Accurate measurement of water levels is essential for effectively managing reservoirs to proactively mitigate flooding and drought. Nonetheless, the inaccuracies in measurements derived from gauging station and remote sensing images impose constraints to water resource management. In this study, we developed a novel water level estimation model which utilizes solely the altitude of reliable water boundary pixels to improve the accuracy. The enhanced water boundary detection, incorporating preprocessing steps such as image filtering, resampling, and polarization multiplication, was applied to achieve sub-pixel precision in detecting water boundaries. The water boundary pixels located in layover and shadow regions, which could be misidentified due to distortion error, are eliminated based on backward geolocation. Ambiguous water boundaries, potentially indicating land with low intensity, were defined by computing their absolute derivatives, and removed. Finally, to enhance water level precision, the model computed water levels by averaging the altitudes of boundary pixels with weighting factors of local incidence angle, derivatives of detected water boundaries, and altitude distribution. Compared with the previous studies utilizing water boundaries, the proposed model demonstrated outstanding performance in improving the accuracy, up to 1/40th smaller than the spatial resolution of SAR images in Mean Absolute Error (MAE). The validation was executed over the results from >700 Sentinel-1 against the in-situ measurements obtained from multiple reservoirs and streams with significant water level fluctuations in the Korean peninsula. In this process, we found that the water boundaries located on the layover and shadow regions significantly influence the dispersion of altitude of reliable water boundary pixels. This study demonstrates the proposed model relying on remote sensing data without in-situ measurements, which holds potential applicability under situations where in-situ data are unavailable.

中文翻译：

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通过增强的水边界检测和基于可靠性的加权平均来高精度监测水位变化的新模型


准确测量水位对于有效管理水库、主动缓解洪水和干旱至关重要。尽管如此，计量站和遥感图像的测量结果的不准确性对水资源管理造成了限制。在本研究中，我们开发了一种新颖的水位估计模型，该模型仅利用可靠水边界像素的高度来提高精度。增强型水体边界检测结合了图像滤波、重采样和偏振乘法等预处理步骤，在水体边界检测中实现了亚像素精度。基于后向地理定位，消除了位于重叠和阴影区域的水边界像素，这些像素可能由于畸变误差而被错误识别。模糊的水域边界可能表明土地强度较低，通过计算其绝对导数来定义并删除。最后，为了提高水位精度，模型通过对边界像素的高度进行平均，以局部入射角、检测到的水边界的导数和高度分布的加权因子来计算水位。与之前利用水边界的研究相比，所提出的模型在提高精度方面表现出出色的性能，平均绝对误差（MAE）比SAR图像的空间分辨率小1/40。验证是根据 >700 Sentinel-1 的结果与从朝鲜半岛水位波动显着的多个水库和溪流获得的现场测量结果进行的。 在此过程中，我们发现位于重叠和阴影区域的水域边界显着影响可靠水域边界像素的高度离散度。这项研究证明了所提出的模型依赖于遥感数据而无需现场测量，在无法获得现场数据的情况下具有潜在的适用性。