The colours of the ocean using multispectral satellite imagery to estimate sea surface temperature and salinity in global coastal areas, the gulf of Mexico and the UK

Abstract

Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.