Year: 2025
Type: article
Abstract: Precise mapping of surface water is of great importance for water resource management, ecological environment protection, and flood disaster mitigation. However, the Songliao Basin still lacks a high-precision, long-term surface water distribution dataset. To address this gap, this paper, based on satellite remote sensing data (Landsat-5/7/8/9) and implemented on the Google Earth Engine (GEE) platform, developed a surface water extraction algorithm suitable for the Songliao River Basin. It produced an annual surface water distribution dataset from 1984 to 2023. This dataset includes an annual Water Body Frequency (WBF) dataset and a Surface Water Classification (SWC) dataset, covering seasonal water bodies, permanent water bodies, and annual maximum water extent. Through accuracy assessment, the overall accuracy of the water extraction method reached 98%, with a Kappa coefficient of 0.95. To verify the reliability of the dataset, the SWC dataset was compared with the Global Surface Water (GWS) historical dataset. The results show that the SWC dataset achieves higher classification accuracy than the GWS dataset in multiple subregions of the Songliao Basin. The high-precision water dataset provided by this research offers important support for the monitoring and sustainable management of water resources in the Songliao Basin and lays a solid data foundation for scientific research in related fields.
Source: China Scientific Data
Authors: Yanling ZHANG, Zhaoyang TANG, Chen SONG, Dan LI, Handong MENG, Ning JIN, Xingwang ZHANG, Haoming XIA
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117136457