Enhancing the Usability of In-Situ Marine Observations Under Increasing Uncertainty of Satellite Data: A Spatiotemporal Interpolation Approach for Korean Offshore and Coastal Waters

Advanced time series interpolation techniques used for estimating marine environmental factors encounter challenges regarding their usability, practical implementation, and reproducibility outside of marine science laboratories. This study aimed to interpolate NIFS Serial Oceanographic Observations and develop a system for analyzing complex factors in offshore and coastal fishing ground formation in South Korea. Additionally, the study explored the potential for integration of spatiotemporally discontinuous in situ data with continuously available satellite data through interpolation methods. Specifically, daily sea temperature and salinity data were generated through conventional time series interpolation techniques such as linear, cubic spline, and STL + PCHIP, and spatial interpolation techniques such as IDW, kriging, and natural neighbor were used to construct monthly raster data. The generated data were compared with the output of the GOFS3.1 model, and statistical indices such as MAE, RMSE, R2, and Pearson or Spearman correlation coefficients were used to evaluate the accuracy and reproducibility. Cubic spline temporal and kriging spatial interpolation methods demonstrated strong performance for the sea temperature data; however, the interpolation performance for the salinity data exhibited limited effectiveness owing to unique local variability. This study introduces techniques for transforming discontinuous in situ observational data into high-resolution data and demonstrates that the integrated use of in situ data can enhance our understanding of the fishing ground formation mechanisms and ecosystem-based fishery management.