Guo et al. (2026) Monthly dynamics of global surface water from 2015 to 2023

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-09-11
• Authors: Xiaoyang Guo, Yuchen Liu, Richard Iestyn Woolway, Ting Du, Lai Lai, Yongqi Sun, Yanjun Tian, Yongnian Gao
• DOI: 10.1016/j.jhydrol.2026.136414

Research Groups

• School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
• Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
• College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China
• School of Ocean Sciences, Bangor University, Anglesey, Wales LL57 2DG, UK
• State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

Short Summary

This study presents the High precision Global Monthly Surface Water dataset (HGMSW), a 10 m monthly surface water record from 2015 to 2023. The framework combines Sentinel 1 SAR and Sentinel 2 multispectral observations to achieve high accuracy in surface water mapping.

Objective

• Investigate global surface water dynamics at a fine spatial scale of 10 m resolution, with a focus on monthly continuity and reliable observations under frequent cloud cover.

Study Configuration

• Spatial Scale: Global, with a spatial resolution of 10 m.
• Temporal Scale: Monthly, from 2015 to 2023.

Methodology and Data

• Models used: Sentinel 1 SAR and Sentinel 2 multispectral observations.
• Data sources: Satellite data from Sentinel 1 and Sentinel 2, processed using the Google Earth Engine platform.

Main Results

• The HGMSW dataset achieved an overall accuracy of 97.59%, an F1 score of 97.55%, and a Kappa coefficient of 95.18% in global validation.
• Independent monthly validation using 3 m PlanetScope imagery across nine representative regions yielded a mean overall accuracy of 97.8%.
• The dataset revealed a global monthly mean surface water area of 2.95 × 10^6 km^2 during 2017–2021, with an average annual growth rate of 2.08%.

Contributions

• Fills the gap between long-term 30 m water products and static 10 m land cover datasets.
• Provides a spatially detailed monthly record for monitoring surface water dynamics and their responses to climate variability and human regulation.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 52179209).

Citation

@article{Guo2026Monthly,
  author = {Guo, Xiaoyang and Liu, Yuchen and Woolway, Richard Iestyn and Du, Ting and Lai, Lai and Sun, Yongqi and Tian, Yanjun and Gao, Yongnian},
  title = {Monthly dynamics of global surface water from 2015 to 2023},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136414},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136414}
}