Public Repository for Ocean Front Detection: a Contribution to Marine Science

Remote sensing plays a vital role in understanding and managing the oceans. This technology is used to observe and monitor the ocean's physical, chemical, and biological properties, allowing scientists to detect large-scale changes in the marine environment, such as currents, sea surface temperature, and marine life populations. This data can then be utilized to track changes in the marine environment, assess the ocean’s health, and identify areas that require conservation efforts. 

In oceanography, a front is a boundary between two distinct water masses with different properties, such as temperature, salinity, and density. These fronts are critical scientific phenomena and have a cascade of events of significant importance to the fishing, marine biology, shipping, and logistics industries. For example, upwelling fronts are typically sites of strong vertical movements that bring cold, nutrient-rich water to the euphotic zone. This phenomenon is a primary factor controlling phytoplankton growth, which is the foundation of the marine food chain. It can also influence the concentration of floating marine litter, plastic, and other human-made objects.  

Our work comprised the search, revision, and implementation of three algorithms to detect oceanic fronts through the model and satellite sea surface temperature (SST) data. The chosen algorithms, Canny, Belkin O’Reilly, and Cayula-Cornillon, use SST data to provide historical frontal probability maps and near-real-time daily fronts identification. These algorithms were aggregated, simplified, and adapted for use in the Python programming language. 

Establishing free and open repositories helps to spur research, innovation, and development. That’s why we have created the following public repository (https://github.com/CoLAB-ATLANTIC/JUNO), which includes a set of notebooks outlining the step-by-step process for obtaining frontal probability or daily fronts maps using each of the three algorithms. The method consists of downloading the data (MUR or CMEMS), applying the algorithms, and saving the results in a NetCDF file. 

This repository will help scientists, researchers, and business people understand the ocean’s dynamics and make front detection more accessible. Through this repository, our work is making strides to advance the oceanography field and make ocean research more efficient and available to everyone.