Sea Surface Temperature estimation from satellite observations and in-situ measurements using multifidelity Gaussian Process regression

Monitoring ocean acidification (OA) is very important for healthy fisheries and marine ecology systems, especially in coastal areas, like the Boston Harbor and the Massachusetts Bay areas. This problem is particularly exacerbated in this region as it is related to the so-called reduced “buffering capacity” of the New England coastal waters. Sea Surface Temperature (SST) is one of the significant OA indicators; however, accurate in-situ data collection in a vast area is very costly while daily satellite observations provide only coarse information along the coastal areas. To this end, best estimation and uncertainty bounds of SST fields can be obtained from data fusion using Gaussian Process regression (GPR). Using single- and multi-fidelity GPR, confidence limits of coastal data can be established and furthermore a comprehensive multi-fidelity monitoring strategy that integrates satellite observation and in-situ measurements can be designed.