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High-resolution multispectral estimation of sea surface salinity and temperature in coastal areas

Covering around 71% of the Earth’s surface, oceans play a major role in the global climate system, [1]. The study of sea surface temperature and salinity is important to understand how oceans communicate with land and atmosphere, but also for the understanding of marine ecosystems and weather prediction, [2]. Sea surface salinity (SSS) and temperature (SST) are also relevant in the study of estuarine processes (mixing of fresh and sea water), stratification, hypoxia, organic matter, or algal blooms, among others, [3]. The SSS and SST data collection has typically been done by means of static buoys, drifters, and ship-based systems, [4]. The estimation of SST and SSS near the coast, where the detail needed might be higher due to the development of different near-shore processes and human activities, is difficult.

Different satellite missions have focused over the past decades on the measurement of oceanographic characteristics to overcome the issues that the in situ measuring techniques present. Satellites provide worldwide coverage of ocean and land phenomena, which is particularly relevant for the extraction of time series and general trends, but also for the study of localised events.

This PhD project will develop and compare techniques to extract SSS and SST from Landsat 8, [5] and Sentinel-2 data, [6, 7]. The student will focus on machine learning techniques to match satellite observations with in situ data provided by buoys, vessels and other. The methodology will extend the work developed by [7] and validate it worldwide. Moreover, the machine learning method for SSS will be extended to the Landsat 8 dataset, and compared with the Sentinel-2 approach.

Landsat 5, 7 and 8 and ASTER satellites are equipped with 100 m resolution thermal bands with moderate spectral resolution. Using atmospheric and radiative models to predict atmospheric correction (e.g. [8]), it is possible to obtain SST errors below 1 K. This methodology will be compared to results obtained by using big data and machine learning. Refinement techniques will be developed to obtain higher resolution values in coastal areas.

This PhD project will be structured as follows:

Year 1: Training in data analysis. Familiarisation with satellite data acquisition and processing techniques. Machine learning training.

Year 2: Development of data analysis algorithms. In situ data acquisition/collection and processing.

Year 3: Data analysis. Dissemination of results via peer-reviewed publications and presentations in scholarly conferences.



[1] Y. Sang, H. Karayaka, Y. Yan, N. Yilmaz and D. Souders, “Ocean (Marine) Energy,” Compr. Energy Syst, vol. 1, p. 733–769, 2018.
[2] National Oceanic and Atmospheric Administration, U.S. Department of Commerce. , “Why do Scientists Measure Sea Surface Temperature?,” 2018. [Online]. Available: [Accessed 1 June 2019].
[3] S. Chen and C. Hu, “Estimating sea surface salinity in the northern Gulf of Mexico from satellite ocean colour measurements.,” Remote Sens. Environ. , vol. 2012007, pp. 115-132.
[4] European Commission, “Copernicus Marine Environment Monitoring Service,” 2018. [Online]. Available: . [Accessed 1 June 2019].
[5] NASA, “Landsat 8,” [Online]. Available: [Accessed 22 November 2019].
[6] M. Drusch, U. D. Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, A. Meygret, F. Spoto, O. Sy, F. Marchese and P. Bargellini, “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services,” Remote Sensing of Environment, vol. 120, pp. 25-36, 2012.
[7] E. Medina-Lopez and L. Urena-Fuentes, “High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data,” Remote Sensing, vol. 11(19), 2192, 2019.
[8] N.K. Malakar, G.C. Hulley, S.J. Hook, K. Laraby, M. Cook, and J.R. Schott, “An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56(10), pp. 5717-5735, 2018.