## 4 Discussion  
This study shows the influence of spatial resolution on irrigation performance indicators, relevant to different plot sizes and schemes. All resolutions captured the seasonal trends. This was evident when observing indicators over time, i.e., equity (e.g., Figure 6). The higher resolution showed a higher accuracy, in terms of R\({}^{2}\) and RMSE, for AGBP compared to in-situ data. The relative AGBP errors (21%, 28%, and 28% for L3, L2, and L1, respectively) is in the upper 50 th percentile of reported error ranges for non-parametrized remote-sensing-based estimates and is within the ranges of error associated with farmer reported yields [11]. Some users may prefer a locally parameterized model to achieve dataset accuracy. The L3 ETIa had lower bias: the R\({}^{2}\) was lower than the L2 or L1 datasets when compared to EC data. While the L1 and L2 data had better R\({}^{2}\), they had a higher bias. Further, the L1 and L2 data have a higher temporal resolution in terms of spectral input, and when comparing to daily flux data, the temporal gaps of the L3 data may be influencing the accuracy more than the gain in spatial resolution. A study on a 22-ha olive farm showed that datasets with resolution coarser than L3 will result in great discrepancies as compared to actual evapotranspiration values due to the aggregation of non-linear components and to the inclusion of non-agricultural areas in such aggregation [27]. Similarly, a study in wheat found that the 30 m NDVI dataset, as compared to 250 m and 1 km NDVI datasets, provided the most accurate yield estimates [20]. This is absolute error, not relative error in space and time, looking at variation. The level consistency of ETIa and AGBP does not seem related to the plot size at annual, scheme level. However, the L3 dataset, at the scheme level, was able to capture the spatial variation better and provide more information on scheme level equity. Validation showed that although differences in datasets were evident, the spatial and temporal trends were consistent. However, the magnitude of seasonal and spatial variability varied between datasets and that the L1 and L2 may be in some cases overestimating the ETIa. However, it should be noted that the schemes with smaller plots (i.e., Zankalon and Koga) also had the highest crop diversity. This will drive more variation in the field yields and water consumption and also in satellite interpretation, as they are less homogenous. However, this is considered to reflect reality, as small holder agriculture is often associated with more diversity, for example in South Africa [58]. Adequacy was captured well by all resolutions at an annual, whole of scheme level in the Wonji and the Metehara. While the L3 data showed higher relative ET CV, each level was able to capture the inter-annual trends. It is suggested that all resolutions are suitable to assess adequacy at interannual or inter-scheme level for the schemes assessed. Inter-plot spatial variability of adequacy was captured well by all in the Worji and the Metehara, however, the L3 dataset captured variability the best. It is suggested that all resolutions are suitable to assess adequacy at interannual scheme level for the schemes assessed, though L3 is preferred for the scheme with smaller plots, i.e., Koga and Zankalon. This research is limited to assessing adequacy at annual scale, and in practice adequacy should be assessed at various stages of critical crop growth. Further research should consider the impact of resolution on a shorter timescale. Inter-annual equity was captured reasonably well by all levels in the Wonji, Metehara, ODN and Zankalan. However, L3 was distinctly different to L1 and L2 in the Koga, suggesting that all levels are suitable for larger plots with mixed results for smaller plots. This may be influenced by the heterogenous landscape in the Koga, which is mixed with irrigated pixels [27]. L1 and L2 are not suitable to assess equity at plot-to-plot scale in the Koga and Zankalon.