Evapotranspiration Estimation in the Arab Region: Methodological Advances and Multi-Sensor Integration Framework

Evapotranspiration (ET) estimation is crucial for sustainable water resource management in arid and semi-arid regions, particularly in the Arab world, where water scarcity remains a significant challenge. The objectives of this study were to map dominant ET estimation techniques and their geographic distribution, demonstrate fusion-based ET estimation under data-scarce conditions, and examine their alignment with climate change and food security priorities. The study reviewed 1279 ET-related articles indexed in the Web of Science, highlighting methodological trends, regional disparities, and the emergence of data-driven techniques. The results showed that traditional methods—primarily the Penman-Monteith model—dominate nearly 70% of the literature. In contrast, machine learning (ML), remote sensing (RS), and artificial intelligence (AI) collectively account for approximately 30%, with hybrid fusion frameworks appearing in only 2% of studies. ML applications are concentrated in Morocco, Egypt, and Iraq, while 50% of Arab countries lack any ML or AI-based research on energy transition (ET). Complementing the bibliometric analysis, this study demonstrates the practical potential of ML-based ET fusion using Landsat and the FAO Water Productivity (WaPOR) data within Saudi Arabia. A random forest model outperformed traditional averaging, reducing the mean absolute error (MAE) to 215.08 mm/year and the root mean square error (RMSE) to 531.34 mm/year, with a Pearson correlation coefficient of 0.86. The findings advocate for greater support and regional collaboration to advance ET monitoring and integrate ML-based modelling into climate resilience frameworks.