## 5 Conclusions  
This research was conducted in a semi-arid climate area, in maize fields irrigated with sub-surface drip and furrow irrigation systems, at two research sites in northern Colorado, USA. We aim to investigate the performance of two TSEB remote sensing of ET\({}_{\text{a}}\) algorithms when using input data from different (multiscale) remote sensing sensors/platforms. The hypothesis was that the accuracy of RS of ET\({}_{\text{a}}\) estimation depended on both the pixel spatial and spectral/radiometric resolutions of the multispectral data used and on the specific parameters within the RS of the ET\({}_{\text{a}}\) algorithms. The primary conclusion is that, for both TSEB approaches evaluated (TSEB\({}_{\text{par}}\) and TSEB\({}_{\text{ser}}\)), the best remote-sensing-based surface reflectance and temperature data for predicting maize hourly ET\({}_{\text{a}}\) were those from the handheld MSR5 radiometer. The second-best RS data were multispectral surface reflectance images from the UAS, Planet, and Sentinel-2 RS platforms (plus surface temperature from stationary IRT sensors). However, using RS data from Landsat (optical and TIRS) resulted in larger ET\({}_{\text{a}}\) estimation errors. While it is possible to estimate crop ET\({}_{\text{a}}\) using various remote sensing platforms, selecting the most suitable RS data for a given ET\({}_{\text{a}}\) algorithm has the potential to significantly enhance irrigation water management by using more accurate ET\({}_{\text{a}}\) estimates. In this study, it was found that the accuracy of the ET\({}_{\text{a}}\) predictions was not the same across the different remote sensing sensors. The use of the appropriate remote sensing data (i.e., MSR5) with the TSEB remote sensing of ET\({}_{\text{a}}\) algorithms, to optimize maize irrigation scheduling, presents a significant contribution toward advancing sustainability in irrigated agriculture. The combination of MSR5 multispectral and thermal data to determine the contributions of soil and vegetation components to ET\({}_{\text{a}}\) can offer a more accurate understanding of water consumption in cropland ecosystems, compared to the most common Landsat data use. To improve the effectiveness of sustainable solutions using remote sensing data, future research in sustainability should focus on refining the TSEB algorithms, integrating diverse datasets within the same data analysis context, and addressing challenges associated with scaling from local (e.g., farms) to regional (e.g., irrigation districts and watersheds) levels. This study highlights the need for further research aimed at improving the data quality of sub-optimal remote sensing platforms/sensors when only those data are available. It is critical to develop imagery calibration protocols to improve the quality of the remote sensing data needed for the prediction of crop ET\({}_{\text{a}}\) under different surface and climate conditions. This would help enable the use of the most desirable remote sensing data with high accuracy for effective irrigation water management. Also, we recognize the need for more research including a wider range of commercial crops to strengthen the analysis of how the TSEB approaches perform when estimating crop ET\({}_{\text{a}}\) for other crop types. Additionally, the role of advanced spatial data analysis and machine learning algorithms in processing and interpreting RS data could be an alternative to explore to improve the quality of the RS data for the sensors that did not perform better than the MSR5. Thesetechnologies can provide a possible framework to address complex patterns and relationships within large imagery datasets, facilitating more applied and predictive approaches to crop water use and stress levels. By leveraging the computational capabilities of these artificial intelligence models, researchers and practitioners can refine the application of TSEB RS algorithms and determine irrigation scheduling practices to meet the water requirements of specific crops under local field conditions.