Remote Sensing and GIS Applications in Environmental Sciences

Spatio‑temporal monitoring and analysis of water surface changes in Mahabad Dam using an integrated data‑driven approach based on Sentinel‑1 data and climatic variables

Document Type : Original Article

Authors

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

10.22034/rsgi.2026.71832.1157

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

Main Subjects

Accurate monitoring of reservoir water surface area is essential for sustainable water resource management, particularly in arid and semi‑arid regions where water availability is highly sensitive to climatic variability and anthropogenic pressures. Sentinel‑1 synthetic aperture radar (SAR) data, with its all‑weather and day‑night imaging capability, provides a reliable framework for investigating surface water dynamics while overcoming the limitations of optical imagery. In this study, 360 monthly averaged ascending Sentinel‑1 images acquired in VV and VH polarizations from January 2017 to December 2024 were processed to analyze the spatio‑temporal variations in the water surface area of Mahabad Dam. Ascending orbit images were selected due to clearer reservoir boundary delineation, whereas descending images were excluded because of reduced visibility associated with acquisition geometry. Water body extraction was performed using a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. Prior to classification, a 3 × 3 Lee filter was applied to reduce speckle noise. The model was trained using 1,000 reference samples (500 water and 500 non‑water), manually selected based on visual interpretation of SAR imagery and ancillary data. Classification accuracy was assessed monthly using Overall Accuracy (OA), User’s Accuracy (UA), and the Kappa coefficient, ensuring the reliability of the extracted water surface area. To further enhance the analysis, the XGBoost machine learning model was employed to integrate SAR‑derived water surface area with key climatic variables, including precipitation and temperature. The gradient boosting structure of XGBoost enabled effective modeling of complex, nonlinear relationships between climatic drivers and reservoir dynamics. Model evaluation indicated that Scenario 3 achieved the highest performance (RMSE = 0.526, MAE = 0.464, R² = 0.911, NSE = 0.911, WI = 0.977), while Scenario 4 showed the weakest performance. Overall, the results demonstrate that the synergistic integration of SAR remote sensing data and advanced machine learning techniques provides a robust, scalable, and efficient framework for monitoring and forecasting reservoir water surface area dynamics. This framework offers valuable insights for improving water resource management, supporting drought mitigation and adaptation strategies, and contributing to sustainable watershed planning in data‑scarce and climate‑sensitive regions. Nevertheless, the study is limited by its focus on a single reservoir and a restricted set of climatic variables. Future research should extend this approach by incorporating additional hydro‑meteorological factors, exploring alternative machine learning models, and applying the framework to multiple reservoirs to enhance its generalizability.

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Remote Sensing and GIS Applications in Environmental Sciences
Volume 6, Issue 19
July 2026
  • Receive Date: 28 March 2026
  • Revise Date: 21 April 2026
  • Accept Date: 25 April 2026