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Related Experiment Video

Updated: Oct 30, 2025

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Mapping flood by the object-based method using backscattering coefficient and interference coherence of Sentinel-1

Xianlong Zhang¹, Ngai Weng Chan², Bin Pan¹

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

The Science of the Total Environment

|July 3, 2021

Summary

This study uses Sentinel-1 Synthetic Aperture Radar (SAR) data time series to monitor flood dynamics. Combining backscatter coefficients and interferometric coherence with an object-based Random Forest model accurately extracts flood information.

Keywords:

Backscattering coefficient Flood mapping Interference coherence Object-oriented Random Forest Sentinel-1

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Area of Science:

Earth Observation
Remote Sensing
Environmental Monitoring

Background:

Synthetic Aperture Radar (SAR) offers all-weather, all-time data acquisition, penetrating clouds and adverse weather.
SAR images provide high contrast and rich texture information, crucial for environmental analysis.

Purpose of the Study:

To investigate an object-oriented classification approach for floodplain flood monitoring using Sentinel-1 SAR time series data.
To assess the utility of backscattering coefficients and interferometric coherence for flood information extraction.

Main Methods:

Analysis of backscattering and interferometric coherence variations in SAR time series.
Feature selection using the Random Forest (RF) model's contribution rate index for dimensionality reduction.
Object-based classification employing multi-scale segmentation and selected SAR image features.

Main Results:

SAR time series attributes accurately correlated with actual flood risk.
Combined use of backscattering coefficient and interferometric coherence significantly improved flood extraction accuracy.
The object-based RF method effectively extracted and segmented water bodies, revealing flood dynamics.

Conclusions:

The study demonstrates the effectiveness of Sentinel-1 SAR time series for flood monitoring.
The object-based Random Forest approach enhances understanding of temporal and spatial flood dynamics.
Findings support timely adaptation and mitigation strategies for flood loss reduction.