Document Type : Original Article

Authors

• Samaneh Bagheri ¹
• Sadra Karimzadeh ²
• Bakhtiar Feizizadeh ¹
• Saeedd Samadianfard ³

¹ Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz

² Department of Remote Sensing and GIS, University of Tabriz, Iran

³ Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

Abstract

Accurate monitoring of reservoir water surface area is essential for sustainable water management, especially in arid and semi‑arid regions that are highly sensitive to climatic fluctuations. Sentinel‑1 synthetic aperture radar (SAR) imagery, with its all‑weather and day‑night acquisition capability, provides a reliable basis for tracking surface water dynamics. In this study, 360 ascending Sentinel‑1 images in VV and VH polarizations were used to extract the water surface area of the Mahabad Dam. Water bodies were identified using the Support Vector Machine (SVM) classifier, which effectively distinguishes water from non‑water features based on radar backscatter. To enhance prediction accuracy, the XGBoost model was applied to integrate SAR‑derived water area with climatic variables such as precipitation and temperature. This approach enabled modeling of nonlinear relationships affecting reservoir variations. Model performance was evaluated using RMSE, MAE, R², NSE, and WI indices. Scenario 3 provided the most accurate results, with RMSE of 0.526, MAE of 0.464, R² of 0.911, and WI of 0.977, indicating strong agreement between predicted and observed values. Scenario 4 showed the weakest performance. Overall, integrating Sentinel‑1 SAR data with machine learning methods such as XGBoost offers an efficient framework for monitoring and predicting reservoir surface area changes, supporting improved water management and drought mitigation in climate‑sensitive regions.

Keywords

• Mahabad Dam
• Sentinel 1
• XGBoost
• Support Vector Machine
• Water Surface Area

Main Subjects

• Remote sensing and Gis