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Geomatics World >

2025 , Vol. 32 >Issue 03: 288 - 298

DOI: https://doi.org/10.20117/j.jsti.202503008

Spatio-temporal Perception

Dynamic monitoring of water area changes in lake Victoria based on SAR imagery

  • ZHU Yutian ,
  • XU Jia ,
  • GE Ying ,
  • WANG Hongyan ,
  • ZHAO Bingkun
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  • 1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China;
    2. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China

Received date: 2025-03-19

  Revised date: 2025-05-14

  Online published: 2025-12-03

Supported by

National Key Research and Development Program of China (2023YFE0207900)

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Abstract

[Objective] Lake Victoria, the largest freshwater lake in Africa, plays a critical role in regional water resource management, flood preparedness, and ecological conservation across the Nile River Basin. However, conventional methods relying on optical remote sensing are severely limited by persistent cloud cover and heavy rainfall in the tropical climate, resulting in fragmented temporal observations long-term monitoring consistency. These atmospheric conditions often lead to discontinuities in temporal observations, limiting the ability to conduct consistent and reliable long-term monitoring. Therefore, there is an urgent need for a more stable, weather-independent, and temporally consistent method for large-scale inland water body mapping.
[Method] We advanced water body extraction techniques by exploiting the all-weather imaging capability of Sentinel-1 SAR datasets. A rigorous comparative analysis identified an optimal SAR-based water index, which was coupled with an improved Edge-Otsu algorithm to replace conventional methods that rely on manually set initial thresholds. This innovation enables adaptive, automated water body segmentation, minimizing subjectivity and enhancing methodological reproducibility. The framework was applied to generate a 10-meter resolution monthly water surface area dataset for lake Victoria from 2017 to 2023. This dataset facilitated a comprehensive analysis of the spatiotemporal dynamics of lake Victoria.
[Result] The proposed method demonstrated exceptional stability and automation across multi-temporal SAR imagery, achieving an overall water extraction accuracy of 98.9%. Compared to global products such as the JRC Global Surface Water (GSW) dataset (R = 0.1) and Dynamic World (R = 0.29), our dataset exhibited a significantly higher correlation of 0.76 with in situ water level records, reflecting superior temporal consistency and reliability. Notably, the algorithm captured dynamic water boundary fluctuations without manual intervention, outperforming traditional threshold-dependent approaches.
[Conclusion] The high-resolution dataset revealed distinct temporal and spatial patterns in lake Victoria's surface area. From 2017 to 2022, the lake exhibited a gradual expansion trend, followed by a minor contraction in 2023. More specifically, water surface area increased rapidly during the long rainy season (March to May), typically peaking in June, then gradually receding through the dry season (July to September). Spatial heterogeneity was most evident in the northeastern and southern basins, particularly in Kenya's Winam Gulf and Tanzania's Mwanza Gulf. This study provides comprehensive and long-term monitoring of the dynamic changes in lake Victoria's water surface area, offering a solid scientific basis for transboundary water resource management. It plays a vital role in supporting ecological conservation and promoting regional sustainable development. Furthermore, the SAR-based methodology pioneered here is transferable to other tropical regions, supporting hydrological modeling, climate adaptation strategies, and cross-border environmental management initiatives.

Key words： surface water resources; water extraction; Edge-Otsu; SAR; spatiotemporal variation

Cite this article

ZHU Yutian , XU Jia , GE Ying , WANG Hongyan , ZHAO Bingkun . Dynamic monitoring of water area changes in lake Victoria based on SAR imagery[J]. Geomatics World, 2025 , 32(03) : 288 -298 . DOI: 10.20117/j.jsti.202503008

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