EC‐measured LE values were collected as the model inputs and validation data, respectively, at two sites with contrasting climatic conditions, namely, the subhumid Yucheng station in northern China and the arid Yingke site in northwestern China.…”