An Evolutionary Process-Embedded Spatiotemporal Interpolation Method for Marine Environmental Fields

In the geographic environment, mesoscale ocean eddies and similar phenomena exhibit continuous and gradual changes. However, due to limitations in remote sensing observation technology, the obtained observational data are discrete, which contradicts the continuously evolving characteristics of these phenomena. Although spatiotemporal interpolation is a key tool for bridging this gap, existing single-model methods fail to fully consider continuous process features, making it difficult to obtain consistent high-quality datasets. To solve this problem, this paper combines deep learning and geostatistics to propose an Evolutionary Process-embedded Marine Spatiotemporal Interpolation Model (EPMSIM). EPMSIM first applies Seasonal and Trend decomposition using Loess (STL) to decompose marine time-series fields into trend, seasonal, and evolutionary components. Then, a Convolutional Bidirectional Long Short-Term Memory (ConvBiLSTM) model is adopted to interpolate the trend and seasonal components. Meanwhile, a Process-based Spatiotemporal Dynamic Tracking Interpolation Method (PSDTIM) is designed to interpolate the evolutionary component. Finally, these components are combined through additive coupling to produce the final interpolation result. In case studies of mesoscale eddy interpolation using SST and SLA data, EPMSIM outperforms traditional geostatistical and deep learning baselines in RMSE, MAE, and SSIM. Experimental results confirm that the model achieves significant interpolation effects in marine environmental element fields with evolutionary characteristics, validating its effectiveness in capturing continuous evolution features of marine phenomena and its feasibility for generating high-temporal-resolution spatiotemporal datasets. This study provides a methodological reference for data interpolation of evolutionary process phenomena in marine information science, and this method can be extended to other similar marine environmental variables, serving research on marine ecological environments and dynamic processes.