Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m², whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m². The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.

Studying the effect of cloud cover on the temporal upscaling of instantaneous evapotranspiration (ET) is significant in the effort toward a more accurate and widely applied upscaling method to obtain the exact ET on a daily or longer time scale, thereby benefiting the practical applications. In this article, the authors concentrated on the effects of cloud cover in different amounts and for varying time durations, with three commonly used upscaling approaches including the constant evaporative fraction (EF) method, the constant reference evaporative fraction (${\mathrm{EF}}_r$ ) method, and the constant global solar radiation ($R_g$ ) method. Transient cloud cover and persistent cloud cover were defined according to the occurrence time, namely, the cloud that appeared 1 h before or after the upscaling moment and the cloud lasting the whole day except during the upscaling time, respectively. The different cloud cover amounts were indicated by the different losses of downwelling shortwave irradiance. The instantaneous fluxes were simulated from the atmosphere-land exchange (ALEX) model, which was driven by the meteorology measurements at the Yucheng station in China. The results showed that (1) the cloud caused the deterioration of the underestimation or overestimation of the daily ET upscaling in comparison with the results of clear days. Specifically, the persistent cloud cover had a more significant effect on the three upscaling methods; for the transient cloud cover, the upscaling results had larger deviations when the cloud appeared before the upscaling moments than when it appeared after them; (2) the effects on the upscaling factors and the upscaling results both increased proportionally with the growth of the cloud cover; and (3) the constant EF_r method performed best for both clear and cloudy situations, with a minimal bias less than $4.7\mathrm{W}/{\mathrm{m}}^2$ (5.5%) and a root-mean-square error (RMSE) less than $8.9\mathrm{W}/{\mathrm{m}}^2$ (20.6%); the EF method was most severely affected, with a bias up to $24.1\mathrm{W}/{\mathrm{m}}^2$ (28.3%) and an RMSE up to $24.9\mathrm{W}/{\mathrm{m}}^2$ (57.7%); the $R_g$ method had an intermediate accuracy with a bias less than $20.9\mathrm{W}/{\mathrm{m}}^2$ (24.6%) and an RMSE less than $20.3\mathrm{W}/{\mathrm{m}}^2$ (47.1%); and (4) all three approaches were influenced most significantly around noontime.