Abstract
The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 °C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.
Highlights
Covering around 71% of the Earth’s surface, oceans play a major role in the global climate system, [1]
Extrapolation results show a similar pattern to that presented in interpolation in the range given, which is reassuring of the consistent prediction behaviour of the neural network
This paper presents a methodology to obtain high-resolution values of sea surface salinity and temperature in the global ocean by using satellite data without any band data pre-processing or atmospheric corrections
Summary
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, and for the understanding of marine ecosystems and weather prediction, [2]. Sea surface salinity (SSS) and temperature (SST) are 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 problems that these measurements present are related to poor extended data coverage. 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
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