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

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Title: Spatial Densification of Coastal Sea Surface Temperature and Chlorophyll via Bayesian Kriging.
Authors: Vassilis, Andronis1 (AUTHOR) andronis@central.ntua.gr, Vassilia, Karathanassi1 (AUTHOR)
Source: Remote Sensing. Mar2026, Vol. 18 Issue 5, p675. 34p.
Subjects: Kriging, Uncertainty (Information theory), Remote sensing, Ocean temperature, Spatial resolution, Geographic spatial analysis, Chlorophyll, Environmental monitoring
Geographic Terms: Portugal, Algarve (Portugal), Italy
Abstract: Highlights: What are the main findings? Bayesian kriging (BK) densifies sparse SST and chlorophyll-a observations into 30–500 m coastal fields with calibrated uncertainty (posterior mean and 95% prediction intervals). Repeated 80/20 cross validation shows consistent out-of-sample skill and interval coverage close to the nominal 95%. Independent grid-to-grid benchmarking against satellite products (MUR SST for Algarve, Landsat-8 OC3 chlorophyll for La Spezia) is performed on the native prediction grids, with satellite fields used strictly for external validation (i.e., not used as model inputs in the baseline experiments). A matched sparsity baseline using ordinary kriging (OK) complements this evaluation, enabling a direct BK–OK comparison that reports both point prediction skill and uncertainty calibration. What is the implication of the main finding? The workflow yields reproducible, georeferenced maps and uncertainty layers that are directly usable for coastal monitoring and risk-aware decision making. The workflow is predictor-free and variable-agnostic in formulation (applicable to scalar coastal fields without auxiliary covariates), demonstrated here on SST and Chl across two coastal pilots. Boader generalization to other regimes is a natural next step when suitable data are available. 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. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Spatial Densification of Coastal Sea Surface Temperature and Chlorophyll via Bayesian Kriging.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 5, p675. 34p.
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  Data: <searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+temperature%22">Ocean temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+spatial+analysis%22">Geographic spatial analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Chlorophyll%22">Chlorophyll</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Portugal%22">Portugal</searchLink><br /><searchLink fieldCode="DE" term="%22Algarve+%28Portugal%29%22">Algarve (Portugal)</searchLink><br /><searchLink fieldCode="DE" term="%22Italy%22">Italy</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Bayesian kriging (BK) densifies sparse SST and chlorophyll-a observations into 30–500 m coastal fields with calibrated uncertainty (posterior mean and 95% prediction intervals). Repeated 80/20 cross validation shows consistent out-of-sample skill and interval coverage close to the nominal 95%. Independent grid-to-grid benchmarking against satellite products (MUR SST for Algarve, Landsat-8 OC3 chlorophyll for La Spezia) is performed on the native prediction grids, with satellite fields used strictly for external validation (i.e., not used as model inputs in the baseline experiments). A matched sparsity baseline using ordinary kriging (OK) complements this evaluation, enabling a direct BK–OK comparison that reports both point prediction skill and uncertainty calibration. What is the implication of the main finding? The workflow yields reproducible, georeferenced maps and uncertainty layers that are directly usable for coastal monitoring and risk-aware decision making. The workflow is predictor-free and variable-agnostic in formulation (applicable to scalar coastal fields without auxiliary covariates), demonstrated here on SST and Chl across two coastal pilots. Boader generalization to other regimes is a natural next step when suitable data are available. 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18050675
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      – Code: eng
        Text: English
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        PageCount: 34
        StartPage: 675
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      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Uncertainty (Information theory)
        Type: general
      – SubjectFull: Remote sensing
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      – SubjectFull: Ocean temperature
        Type: general
      – SubjectFull: Spatial resolution
        Type: general
      – SubjectFull: Geographic spatial analysis
        Type: general
      – SubjectFull: Chlorophyll
        Type: general
      – SubjectFull: Environmental monitoring
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      – SubjectFull: Portugal
        Type: general
      – SubjectFull: Algarve (Portugal)
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      – SubjectFull: Italy
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      – TitleFull: Spatial Densification of Coastal Sea Surface Temperature and Chlorophyll via Bayesian Kriging.
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            NameFull: Vassilis, Andronis
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              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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