Detection of Inverted Channels Using Sentinel-1 Data and Sentinel-2 Data on Google Earth Engine: A Comparative Analysis

Inverted fluvial channels are significant geomorphological features, particularly in arid regions, offering insights into past hydrological regimes and paleo-climatic conditions. Accurate mapping of these features is crucial for understanding landscape evolution in arid regions. However, it is challenging to detect these inverted channels over large areas based on traditional field methods. This study develops and evaluates a workflow for inverted channel detection by comparing the performance of a Sentinel-1 Synthetic Aperture Radar (SAR) only approach with a synergistic data fusion approach. The first method relies on thresholding the backscatter intensity of Sentinel-1 imagery. The second method is to fuse Sentinel-1 SAR data and Sentinel-2 multispectral data for classification using Random Forest (RF) classifier to generate classification probability maps, which are then used for channel detection. The methodologies are tested and validated across four distinct study sites. The results demonstrate the superiority of the data fusion approach. Accuracy assessments show that the integration of Sentinel-2 data improve the classification performance, with Overall Accuracy (OA) increasing from a range of 0.82-0.86 to 0.84-0.90 and Kappa coefficients rising from 0.66-0.77 to 0.69-0.79. Receiver operating characteristic (ROC) analysis further confirms this enhancement, with an increase in the area under the curve (AUC). Visual comparison also shows that the fusion approach produces a more coherent and complete channel network, overcoming the inherent problems of the SAR-only approach. We conclude that the synergy between Sentinel-1’s sensitivity to surface roughness and Sentinel-2’s spectral information provides a more comprehensive characterization of the landscape, leading to a highly accurate and robust delineation of inverted channels. The proposed workflow provides an efficient method for detecting inverted channel, offering a valuable tool for geomorphological research, landscape evolution studies, and hydrological exploration in arid environments.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3591178
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection of Inverted Channels Using Sentinel-1 Data and Sentinel-2 Data on Google Earth Engine: A Comparative Analysis

Xuhua Weng
IEEE Access
Anomaly Detection Techniques and Applications
article

Detection of Inverted Channels Using Sentinel-1 Data and Sentinel-2 Data on Google Earth Engine: A Comparative Analysis

Xuhua Weng
article en

Abstract

Inverted fluvial channels are significant geomorphological features, particularly in arid regions, offering insights into past hydrological regimes and paleo-climatic conditions. Accurate mapping of these features is crucial for understanding landscape evolution in arid regions. However, it is challenging to detect these inverted channels over large areas based on traditional field methods. This study develops and evaluates a workflow for inverted channel detection by comparing the performance of a Sentinel-1 Synthetic Aperture Radar (SAR) only approach with a synergistic data fusion approach. The first method relies on thresholding the backscatter intensity of Sentinel-1 imagery. The second method is to fuse Sentinel-1 SAR data and Sentinel-2 multispectral data for classification using Random Forest (RF) classifier to generate classification probability maps, which are then used for channel detection. The methodologies are tested and validated across four distinct study sites. The results demonstrate the superiority of the data fusion approach. Accuracy assessments show that the integration of Sentinel-2 data improve the classification performance, with Overall Accuracy (OA) increasing from a range of 0.82-0.86 to 0.84-0.90 and Kappa coefficients rising from 0.66-0.77 to 0.69-0.79. Receiver operating characteristic (ROC) analysis further confirms this enhancement, with an increase in the area under the curve (AUC). Visual comparison also shows that the fusion approach produces a more coherent and complete channel network, overcoming the inherent problems of the SAR-only approach. We conclude that the synergy between Sentinel-1’s sensitivity to surface roughness and Sentinel-2’s spectral information provides a more comprehensive characterization of the landscape, leading to a highly accurate and robust delineation of inverted channels. The proposed workflow provides an efficient method for detecting inverted channel, offering a valuable tool for geomorphological research, landscape evolution studies, and hydrological exploration in arid environments.

IEEE AccessVol. 13
Zhejiang University (CN)
Climate action
Openalex Percentile: Top 91%
Anomaly Detection Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Detection of Inverted Channels Using Sentinel-1 Data and Sentinel-2 Data on Google Earth Engine: A Comparative Analysis — Xuhua Weng · IEEE Access (2025) | TGRS Research Map | TGRS