# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 4 Discussion
### Sharpening and Disaggregation  
As was previously mentioned, thermal sharpening relates empirically or semi-empirically coarse resolution surface temperature with fine resolution multispectral and other ancillary data. This technique could be a sound alternative to the lack high resolution thermal imagery for operational activities. However, previous studies in thermal sharpening have reported some uncertainties when compared to actual \(T_{rad}\) temperatures, with errors ranging up to 3.5 K ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON], 2017). Therefore, for some applications requiring ET estimates at higher accuracy (i.e., precision agriculture), sharpening might not be considered as a suitable substitute of \(T_{rad}\) but complementary to it, such as in the fusion approach by [PERSON] et al. (2019). In order to reduce flux retrieval errors with sharpened \(T_{rad}\) inputs, we also tested a flux disaggregation method ([PERSON] et al., 2018; [PERSON] et al., 2020). Our results listed in Tables 3-7 show that disTSEB model, i.e., coarse S3 TSEB-PT fluxes disaggregated with fluxes derived with TSEB-PT and fine resolution sharpened \(T_{rad}\), yielded only modest improvement (5 W m\({}^{-2}\) reduction in RMSE in case of H and only 1 W m\({}^{-2}\) in case of \(\lambda\)E) to the TSEB-PT model, i.e., running TSEB directly on the sharpened \(T_{rad}\) imagery. The one exception was at the savanna sites (see Table 5) where using disaggregation reduced the errors in H by around 12% and errors in \(\lambda E\) by around 10%. However, previous studies have shown the robustness of this approach to overcome limitations of the likely less reliable fine resolution \(T_{rad}\) images [4; 93; 94]. Furthermore, coarse input data must be produced beforehand for thermal sharpening and hence it is readily available for running the models at coarse resolution, which indeed is computationally inexpensive given the much lower number of pixels within a scene. Therefore, flux disaggregation would still be recommended when running TSEB-PT with sharpened temperatures. In addition, the sharpening of a coarse resolution \(T_{rad}\) image using fine resolution images acquired on different days, with a maximum of 10 days offset, might lead to additional uncertainties. This is caused by the fact that some changes in either land cover properties, (e.g., vegetation growth, harvests, fires) or moisture conditions (e.g., rainfall or irrigation) might happen between the Sentinel 2 and 3 acquisitions. Figure 2 shows that at a general level (all validation sites taken together) this does not appear to be a significant issue as the error does not increase as the day offset between thermal and shortwave acquisitions gets larger. Particularly relevant in this analysis is H since it is the energy component that is directly related to \(T_{rad}\), and hence more prone to errors in sharpening. However, more studies should be conducted to look at the effect of the day offset in particular situations, e.g., in crops during senescence or with localized irrigation patterns. It might be also worth to investigate using high-resolution radar data (e.g., from Sentinel-1), which is sensitive to soil moisture, in the thermal data sharpening approach [95]. Furthermore, the Landsat family of satellites could also be utilised during the sharpening since they acquire thermal data at around 100 m spatial resolution although at 8 days (two satellites) to 16 days (single satellite) temporal resolution. Using observations from those satellites would both increase the temporal density of the high-resolution data but also capture physical processes and properties which are not reflected in the shortwave data, such as near soil surface soil moisture and soil evaporative efficiency. Finally, some studies have reported larger errors than in this study, but they were using coarser resolution imagery [43].