Virtual sea surface temperature stations for the Turkish coastal gaps: a machine learning-driven fusion of satellite and in-situ data

Abstract

Accurate monitoring of sea surface temperature (SST) is vital for understanding regional climate variability, marine ecosystem dynamics, and long-term climate change. In this study, the consistency between satellite-derived SST data from the Copernicus Marine Environment Monitoring Service (CMEMS) and in-situ observations from 21 coastal stations operated by the Turkish State Meteorological Service was evaluated across the Turkish coastline. Initial assessments were based on classical statistical comparisons using the root-mean-square deviation and Pearson correlation. Subsequently, four machine learning (ML) regression models, linear regression, support vector regression, gradient boosting, and artificial neural networks, were applied to assess the predictive capability of CMEMS data for estimating in-situ SST. Among the models, GB achieved the best overall performance (coefficient of determination = 0.97, root-mean-square error = 0.84 degrees C), owing to its ability to effectively capture complex nonlinear relationships between datasets. Based on these results, a spatial gap analysis was conducted, and eight statistically optimised proxy observation points (termed virtual SST stations) were proposed to enhance SST coverage in underserved coastal segments. This study demonstrates a scalable (regionally adaptable) and objective methodology for optimising SST monitoring networks by integrating ML with geospatial analysis. The proposed approach offers practical benefits in enhancing climate resilience, improving SST anomaly forecasting, and supporting evidence-based marine resource management, such as fishery zoning or coastal ecosystem protection.