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Spatially-informed interpolation for reconstructing lake area time series using semantic neighborhood correlation
1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China. sweet8degree@gmail.com.
Scientific Reports
|July 9, 2025
Summary
A new Semantic Neighborhood Correlation-based Interpolation (SNCI) method reconstructs missing lake surface area data by using spatial correlations between lakes. This approach improves accuracy, especially when optical remote sensing data is limited, aiding hydrological studies.
Area of Science:
- Hydrology and Remote Sensing
- Inland water body dynamics
- Geospatial data analysis
Background:
- High-resolution lake surface area records are crucial for understanding inland water dynamics.
- Optical remote sensing offers high spatial resolution but is limited by cloud cover and sensor issues, causing data gaps.
- Existing methods struggle with extensive data loss in lake area monitoring.
Purpose of the Study:
- To introduce a novel interpolation method, Semantic Neighborhood Correlation-based Interpolation (SNCI), for reconstructing missing lake area observations.
- To leverage spatial correlations among hydrologically connected lakes for robust data imputation.
- To enhance the accuracy and scalability of lake area monitoring, particularly under data-sparse conditions.
Main Methods:
- Developed and applied the Semantic Neighborhood Correlation-based Interpolation (SNCI) method.
- Utilized monthly lake area data for 54 lakes in the Wuhan region (2000-2020) from the Global Surface Water dataset.
- Validated SNCI against high-resolution Dynamic World observations and compared it with polynomial fitting, Random Forest, and Long Short-Term Memory models.
Main Results:
- SNCI demonstrated superior accuracy and lower interpolation errors compared to baseline methods across all evaluated lakes.
- For East Lake, SNCI reduced mean absolute error by 50.1% and root mean square error by 28.3% relative to the best baseline.
- The method showed particular effectiveness in improving accuracy under data-sparse conditions and diverse seasonal/interannual variations.
Conclusions:
- SNCI offers a robust, accurate, and scalable solution for reconstructing lake surface area, effectively addressing data gaps from optical remote sensing.
- The method's ability to model hydrological and climatic coherence among lakes enhances interpolation reliability.
- SNCI has significant potential for advancing hydrological modeling, environmental monitoring, and climate impact assessments.
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