Implementation on the Cloud
In this project, we propose to extract bathymetry from a single Sentinel-2 dataset, exploiting the time lag that exists between two bands on the focal plane of the Sentinel 2 sensor. To tackle the issue of estimating bathymetry using two Sentinel-2 bands acquired quasi simultaneously, we developed a method based on cross-correlation analysis that exploits the spatial and temporal characteristics of the Sentinel 2 dataset to jointly extract both ocean swell celerity (c) and wavelengths λ. We wrote the code in Python (V3) in reason of its portability and for facilitating the implementation on cloud processing platforms.
“Implementation of the algorithm on CREODIAS platform to facilitate direct access to Copernicus Sentinel data and sufficient computing resources.”
Assisted by Cloudferro, accessed the CREODIAS platform (www.creodias.eu). There, we could find the whole offer for Computing&Cloud and EO collection database. They provide cloud computing (virtual machines, operating systems) and storage (standard HDD and fast – SSD) services. Our Python code is now implemented there, in an encrypted environment. It runs close to the Sentinel archive. We used this configuration for a wide area demonstration. So, there is potential to upscale in the future.