Spatial Densification of Coastal Sea Surface Temperature and Chlorophyll via Bayesian Kriging

In many environmental applications, high-quality measurements are too sparse to resolve the small-scale patterns required for process understanding and management. We investigate a Bayesian kriging (BK) framework that densifies sparse coastal observations into high-resolution gridded fields with calibrated uncertainty. Two pilot sites are considered: (i) sea surface temperature (SST) in the Algarve (Portugal), where point measurements (~10 km spacing) are reconstructed on a 500 m grid, and (ii) chlorophyll (Chl) in the La Spezia embayment (Italy), where in situ supported fields are reconstructed at 30 m. The variogram parameters are treated as random variables with weakly informative priors and inferred via MCMC, so that both measurement noise and structural (variogram) uncertainty are propagated to predictions, yielding posterior means and 95% prediction intervals per grid cell. Independent repeated 80/20 cross validation demonstrates robust out-of-sample skill in both sites. For Algarve, the BK maps recover fine-scale thermal structure while preserving defensible uncertainty under severe sparsity. For La Spezia, the same framework resolves estuarine gradients at 30 m. Credible intervals widen away from observations yet remain sufficiently narrow elsewhere to guide interpretation. Satellite products are used strictly for validation on a common grid (MUR SST at 1 km resampled to 500 m, Landsat OC3 Chl at 30 m), confirming spatial fidelity and clarifying seasonal differences. Overall, the approach produces uncertainty-aware, high-resolution coastal fields from heterogeneous, sparse records, supporting reproducible EO analyses and risk-aware coastal monitoring.