## 5 Discussion  
### Novelty, Performances and Opportunities  
In this paper, machine learning approaches were first introduced for ET downscaling from coarse resolution to fine resolution by combining MODIS 8-day ET product and Landsat 8-derived eleven indicators. The rRMSEs of RF models were less than 20% and within the error range of the MODIS ET product (rRMSE around 25%), indicating the reliability of the downscaling model. Among the eleven indicators, vegetation greenness-related indices such as NDVI/EVI/SAVI were the most important variables used in the machine learning modeling. In Penman-Monteith model that was used to generate MOD16 ET products, remotely-sensed LAI is an important input parameter for quantification of wet canopy surface evaporation and plant transpiration [33]. As LAI and NDVI/EVI/SAVI are highly correlated, it is reasonable that these variables were highly important. These indices have also long been recognized as important indicators for ET estimation, especially at crop areas that were dominant land cover types in study Sites 1, 2 and 4. NDVI is on average the most important indicator among the VIs. Although NDVI is known to have saturation issues and it can be influenced by soil background, the use of EVI and other vegetation greenness related indices such as SAVI could help to avoid these issues and ensures the reliability of the models. Vegetation moisture-based indicators such as NDIIb7 and NDWI were also important because these variables describe soil moisture and leaf water content, higher leaf moisture results in greater amount of water transpired from leaves, especially during growing seasons. Interestingly, LST-related indicators were less important. Unlike SEB models that rely on LST to derive ET, the Penman-Monteith model did not use remotely sensed LST as an input. On the other hand, LST derived from Landsat 8 imagery only represents the instant thermal conditions at the image acquisition date/time. Although our investigation showed that the instant Landsat-derived LST had high correlation with the MODIS 8-day average LST product (MOD11A2) (\(\rho\) > 0.8 and mean R\({}^{2}\) = 0.83), the surface temperature can vary considerably during the 8-day period. Therefore, it is not surprising that LST has less of a contribution to the modeling of 8-day aggregated ET. Our results showed that the downscaled ET is overall consistent with MODIS ET product, and it demonstrates detailed spatial variation and temporal trend at 30 m resolution. The downscaling approach assumes that Landsat-derived indicators characterize vegetation conditions during each 8-day period. The overall consistency between downscaled and MODIS ET proved that this assumption is reasonable even when the resolution of MODIS ET product is over 1000 times (33 \(\times\) 33) coarser than Landsat data. Greater variation of the downscaled ET over cropland was observed as crop ET varies significantly with irrigation and growth. This also demonstrated the capability of the product to represent the spatial and temporal heterogeneity of ET. The ET estimates have better agreement with _in situ_ measurements, especially at Site 2 where ET heterogeneity is high within 1 km grids. Because the downscaling models rely on the MODIS ET estimation, the accuracy of the MODIS product highly impacts the finer-resolution estimation. With the improved accuracy of coarse resolution global or regional ET products, it is expected that our downscaling approach can achieve better performance in various regions with different climate and environmental characteristics.