Zeng et al. (2025) Emerging remote sensing techniques for hydrological applications

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-10-15
• Authors: Jiangyuan Zeng, Di Long, Yongqiang Zhang, Dongryeol Ryu, Jean‐Pierre Wigneron, Qi Huang
• DOI: 10.1016/j.rse.2025.115060

Research Groups

Editorial team of the special issue "Emerging remote sensing techniques for hydrological applications" in Remote Sensing of Environment.

Short Summary

This editorial provides a systematic overview of 31 publications within a special issue, highlighting advancements in remote sensing techniques, including multi-sensor platforms and machine learning, for monitoring and modeling hydrological variables and their operational applications.

Objective

• To review state-of-the-art models, algorithms, methods, products, and applications of remote sensing in hydrology.
• To present new methods and techniques incorporating remote sensing data to estimate and predict hydrological flux and state variables.
• To showcase new global and regional hydrological applications using remote sensing data.
• To explore the integration of remotely sensed information with hydrological modeling to improve process-based understanding of hydrology under a changing environment.

Study Configuration

• Spatial Scale: Studies reviewed cover varying spatial resolutions, including global and regional hydrological applications.
• Temporal Scale: The special issue spans from October 2022 to April 2024, reviewing studies that address hydrological processes over various temporal scales, including real-time monitoring.

Methodology and Data

• Models used: Advanced physical models and machine learning approaches (as highlighted in the reviewed publications).
• Data sources: Multi-sensor satellite platforms (e.g., SAR, hyperspectral imaging, LiDAR, SMOS, SMAP, GRACE-FO, SWOT, Sentinel-1/2/3, Landsat-8/9, China’s Gaofen and Fengyun series), unmanned aerial vehicles (UAVs), active and passive remote sensing, multi-sensor fusion, and multi-mode integration.

Main Results

• The special issue comprises 31 publications demonstrating significant advancements in hydrological remote sensing.
• Methodologies leverage multi-sensor satellite platforms, UAVs, and advanced physical models and machine learning approaches.
• These techniques improve the monitoring and modeling of key hydrological flux and state variables, such as water body extent, soil moisture, river discharge, water level, and terrestrial water storage.
• Remote sensing retrievals are applied to various operational hydrological applications, including real-time flood monitoring and drought risk assessment.
• Emerging trends identified include multi-sensor integration and machine learning-driven approaches, which are expected to shape future research.

Contributions

• Provides a systematic synthesis and categorization of 31 recent publications on emerging remote sensing techniques for hydrological applications.
• Highlights the diverse range of cutting-edge technologies (e.g., SAR, LiDAR, hyperspectral, multi-sensor platforms, UAVs) and methodologies (e.g., advanced physical models, machine learning) being applied in hydrology.
• Showcases the application of remote sensing data to improve monitoring and modeling of hydrological variables and support operational applications like flood and drought management.
• Offers an outlook on future research directions and emerging trends in hydrological remote sensing.

Funding

Not specified for this editorial.

Citation

@article{Zeng2025Emerging,
  author = {Zeng, Jiangyuan and Long, Di and Zhang, Yongqiang and Ryu, Dongryeol and Wigneron, Jean‐Pierre and Huang, Qi},
  title = {Emerging remote sensing techniques for hydrological applications},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115060},
  url = {https://doi.org/10.1016/j.rse.2025.115060}
}