Spatially-informed interpolation for reconstructing lake area time series using semantic neighborhood correlation.

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.