| Abstrakt: |
Sea surface temperature (SST) is one of the essential variables of the Earth climate system. Being at the interface with the atmosphere, SST modulates heat fluxes in and out of the ocean, provides insight on several upper/interior ocean dynamical processes, and it is a fundamental indicator of climate variability potentially impacting marine ecosystems' health. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide a much better coverage than what achievable from in situ platforms, they cannot sense the sea surface under cloudy/rainy conditions. Large gaps are present even in merged multi-sensor satellite products and different statistical strategies have thus been proposed to obtain gap-free (L4) images, mostly based on the Optimal Interpolation algorithms. This kind of techniques, however, filter out the signals below the space-time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single image Super Resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging Convolutional Neural Networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100° spatial resolution starting from 1/16° data over the Mediterranean Sea. The application of this technique shows a remarkable improvement in the high resolution reconstruction, being able to capture small scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data. [ABSTRACT FROM AUTHOR] |
