# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
In those cases, alternative approaches can be tested to improve the dynamical reconstruction and eventually recover high-resolution information from existing data. Actually, many advanced computer vision algorithms can be adapted to geoscientific analysis, making the most of past efforts dedicated to addressing similar problems. One such example is given by a specific class of techniques, known as super-resolution (SR), that aims to recover high-resolution (HR) details from low-resolution images [25]. In the single-image super-resolution algorithms, deep convolutional neural networks are optimized to identify the features of an image by looking at its different channels and learning how to recover the original features from their degraded versions. This is done by combining the translation invariance and locality properties of convolutional layers with the impressive learning properties of deep architectures coupled with non-linear activations. In computer vision applications, this problem is inherently ill-posed since multiple HR images could correspond to a single LR image, and the performance of the models is highly dependent on the extensiveness of the catalogue of images it was trained with. State-of-the-art super-resolution algorithms do achieve impressive results in the processing of blurred/low-resolution photographs, though, and some attempts to adapt them to Earth observation problems have already been carried out: DeepSD (Deep Statistical Downscaling), a stacked SR-CNN algorithm based on the early three-layer model by [26], has been applied to downscale Earth system model simulations, and CNN models have been tested for sea surface temperature (SST) and wind field downscaling, with positive results [27; 28]). Here, we aim to recover high-resolution sea surface dynamical features by combining low-resolution ocean absolute dynamic topography (ADT) fields based on satellite altimetry (resolving O(100 km) wavelengths) and high-resolution SST acquired by thermal imaging spaceborne radiometers (between \(O\)(1 km) and \(O\)(10 km) depending on the sensor). This represents a different problem with respect to simple model output downscaling or single variable super-resolution, as we want to combine the information provided by channels at both original and degraded resolution in a multi-channel image, taking advantage of the physical relations among the variables we include in the different channels. In fact, even if it only implies a weak constraint, our strategy includes physical considerations in the choice of the predictor variables, building upon the role of surface water mass advection in the local evolution of the SST [29], but we also aim to exploit the repetitiveness of satellite observing system geometries. We thus consider also the temporal SST variation and the ADT mapping error as additional predictors. High-resolution observations of ocean dynamic topography through imaging sensors (thus 'natively' 2D), however, will not be available before the launch of the joint NASA/CNES/CSA ASC/UK Space Agency surface water and ocean topography (SWOT) mission, expected by the end of 2022, while more direct observations of surface currents could be provided by ESA Earth Explorer 10 Harmony mission only after 2027-2028, if present phase A, namely the design consolidation and feasibility studies, proves successful [30], or even later by SEASTAR ESA Earth Explorer 11 candidate mission [31]. As such, we rely here on an observing system simulation experiment (OSSE). In practice, we use the output of an ocean general circulation numerical model to simulate both predictor and target variables, considering that, in the future, our network can be trained directly with remotely sensed data. After training with OSSE data, our model can be applied to real altimeter-derived ADT and SST data in the test/prediction phase.