Especially for agriculture applications, MODIS resolution of 1 km is too coarse and may cause significant ET errors since a single pixel is generally larger than individual crop fields. During the last five years, a few methodologies have been proposed for the downscaling of ET from coarser resolution remotely sensed data using information provided by higher resolution imagery. Some studies [18; 19; 20] introduced disaggregation schemes that have been applied in thermal data fusion by examining the linear relationship between lower and higher resolution ET from multisensor datasets; other research [21; 22] made use of image fusion methods such as the spatial and temporal adaptive reflectance fusion model (STARFM) that was designed for surface reflectance blending. Some studies aimed at improving spatial details of coarse resolution ET based on fine resolution remotely sensed data [19; 21], while others aimed at generating both high spatial and temporal resolution ET [21; 22]. A comprehensive review by [PERSON] _et al._[23; 24] stated that although downscaling methods have been widely developed to retrieve high resolution air temperature, precipitation and soil moisture, the approaches specifically for ET downscaling have not been fully explored. The study reviewed 28 downscaling methods used in various disciplines including hydrology, meteorology, image processing, and geostatistics, and addressed the potential of introducing these methods for ET downscaling. The existing approaches developed specifically for ET downscaling mainly involved generation of ET from both MODIS and Landsat images, and then the relationship between the two ET products was evaluated to derive daily [18; 22], monthly [19] or seasonal [20] ET at Landsat resolution. This indicatesthat the accuracies of downscaled ET are dependent on both MODIS and Landsat ET calculations, which are mostly based on SEB models such as METRIC and SEBAL. Although SEB models have been widely used for Landsat-series data including Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper plus (ETM+) [3; 4; 5; 6; 7; 8], they have not been used for recently launched Landsat 8 data. Research has revealed that estimation of daily ET at pixel level could be very sensitive to LST, meteorological input parameters such as net radiation, air temperature, and reference ET calculated with these parameters [25; 26]; some parameters are satellite sensor specific, which have been well established for MODIS and Landsat TM/ETM+ sensors, but not for Landsat 8 Operational Land Imager (OLI) or Thermal Infrared Sensor (TIRS) sensors yet. Conventional SEB models that were tuned for Landsat TM/ETM+ sensors must be used with caution because the new sensors vary with their predecessors in that the spectral bands and the instrument performances are different [27]. In this paper, we introduced machine learning approaches for MODIS ET downscaling. Although machine learning algorithms have been successfully used in downscaling land parameters in other topics such as downscaling of General Circulation Models (GCMs) outputs [28] or latent heat flux estimates from Noah land surface model [29], and also have been used in downscaling satellite-derived soil moisture [30], they have not been applied in remotely sensed ET downscaling [23; 24]. The objective of this study is to develop a machine learning-based method in order to generate 8-day ET maps at 30 m resolution, based on MODIS 8-day ET product (MOD16A2) and Landsat 8 data, aiming to produce higher resolution ET that is consistent with the MODIS ET product. The method assumed that vegetation and surface wetness conditions do not change considerably within an 8-day interval.