## 5 Conclusions  
There is a growing interest in improving agricultural water productivity due to the decline in water resources while meeting the increasing global demand for food using limited fresh water. The aim is to increase crop yields using less water, thus reducing the water footprint per unit of agricultural output. Accordingly, the research demonstrated the effectiveness of predicting the green water footprint of sugarcane crops in White Nile State, Sudan, to reach the best sustainable water management approach for the crop during the period 2001-2020 through the use of machine learning algorithms and remote sensing indices. No significant advantage was observed when using hybrid models compared to single models, especially when dealing with the estimation of coefficient of determination (R2). However, there was a significant difference in terms of RMSE, NBE, and MAE as performance indicators for GWFP prediction effectiveness, where the hybrid models had less errors compared to individual models. The evaluation indices showed that Sc3 is the worst-case scenario, associated with remote sensing indices, while Sc1 (all parameters) and Sc4 (Pe\({}_{\text{eff}}\), Tmax, Tmin, and SA) were identified as the best-case scenarios, with convergence of the evaluation results. The choice between them depends on the availability of different criteria such as climatic parameters, crop parameters, and remote sensing indices. In cases of a lack of capabilities and limited data, the study recommends choosing Sc4. The suboptimal functionality of Sc3 can be remedied by employing higher-resolution remote sensing datasets, increasing the temporal resolution, and integrating supplementary remote sensing indices, such as NDTI, GPP, and Soil Moisture along with LST to facilitate the understanding of the hydrological dynamics pertinent to sugarcane cultivation. Conceptualization, R.H.A.-T., M.E.A., A.A.-R.S.A., A.S. and A.M.; methodology, R.H.A.-T., M.E.A. and A.M.; software, R.H.A.-T., M.E.A. and A.M.; validation, R.H.A.-T., M.E.A., A.A.-R.S.A. and A.M.; formal analysis, R.H.A.-T., M.E.A., A.A.-R.S.A. and A.M.; investigation, A.A.-R.S.A., A.S., S.A.H., E.A.H. and A.M.; resources, A.A.-R.S.A., M.M.H., A.S., S.A.H. and E.A.H. ; data curation, E.A.H. and A.S.; writing--original draft preparation, R.H.A.-T. and M.E.A. ; writing--review and editing, R.H.A.-T., M.E.A., A.A.-R.S.A., M.M.H., A.S., S.A.H., E.A.H. and A.M.; project administration and funding acquisition, M.E.A., A.S. and A.M. All authors have read and agreed to the published version of the manuscript. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors declare no conflicts of interest. The authors declare they have no financial interests. The authors declare no conflicts of interest. The authors declare they have no financial interests. List of Abbreviations Support vector regression (SVR); random forest (RF); extreme gradient boost (XGB) and artificial neural network (ANN); blue water footprint (BWF); Nash-Sutcliffe model efficiency coefficient (NSE); root mean square error (RMSE); the mean absolute error (MAE); the mean bias error (MBE); the coefficient of determination (R\({}^{2}\)); mean average percentage error (MAPE); uncertainty with 95% (U\({}_{\text{S}}\)). References References
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