## 3 Results
### Equity  
The 2009-2018 annual scheme ETIa CV was always highest for the L3 dataset (Table 5). The L3 annual scheme ETIa CV was higher for all schemes in all years on all but two occasions (Figure 4). All levels show similar inter-annual trends in CV; however, the magnitude of interannual variability was greater for the L3 dataset. The Koga was an exception: while L2 and L1 followed the same interannual trend, the L3 CV showed an opposite trend in years 2010-2011 and 2015-2018. Each level  
Figure 3: Average plot adequacy (relative ETa) in 2018 for the (upper) Wonji and (lower) Metehara schemes for L1, L2, and L3 resolution.  
Figure 2: Box and whisker plot showing annual mean, quartiles and range relative ET of the (**a**) Wonji and (**b**) Metehara schemes for the L1, L2, and L3 resolutions.  
in the Koga showed similar mean annual ETIa trends; however, the L1 and L2 SD decreased in those years where L3 SD either increased or remained similar. This may be a result of varying year-to-year agricultural practices that are better picked up by L3. Alternatively, it may be a result of processing issues: for example, a wetter year can increase cloud cover and therefore data gaps, particularly of L3, where the revisit time is already lower than for L1 and L2.  
Figure 5 shows the 2018 annual evapotranspiration in Zankalon and Metehara. The L3 dataset is capturing more spatial variability than the L1 and L2 datasets in both schemes. The plot size is significantly smaller in the Zankalon, and the L3 is not capturing plot ETIa differences as well as in the Metehara. This is shown by the plot clusters of similar values. In the Metehara, despite having similar annual CV in 2018, it is evident that L2 is picking up more spatial variations than L1.  
Figure 4: Annual intra-plot ETIa CV in each scheme: **(a)** Wonji, **(b)** Metehara, **(c)** ODN, **(d)** Koga, and **(e)** Zankalon.  
Figure 5: Average plot ETIa in 2018 for the (upper) Zankalon and (lower) Metehara schemes to compare three levels of spatial resolution (L1, L2, L3).  
Figure 6 shows the dekadal ETIa CV for each scheme from 2009-2018. In most cases, a greater dekadal variation in the L3 CV compared to L1 CV and L2 CV suggests that L3 captures seasonal variation better. The L3 dataset reports the largest intra-annual variation among three datasets, particularly in schemes with smaller plots. This suggests that the lower resolution is doing a better job at picking up variation in the larger plots as compared to smaller plots, i.e., L1 may be suitable at identifying variation or equity in the Wonji but not the Koga, where differences between L1 and L3 are most significant. The magnitude of differences in CVs between datasets was less for schemes with larger plots (e.g., Wonji and Metehara). However, the Wonji has the least consistent difference in the CV at a dekadal scale. The L2 mimics the L1 CV trend up to 2013, where it then deviates in four of the five schemes. This corresponds with the introduction of the PROBA-V sensor into the L2 dataset. The L3 dataset captured the magnitude of variability better than the L1 and L2 datasets. However, the L3 dataset may not have captured the dekadal-to-dekadal changes as well. This is noted by a frequently smoother L3 trend line as compared to L1 and L2.