Liu Chen
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Sci Rep. 2025 Jul 9;15(1):24787. doi: 10.1038/s41598-025-09410-3.
Long-term, high-resolution records of lake surface area are essential for characterizing the spatiotemporal dynamics of inland water bodies. Although Synthetic Aperture Radar has substantially improved water extent detection under adverse conditions, optical remote sensing imagery remains the dominant data source owing to its higher spatial resolution. Nevertheless, optical data are frequently compromised by persistent cloud cover and sensor limitations, leading to substantial observational gaps. To effectively address this challenge, this study introduces a novel spatially-informed interpolation method termed Semantic Neighborhood Correlation-based Interpolation (SNCI), which leverages spatial correlations among hydrologically interconnected lakes to reconstruct missing lake area observations. By explicitly modeling the inherent hydrological and climatic coherence among neighboring lakes, SNCI provides robust, accurate, and scalable interpolations even in the presence of extensive temporal data losses. The method was evaluated on monthly lake area data from 54 lakes in the Wuhan region between 2000 and 2020, using the Global Surface Water dataset, and validated against high-resolution Dynamic World observations. Several representative lakes were analyzed in detail to assess SNCI's robustness across diverse seasonal and interannual conditions. Compared with polynomial fitting, Random Forest, and Long Short-Term Memory, SNCI consistently achieves lower interpolation errors. In the case of East Lake, SNCI reduces mean absolute error by 50.1% and root mean square error by 28.3% relative to the best-performing baseline. Across all lakes, SNCI demonstrates superior accuracy and correlation, particularly under data-sparse conditions. These results underscore SNCI's potential to enhance lake area reconstruction accuracy and support broader applications in hydrological modeling, environmental monitoring, and climate impact assessment.
长期、高分辨率的湖泊表面积记录对于描述内陆水体的时空动态至关重要。尽管合成孔径雷达在不利条件下显著改善了水域范围的探测，但光学遥感图像因其更高的空间分辨率，仍然是主要的数据来源。然而，光学数据经常受到持续云层覆盖和传感器限制的影响，导致大量观测空白。为了有效应对这一挑战，本研究引入了一种新的空间信息插值方法，称为基于语义邻域相关性的插值（SNCI），该方法利用水文相互连接的湖泊之间的空间相关性来重建缺失的湖泊面积观测值。通过明确模拟相邻湖泊之间固有的水文和气候连贯性，即使在存在大量时间数据损失的情况下，SNCI也能提供稳健、准确且可扩展的插值。该方法使用全球地表水数据集，对2000年至2020年武汉地区54个湖泊的月度湖泊面积数据进行了评估，并与高分辨率动态世界观测数据进行了验证。对几个具有代表性的湖泊进行了详细分析，以评估SNCI在不同季节和年际条件下的稳健性。与多项式拟合、随机森林和长短期记忆相比，SNCI始终能实现更低的插值误差。以东湖为例，相对于表现最佳的基线，SNCI将平均绝对误差降低了50.1%，均方根误差降低了28.3%。在所有湖泊中，SNCI都表现出卓越的准确性和相关性，特别是在数据稀疏的条件下。这些结果凸显了SNCI在提高湖泊面积重建准确性以及支持水文建模、环境监测和气候影响评估等更广泛应用方面的潜力。
