## 3 Results
### Error Analysis from the TSEB Algorithms
#### 3.1.1 TSEB Data Analysis Combined (LIRF and IIC 2020-2021)
When considering the \(\mathrm{TSEB_{par}}\) algorithm, there was an underestimation of hourly maize \(\mathrm{ET_{a}}\) when using UAS (\(-3\%\)), MSR5 (\(-3\%\)), Sentinel-2 (\(-3\%\)), and Landsat-8 TIRS (\(-5\%\)). The slight overestimations of hourly maize \(\mathrm{ET_{a}}\) for CubeSat (3 m) and Landsat-8 IRT (30 m) were 2% and 4%, respectively. Sentinel-2 (10 m) and Planet CubeSat (3 m) had similar performances since the NRMSE for \(\mathrm{TSEB_{par}}\) and \(\mathrm{TSEB_{ser}}\) had a 6% difference between the two high-resolution spaceborne platforms. The underestimation trend shows a concurrent bias in the \(\mathrm{TSEB_{ser}}\) algorithm's ability to predict actual \(\mathrm{ET_{a}}\) rates across various platforms. The largest underestimation by Landsat-8 TIRS could be attributed to its thermal infrared sensor's spatial resolution and spectral characteristics, which may not fully capture the heterogeneous surface temperature and moisture conditions within agricultural fields. This result suggests that there are limitations, inherent to this sensor, in accurately detecting surface temperatures for relatively small maize fields displaying some surface heterogeneity. The slight overestimations noted with CubeSat and Landsat-8 IRT suggest that these sensors, at their respective resolutions, might capture more of the variance in surface temperatures and moisture levels than the TIRS sensor. These discrepancies underscore the importance of the spectral and spatial resolution's role in capturing field heterogeneity. The comparative performance of Sentinel-2 and Planet CubeSat, with a minimal difference in NRMSE for both \(\mathrm{TSEB}\) algorithms, indicates that high-resolution spaceborne platforms are capable of providing reliable \(\mathrm{ET_{a}}\) estimates, as compared to Landsat-8. The similar performance of these two sensors, despite their different operational designs and data characteristics, suggests that high-spatial-resolution spaceborne sensors have better results for small agricultural fields, compared to coarse satellite pixel resolutions. It also highlights the critical role of spatial resolution in capturing the detailed variability of agricultural landscapes, which is essential for promoting conditions to enhance sustainable irrigation water management in cropland fields. In addition, the trend of underestimation observed with the \(\mathrm{TSEB_{par}}\) algorithm across UAS, MSR5, Sentinel-2, and Landsat-8 TIRS (with relatively minor variations) suggests the need for algorithmic adjustments or enhancements to further improve \(\mathrm{ET_{a}}\) estimates. The \(\mathrm{TSEB}\) RS of the \(\mathrm{ET_{a}}\) models were originally developed and validated using airborne data at a 5 m spatial resolution. Given the large number of input parameters in the \(\mathrm{TSEB}\) RS of \(\mathrm{ET_{a}}\), some of the uncertainty in the final maize \(\mathrm{ET_{a}}\) estimation might be due to sub-models that do not have locally calibrated parameters for the semi-arid region across different climate conditions. Nevertheless, the performance of the \(\mathrm{ET_{a}}\) algorithm is acceptable. In our study, the range of accuracy of both \(\mathrm{TSEB}\) approaches for maize \(\mathrm{ET_{a}}\) (10-30%) was consistent with previously reported values in the current existing literature for other row crops such as vineyards when using sUAS imagery aggregated to different local spatial scales (e.g., 4 m, 7 m, 14 m, and 30 m) [27] and soybeans and maize when using Landsat-7 (30 m) and UAS (6 m) imagery spatial scales [68]. The similar results obtained between maize \(\mathrm{ET_{a}}\) observed and estimated values from previous studies using different spatial and spectral resolution RS sensors reinforces the reliability of selecting finer RS spatial resolution sensors to use as input for predicting crop \(\mathrm{ET_{a}}\) with a \(\mathrm{TSEB}\) RS of the \(\mathrm{ET_{a}}\) algorithm to advance sustainable agricultural water management. Both Ref. [27] and Ref.