Wu et al. (2026) Surface Water Extraction by Multidimensional Feature Fusion of Sentinel-1/2 and Random Forest: A Five-Year Analysis in Jiangsu, China

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Mingxing Wu, Fengcheng Guo, Jing Zhang, Li Zhao, Wensong Liu, Chongchong Zhou
• DOI: 10.1109/jstars.2026.3667561

Research Groups

Not available in the provided text.

Short Summary

This paper aims to develop and apply a method for surface water extraction using multidimensional feature fusion of Sentinel-1 and Sentinel-2 data with a Random Forest classifier, conducting a five-year analysis in Jiangsu, China.

Objective

• To develop and apply a method for accurate surface water extraction using multidimensional feature fusion of Sentinel-1 and Sentinel-2 satellite data, combined with a Random Forest classifier, and to analyze surface water dynamics over a five-year period in Jiangsu, China.

Study Configuration

• Spatial Scale: Jiangsu Province, China
• Temporal Scale: Five-year analysis

Methodology and Data

• Models used: Random Forest
• Data sources: Sentinel-1 (Synthetic Aperture Radar), Sentinel-2 (Multispectral Imager)

Main Results

Not available in the provided text.

Contributions

Not available in the provided text.

Funding

Not available in the provided text.

Citation

@article{Wu2026Surface,
  author = {Wu, Mingxing and Guo, Fengcheng and Zhang, Jing and Zhao, Li and Liu, Wensong and Zhou, Chongchong},
  title = {Surface Water Extraction by Multidimensional Feature Fusion of Sentinel-1/2 and Random Forest: A Five-Year Analysis in Jiangsu, China},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3667561},
  url = {https://doi.org/10.1109/jstars.2026.3667561}
}