## 1 Introduction  
Evapotranspiration (ET) is an important climatic factor besides solar radiation and atmospheric circulation, which controls the energy and mass exchange between the earth's ecosystem and the atmosphere, thus affecting the water balance of the ecosystem [1; 2; 3]. The accurate estimation of ET minute frequency at a regional scale is crucial to better understand detailed surface hydrological processes and provide more efficient catchment water management [4; 5]. Traditionally ET is estimated for points or patches on the land surface but spatial heterogeneity in land surface characteristics precludes robust upscaling to the regional scale [6; 7; 8]. However, surface energy balance models using remote sensing data (specifically terrain, soil humidity, air and land surface temperature) enables accurate ET estimates at the regional scale [6; 9; 10; 11]. For example, in the Haihe River Basin of China, ground verification studies suggested the root mean square error associated with estimates of ET was approximately 0.32 mm/3 h [12]. Further, land-atmosphere interactions in California Delta of USA,showed that the root mean square error was within 0.98 mm/day when calculating the one-day ET value [13]. Accurate estimation of evapotranspiration in irrigation areas is the basis for optimal management of water resources and water-saving irrigation. Remote sensing evapotranspiration model is of great significance for improving irrigation water use efficiency and simulating crop yield [14; 15]. At present, remote sensing evapotranspiration model is mainly combined with meteorological data to evaluate irrigation water demand, irrigation efficiency and crop water monitoring in water resources management in irrigation areas [16; 17; 18]. In the above research, the input data with high temporal resolution played a very important role in the fine description of the above process. At present, the precipitation and temperature at minute frequency can already be obtained through the measured data of meteorological stations, and then the spatial distribution of temperature and precipitation in the irrigation area can be obtained through spatial interpolation, but the evapotranspiration is \"cumulative data\", which is different from \"instantaneous data\" such as temperature and precipitation, and the change at minute frequency is difficult to measure[19; 20; 21; 22]. At present, most of the data measured by meteorological stations are the evaporation measured by the evaporation pan, not the evapotranspiration. Although lysimeter, scintillator, flux tower and other methods can be used to measure the evapotranspiration with time interval of minutes, the cost of measuring instruments is huge and it is difficult to realize widespread arrangement like meteorological stations [21; 23]. Therefore, using remote sensing data to simulate evapotranspiration at minute frequency has very significant potential applications. However, limited by the current temporal resolution of satellites, the minimum time interval for simulating evapotranspiration using remote sensing data is hourly. Moreover, estimating ET with 1 h intervals is at the expense of expanding the spatial resolution of ET, for example ([PERSON] et al., 2015), using FY meteorological satellite images simulated the evapotranspiration of Tibetan Plateau at 1 h interval, but the spatial resolution of ET was 1000 m, and the result could not accurately reflect the spatial diversity of ET [24]. Ideally, to obtain accurate ET at a regional scale, satellite data with both high temporal (i.e., hourly) and spatial (i.e., \(<\)100 m) resolution would be used. However, current satellite imagery either has a high temporal resolution and low spatial resolution (e.g., the time resolution of moderate-resolution imaging spectroradiometer is less than half a day, but its spatial resolution is 250 m to 1000 m.) or has a low temporal resolution and high spatial resolution (e.g., the spatial resolution of Landsat 8 is 15 m to 100 m, but its time resolution is 16 days).