论文标题
使用Sentinel-1合成孔径雷达图像和数字高程模型的水位估算
Water Level Estimation Using Sentinel-1 Synthetic Aperture Radar Imagery And Digital Elevation Models
论文作者
论文摘要
水电坝和水库已被确定为重新定义自然水文周期的主要因素。因此，监测水库中的水位状态在规划和管理水资源以及预测干旱和洪水方面起着至关重要的作用。传统上，这项任务是通过在附近的地面上安装传感器站来完成的，该水域的维护成本，可访问性和全球覆盖范围有多个缺点。为了应对这些问题，遥感被称为获取有关物体或区域的信息而无需与之接触的科学，已被积极研究。在本文中，我们提出了一种新型的水位提取方法，该方法采用了Sentinel-1合成孔径雷达图像和数字高程模型数据集。实验表明，该算法在全球三个储层上达到了0.93米的较低平均误差，证明其可能被广泛应用和研究的潜力。
Hydropower dams and reservoirs have been identified as the main factors redefining natural hydrological cycles. Therefore, monitoring water status in reservoirs plays a crucial role in planning and managing water resources, as well as forecasting drought and flood. This task has been traditionally done by installing sensor stations on the ground nearby water bodies, which has multiple disadvantages in maintenance cost, accessibility, and global coverage. And to cope with these problems, Remote Sensing, which is known as the science of obtaining information about objects or areas without making contact with them, has been actively studied for many applications. In this paper, we propose a novel water level extracting approach, which employs Sentinel-1 Synthetic Aperture Radar imagery and Digital Elevation Model data sets. Experiments show that the algorithm achieved a low average error of 0.93 meters over three reservoirs globally, proving its potential to be widely applied and furthermore studied.
