Zhu et al. (2026) Application and optimization algorithm of multi-source remote sensing data fusion in water resources monitoring

Identification

• Journal: IET conference proceedings.
• Year: 2026
• Date: 2026-01-01
• Authors: He Zhu, Jianwei Ma, Yayong Sun, Nan Li, Xinyu Chen
• DOI: 10.1049/icp.2025.3714

Research Groups

China Institute of Water Resources and Hydropower Research, Beijing, People's Republic of China

Short Summary

This paper proposes an optimized multi-source remote sensing data fusion algorithm to address challenges in water resources monitoring, demonstrating its superiority over traditional methods in improving water extraction accuracy, soil moisture inversion accuracy, and drought monitoring timeliness.

Objective

• To develop and optimize a multi-source remote sensing data fusion algorithm to overcome limitations of traditional water resources monitoring methods, specifically addressing data heterogeneity, temporal and spatial consistency, and algorithm efficiency.

Study Configuration

• Spatial Scale: Regional (Henan section of the middle and lower reaches of the Yellow River).
• Temporal Scale: Not explicitly defined, but the methodology focuses on improving monitoring timeliness and real-time capabilities.

Methodology and Data

• Models used: An optimized multi-source remote sensing data fusion algorithm, incorporating a phased fusion strategy with five core modules: data preprocessing and quality evaluation, adaptive weight distribution, multi-scale temporal and spatial alignment, lightweight fusion model construction, and parallel computing optimization.
• Data sources: Multi-source remote sensing data including Landsat-8, Sentinel-1, Sentinel-2, and MODIS.

Main Results

• The proposed multi-source remote sensing data fusion method significantly improves water extraction accuracy, soil moisture inversion accuracy, and drought monitoring timeliness compared to traditional methods.
• The algorithm enhances overall monitoring accuracy and efficiency for water resources.

Contributions

• Proposes an innovative, optimized multi-source remote sensing data fusion algorithm specifically designed to overcome data heterogeneity, temporal and spatial consistency issues, and improve algorithm efficiency in water resources monitoring.
• Provides a new technical means for more accurate and timely water resources monitoring, addressing limitations of existing methods.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Zhu2026Application,
  author = {Zhu, He and Ma, Jianwei and Sun, Yayong and Li, Nan and Chen, Xinyu},
  title = {Application and optimization algorithm of multi-source remote sensing data fusion in water resources monitoring},
  journal = {IET conference proceedings.},
  year = {2026},
  doi = {10.1049/icp.2025.3714},
  url = {https://doi.org/10.1049/icp.2025.3714}
}