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

Bulletin of Geophysics and Oceanography, cilt.66, sa.4, ss.1, 2025 (Hakemli Dergi)

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 (MGM) 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 (GB), 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 °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