Patel et al. (2025) Advances in remote sensing and GIS applications in watershed hydrology: a systematic review

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-10-21
• Authors: Rahul Kumar Patel, Prasoon Soni, Pushpraj Singh
• DOI: 10.1016/j.jhydrol.2025.134459

Research Groups

Department of Rural Technology and Social Development, Guru Ghasidas University, Koni Bilaspur, Chhattisgarh, India

Short Summary

This systematic review analyzes the advancements in remote sensing (RS) and Geographic Information Systems (GIS) applications in watershed hydrology from 2000 to 2024, with a detailed focus from 2018, highlighting the increasing integration of artificial intelligence (AI) and multi-source modeling for improved hydrological predictions.

Objective

• To systematically review studies on the application of remote sensing and GIS in watershed hydrology, focusing on advancements from 2000 to 2024, particularly from 2018 onward, to identify novel datasets, methods, and impacts in the field.

Study Configuration

• Spatial Scale: Global, encompassing various watershed scales as reported in the reviewed literature.
• Temporal Scale: Systematic review of studies published between 2000 and 2024, with a detailed focus on advancements from 2018 onward.

Methodology and Data

• Models used: Hydrological models (e.g., SWAT, HEC-HMS, MIKE SHE) and AI-based models (e.g., LSTM networks) as applied in the reviewed literature.
• Data sources: Satellite remote sensing data (e.g., Landsat, Sentinel, MODIS, GPM, SMAP), traditional ground observations (e.g., rain gauges, streamflow recorders, soil sampling), and multi-sensor/UAV datasets as utilized in the reviewed literature. Databases for the review: Scopus, Web of Science, and Google Scholar.

Main Results

• A systematic literature review identified 65 critically assessed studies from an initial pool of 312 articles.
• Bibliometric analysis reveals a significant increase in publications related to RS-GIS applications in watershed hydrology since 2018.
• There is a notable focus on AI-developed runoff prediction and integrated multi-source modeling approaches in recent literature.
• Advancements include improved sensor resolution, artificial intelligence, and cloud computing, enhancing the capability to simulate and forecast hydrological processes.

Contributions

• Provides a systematic and detailed review of RS and GIS applications in watershed hydrology, specifically covering the period 2000–2024, with an in-depth analysis of advancements from 2018.
• Highlights the impact of recent technological advancements (sensor resolution, AI, cloud computing) on hydrological studies.
• Identifies trends in novel datasets (multi-sensor, UAV), methods (AI, participatory GIS), and applications in data-scarce basins.
• Synthesizes the fragmented literature on various hydrological processes (precipitation, evapotranspiration, runoff, erosion, groundwater) within a watershed context.

Funding

• No specific funding information was provided in the paper text.

Citation

@article{Patel2025Advances,
  author = {Patel, Rahul Kumar and Soni, Prasoon and Singh, Pushpraj},
  title = {Advances in remote sensing and GIS applications in watershed hydrology: a systematic review},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134459},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134459}
}