## 3 Results
### Validation-Evaluation of the WaPOR Dataset  
The AGBPe is plotted against the farmer reported (or AGBPa) in Figure 8. The L3 dataset has the highest performance when compared to the in-situ data in terms of both coefficient of determination  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Product** & **Dataset** & **Wonji \({}^{*}\)** & **Metehara \({}^{*}\)** & **ODN** & **Koga** & **Zankalon** \\ \hline \multirow{3}{*}{AGBP or CWP (kg/m3)} & L3 & 3.7 (0.08) & 2.4 (0.10) & 1.4 (0.16) & 3.5 (0.09) & 3.0 (0.05) \\  & L2 & 4.3 (0.05) & 2.8 (0.07) & 1.5 (0.05) & 4.5 (0.09) & 3.0 (0.04) \\ \cline{1-1}  & L1 & 4.2 (0.05) & 2.7 (0.07) & 1.4 (0.05) & 4.4 (0.09) & 3.1 (0.03) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean annual scheme crop water productivity (CWP) (CWP denoted with \({}^{*}\)) or AGBP WP for 2009–2018 for all schemes (scheme CV in brackets).  
Figure 7: Average plot CWP (2018) in the (upper) Wonji, (middle) ODN, and (lower) Koga (lower) for the L1, L2 and L3 resolutions.  
(R\({}^{2}\)) and root mean square error (RMSE). The L3, L2, and L1 R\({}^{2}\) are 0.7, 0.4, and 0.5, respectively, and the L3, L2, and L1 RMSE are 33.9 ton/ha, 31.11 ton/ha, and 48.2 ton/ha, respectively, which equates to a normalized RMSE (NRMSE) of 22.5%, 21.8%, and 33.8%.  
The AGBP\({}_{\text{a}}\) shows more variation between plots and years, with an AGBP\({}_{\text{a}}\) SD of 47 ton/ha compared to an AGBP\({}_{\text{e}}\) of 26 ton/ha. The AGBP\({}_{\text{e}}\) is comparable to the average global biomass yield which is 69.8 ton/ha [57]. FAO WATER [57] reports that the cane yield varies from 50 ton/ha to 150 ton/ha depending on the variety and ratooning stages, which suggests that the WaPOR-derived values are within the reported range, while the farmer estimates are on the high side. The harvested biomass is related to the growing period. The average total growing period of sugarcane in the Awash L3 area is 585 days with a range of 305-1037 days or 0.8-2.8 years. WaPOR give reasonable results for sugarcane, but a gap exists in linking biomass to yield in remote sensing-based estimates [11]. Often, a priori knowledge of harvest index is the most useful way to link biomass and yield.  
ETIa was compared to the EC in the Zankalon irrigation scheme. The 10-day daily average ETa-EC and WaPOR ETIa for all three spatial resolutions at the EC site are shown in Figure 9. The L1 and L2 ETIa show high consistency with each other. The L3 ETIa consistently sits between the ETa-EC and the L1 and L2 ETIa. All levels capture the overall ETa-EC seasonal trends. The L3 data shows a slightly lower R\({}^{2}\) (L3 = 0.36, L2 = 0.60, and L1 = 0.61) and noticeably lower bias (L3 = 1.06 mm/day, L2 = 1.7 mm/day, and L1 = 1.7 mm/day) and a lower RMSE (L3 = 1.0 mm/day, L2 = 2.2 mm/day, and L1 = 2.2 mm/day) when compared with the ETa-EC. The higher R\({}^{2}\) associated with the L1 and L2 ETIa reflects their ability to capture the temporal fluctuations of ETa-EC better than L3 ETIa. An example of this is at dekad 1117 or 2011-17, where L1 and L2 ETIa capture the ETa-EC dip, whereas L3 ETIa stays flat. These findings are consistent with other validations that compare WaPOR L1 ETIa data with EC data in other agricultural fields in both the Nile Delta and other dry, arid regions [55]. It should be noted that the same study found lower suitability of WaPOR data in very wet, non-agricultural areas.  
Figure 8: WaPOR-derived AGBPe for L3 (left), L2 (middle), and L1 (right) compared against AGBPa.  
Figure 9: WaPOR dekadal (**a**) L3, (**b**) L2 and (**c**) L1 ETIa (mm/day) compared to in-situ dekadal ETa (mm/day).