Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

中文翻译：

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通过多源数据和转换器释放 CYGNSS 在泛热带内陆水域测绘方面的潜力


气旋全球导航卫星系统 （CyGNSS） 数据因其对内陆水体的敏感性而得到广泛认可。然而，使用单个 CyGNSS 数据检测水体存在不确定性，这给大规模和准确的水系统检测带来了挑战。在这项研究中，我们使用 CyGNSS 数据进行回归估计以绘制内陆水体。与以前的研究相比，我们纳入了额外的限制，包括地形因素、植被信息、土壤湿度以及经纬度数据。利用 U 形结构、Swin Transformer 和 ContextModule，我们有效地提取了水体分布信息，称为 CFRT。通过与流行的深度学习模型进行严格的性能比较，我们的方法表现出了卓越的准确性。生成的水百分比与参考数据表现出显著的一致性，在 CyGNSS 数据的可到达区域内实现了 7.15% 的均方根误差 （RMSE） 和 0.778 的平均交集与并集之比。我们的方法强调了利用多源数据大幅提高 CyGNSS 供水系统检测准确性的重要性。