Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches  
[PERSON]  
1 Beijing Laboratory of Water Security, Base of the State Key Laboratory of Urban Environment Process & Digital Modeling, Capital Normal University, Beijing 100089, China; [EMAIL_ADDRESS] (Y.K. ); 1  
[PERSON]  
2 School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea; [EMAIL_ADDRESS]  
[PERSON]  
1 Beijing Laboratory of Water Security, Base of the State Key Laboratory of Urban Environment Process & Digital Modeling, Capital Normal University, Beijing 100089, China; [EMAIL_ADDRESS] (Y.K. ); 1  
Footnote 1: email: {[EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS], seonyoung.park@unist  
water requirement evaluation, and agricultural water allocation and management [1]. At regional to global scales, quantifying ET is essential for mesoscale and global circulation modeling as it affects water and energy cycles between land, ocean, and atmosphere [2]. As satellite remote sensing techniques can provide viable approaches for land surface process monitoring over large areas, various approaches have been developed for ET estimation from moderate to low resolutions based on remotely sensed data during the last few decades. These approaches generally include empirical/statistical methods that link land surface temperature (LST) and/or vegetation indices (VI) with _in situ_ ET measurements, and more physically-based models including surface energy balance (SEB) models such as Surface Energy Balance Algorithm for Land (SEBAL) [3; 4], Mapping EvapoTRanspiration with Internalized Calibration (METRIC) [5], Atmosphere-Land Exchange Inverse (ALEXI) model [6], and Surface Energy Balance System (SEBS) models [7], and Penman-Monteith models [8]. The Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration products (MOD16) are the first 1 km land surface ET dataset for the 109,030,000 km\({}^{2}\) global vegetated land surface at 8-day, monthly, and annual intervals [9]. The products were developed using a Penman-Monteith approach driven by MODIS derived surface albedo, land cover, Leaf Area Index (LAI), the Fraction of absorbed Photosynthetically Active Radiation (FPAR), and daily surface meteorological parameters [9; 10]. Various studies have evaluated the products over different regions and shown that they agree well with the other satellite or model-based ET datasets, such as the EUropean Organisation for the Exploitation of METeorological SATellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF), ET product from Meteosat Second Generation (MSG) [11] and the European Centre for Medium-Range Weather Forecasts (ECMWF) (GLDAS) ET images [12]. The evaluation against ground measurements showed that the errors of MODIS ET products were around 0.31 mm/day, or 24% of the ET measured from 46 AmeriFlux eddy covariance flux towers [10]. It was also reported that the products had better accuracy in temperate and fully humid climates, but underestimated ET in semiarid climates compared to 15 CarboEurope eddy covariance tower sites [11]. Currently, the MODIS ET products have been used in many applications such as terrestrial gross primary productivity (GPP) estimation [13], drought monitoring [14], groundwater recharge quantification [15; 16], and watershed runoff simulation [17]. Although MODIS ET products have provided a great opportunity for routine monitoring of land surface conditions at mesoscale, the 1 km ET estimates tend to be inaccurate and are not applicable at field, local, or basin scale because of a high level of spatial heterogeneity of land cover within a pixel.