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Water Body Extraction Methods for SAR Images Fusing Sentinel-1 Dual-Polarized Water Index and Random Forest.

Min Zhai^1,2, Huayu Shen¹, Qihang Cao¹

¹College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

A new fusion method combining Synthetic Aperture Radar (SAR) dual-polarized water index and random forest improves water body extraction accuracy from Sentinel-1 images. This approach overcomes limitations of single methods, enhancing reliability for all-weather remote sensing applications.

Keywords:

NDWI RF SDWI Sentinel-1 water body extraction

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Area of Science:

Earth Observation
Remote Sensing Technology
Geospatial Analysis

Background:

Optical remote sensing is limited by weather conditions, hindering efficient water body extraction.
Single methods for extracting water bodies from Synthetic Aperture Radar (SAR) images often yield low accuracy and unstable results.
Sentinel-1 SAR images offer all-day, all-weather capabilities crucial for consistent water monitoring.

Purpose of the Study:

To develop and evaluate a novel water body extraction method for Sentinel-1 SAR images.
To enhance the accuracy and reliability of water extraction by fusing dual-polarized water index and random forest algorithms.
To address the limitations of single-method approaches in water body delineation from SAR data.

Main Methods:

A fusion method was developed by integrating the Sentinel-1 dual-polarized water index and the random forest algorithm.
Water bodies were extracted from Sentinel-1 SAR images using the dual-polarized water index, random forest, and the proposed fusion method.
Accuracy was quantitatively evaluated against Sentinel-2 optical image-derived Normalized Difference Water Index (NDWI) results for Dalu Lake, Yinfu Reservoir, and Huashan Reservoir.

Main Results:

The fusion method demonstrated superior performance compared to individual methods.
Average increases in overall water body extraction accuracy ranged from 1.8% to 4.1%.
Average increases in Kappa coefficients ranged from 3.5% to 8.2%, indicating improved classification agreement.

Conclusions:

The fusion of the dual-polarized water index and random forest significantly improves water body extraction accuracy and reliability from SAR images.
This integrated approach effectively mitigates the biases associated with single-method water extraction.
The proposed method provides a robust solution for efficient water monitoring using all-weather SAR data.