## 3 Results  
### Error Analysis from the TSEB Algorithms  
#### 3.1.1 TSEB Data Analysis Combined (LIRF and IIC 2020-2021)  
The results from TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\) of the ET\({}_{\text{a}}\) algorithms indicated that data used from the MSR5 (1 m) RS sensor resulted in the best performance when estimating hourly maize ET\({}_{\text{a}}\) compared to the use of other RS platforms' data, since the smallest NRMSE was 11% and 14%, respectively, for both TSEB algorithms used. The overall errors in predicting hourly maize ET\({}_{\text{a}}\) using MSR5 (1 m) data were \(-0.02\) (\(-3\%\)) \(\pm\) 0.07 (11%) mm/h and \(-0.02\) (\(-4\%\)) \(\pm\) 0.09 (14%) mm/h for the TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\), respectively. The errors associated with Landsat-8 (30 m) TIRS data used in the RS of ET\({}_{\text{a}}\) estimation were \(-0.03\) (\(-5\%\)) \(\pm\) 0.16 (29%) mm/h and \(-0.07\) (\(-13\%\)) \(\pm\) 0.15 (29%) mm/h, while reductions of 31% and 34% in NRMSE were observed when the TIRS was replaced by the ground-based IRT data for the TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\), respectively (Figures 8 and 9). The NRMSE errors in estimating H using Landsat-8 multispectral visible and NIR and TIRS data were greater than 30% for the TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\), while the NRMSE from the remaining spaceborne, airborne, and proximal platforms were within 20 to 30% (Figures 10 and 11). Using proximal IRT data to estimate maize ET\({}_{\text{a}}\) with Landsat-8 (30 m), surface reflectance improved the estimation of hourly maize ET\({}_{\text{a}}\), with reduced model performance errors of 0.02 (4%) \(\pm\) 0.10 (19%) mm/h and \(-0.04\) (\(-8\%\)) \(\pm\) 0.11 (20%) mm/h for the TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\), respectively. Most of the improvements in hourly maize ET\({}_{\text{a}}\) using TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\) were due to better H predictions than the accuracy on the estimation of R\({}_{\rm n}\) and G fluxes. For the TSEB\({}_{\rm par}\), the performance of the R\({}_{\rm n}\) model did not significantly change when using TIRS and IRT data since the NRMSE was 13% and 14%, respectively. The TSEB G model NRMSE ranged from 20 to 45% across all the RS platforms in this study, which had a fair to poor performance since NRMSE > 20%. However, since the magnitude of G is much smaller than R\({}_{\rm n}\) and H, the error propagation in LE estimates due to G is also smaller than the other two SEB input fluxes. Furthermore, the original TSEB G model assumes that G varies linearly with the R\({}_{\rm n}\) flux associated with bare soil. However, the work of [18] indicated that G and bare soil R\({}_{\rm n}\) have a time-phased difference between the two fluxes that requires a specific local calibration. Figure 8: Scatter plots (1:1 line) and error analysis results regarding the TSEB parallel (TSEB\({}_{\rm par}\)) maize hourly ET\({}_{\rm a}\) modeling results for the combined LIRF and IIC 2020–2021 data. The sample size (\(n\)) of each platform is indicated in the figure. Figure 9: Scatter plots (1:1 line) and error analysis results regarding the TSEB series (TSEB\({}_{\rm ser}\)) maize hourly ET\({}_{\rm a}\) modeling results for the combined LIRF and IIC 2020–2021 data. The sample size (\(n\)) of each platform is indicated in the figure. Figure 11: Error analysis of the SEB fluxes using the TSEB parallel (TSEB\({}_{\text{par}}\)) algorithm and LIRF and ILC 2020–2021 data combined. Figure 10: Error analysis of the SEB fluxes using the TSEB series (TSEB\({}_{\text{ser}}\)) algorithm and LIRF and ILC 2020–2021 data combined. A consistent trend of maize \(\mathrm{ET_{a}}\) underestimation was present for all spaceborne platforms/sensors (data) when considering the \(\mathrm{TSEB_{ser}}\) RS of the \(\mathrm{ET_{a}}\) algorithm. The \(\mathrm{TSEB_{ser}}\) hourly maize \(\mathrm{ET_{a}}\) underestimation range was within \(-3\%\) (CubeSat) to \(-13\%\) (Landsat-8 TIRS).