Poster Session 3, Wednesday, October 5, 16:00–18:00
Remote sensing estimation of sea surface temperature and salinity from hyperspectral ground radiometry and multispectral satellite ocean colour imagery
Sea surface salinity (SSS) and sea surface temperature (SST) are important measures of ocean health. Temperature and salinity can be linked to ecosystem productivity, such as acidification or eutrophication and help to stratify different marine zones. Salinity can also be used as a measure of land influence in the ocean. Satellite-derived ocean colour provides the opportunity to process data at a higher temporal and spatial resolution than in-situ monitoring, with its pointwise spatial data limitation, or that of microwave remote sensing, ocean colour satellites having pixel resolution on the 10s to 100s metres scale compared to microwave scales in the range of kilometres. The method proposed in this research uses temporally and spatially matched water data of SSS and SST in Patagonia, with hyperspectral radiance. First, ground level hyperspectral values are used to train a linear regression model. The model is then able to learn the relationship between the spectral input and SSS/SST values and accurately estimates SSS and SST in test regions using just the spectral signature as input. Then, neural network algorithms are trained to be able to learn more complex relationships and predict values in different optical water types. This relationship between sea surface properties and spectral signatures is then tested with multispectral satellite images matched with in-situ data in case study locations. The model is able to estimate salinity and temperature to within 1PSU and 1 degree centigrade respectively.
Encarni Medina-Lopez, Edinburgh University, [email protected]
Tiago Silva, Cefas, [email protected]
Evangelos Spyrakos, Stirling University, [email protected]