Aim of the study:
The research problem of this study is to model and track the surface water temperature changes in the Euphrates River at Al-Hindiya Barrage, Iraq, by relying on in-situ measurements and Landsat 8/9 satellite (2018–2024)

Material and methods:
This study employed GIS and statistical methods to process field observations and Landsat satellite thermal infrared images to determine average monthly surface water temperatures. The Landsat dataset had about 4.8% missing data points, while on-site measurements had approximately 1.2% missing values. Missing data were reconstructed using IBM SPSS Statistics (Version 26) through mean compensation, substituting missing values with the arithmetic mean of the same month across available years. This approach ensured a stable and reliable time series, enabling modeling of the relationship between on-site and satellite-measured temperatures via simple linear regression. Linear regression performance at Hindiya Barrage was assessed using the Nash–Sutcliffe efficiency coefficient.

Results and conclusions:
Results show that in-situ water temperatures produced significant monthly temperature variation (actual water temperatures ranged from a minimum of 11.8°C to a maximum of 32.9°C), and satellite data recorded even wider temperatures ranging from a minimum of 12.0°C to a maximum of 40.8°C. A highly significant correlation (R² = 0.93) between datasets shows that Landsat-derived water temperature values. NSE reached 0.89 indicating excellent agreement between observed and estimated values. These findings validate Landsat for monitoring water temperature, supporting water resource management and ecosystem protection. Integrating remote sensing with traditional methods enhances assessment accuracy for environmental decision-making and adaptation. The low-cost predictive linear regression aids monitoring in data-scarce and arid areas.