Training Courses

Trainers: Dirk Aurin (NASA), Juan Gossn (EUMETSAT) assisted by Agnieszka Biale, Raphael Mabit, Ashley Ramsay

In this theoretical-practical session, we will discuss HyperCP, an open-source software designed for the automated processing of in situ hyperspectral ocean color radiometric data from above-surface measurements. HyperCP employs methods and protocols agreed upon by the Ocean Color (OC) community (especially IOCCG Protocols Ch. 3), including standard quality control procedures, uncertainty estimation and propagation, correction for skyglint and sunglint effects, convolution to multispectral satellite bands, and the estimation of OC products derived from water reflectance.

This course will briefly introduce basic above water radiometry principles and then primarily focus on practical aspects of using HyperCP. The remote sensing reflectance (and other optical properties) obtained through HyperCP are provided in both SeaBASS and HDF5 formats and are reported along with automated processing reports and plots. The software package is designed to facilitate rigorous, flexible, and transparent data processing for the OC community. Radiometric data processed with HyperCP are used for the optical characterization of water bodies, the development of OC product algorithms, and the validation of satellite-derived OC products.

Currently, HyperCP supports the processing of surface radiometry data obtained with Sea-Bird Scientific HyperOCR and TriOS RAMSES sensors, with or without robotic platforms such as SolarTracker and pySAS. HyperCP is an open science and open-source collaboration involving NASA, EUMETSAT, the FRM4SOC-2 project, the University of Victoria, and the University of Maine. Integration of the DALEC and So-Rad platforms is ongoing with collaboration from NOAA and PML, respectively. HyperCP is derived from NASA’s HyperInSPACE, originally designed to adhere to best practices outlined in NASA’s bio-optical in situ measurement protocols (Mueller et al., 2003) and to incorporate advancements defined by the International Ocean Colour Coordinating Group (IOCCG) protocols.

Participant prerequisites or requirements

Participants must bring a laptop and, if possible, test the software and review its README in advance. HyperCP, official repository.

Trainers: Juan Gossn (EUMETSAT) assisted by Dirk Aurin, Agnieszka Biale, Raphael Mabit, Ashley Ramsay

In this theoretical-practical session, we will discuss the ThoMaS tool (Tool to generate Matchups of OC products with Sentinel- 3/OLCI). ThoMaS provides a comprehensive set of routines and methods for extracting match-ups and validating satellite- derived Ocean Color products using in situ measurements. Although it was originally developed to validate standard Sentinel- 3 OLCI products from EUMETSAT, it has been expanded to support a broader range of sensors and processors.

We will focus on learning how to configure and use the ThoMaS package to extract match-ups according to the specific needs of the course participants. Simple workflows will be presented to demonstrate the extraction of match-ups, while more advanced workflows supporting validation activities will include:

• Preparation of in situ data in SeaBASS format
• Access to satellite data from remote and local repositories
• Recommended match-up protocols
• Application of BRDF correction
• Generation of statistical analyses and graphical representations of match-up results

If time permits, participants are encouraged to bring their own in situ data to perform match-ups using ThoMaS. This activity also serves as a natural continuation of the morning session on in situ hyperspectral data processing with HyperCP, as the data processed in that session can be used as inputs for the ThoMaS tool.

Participant prerequisites or requirements

Participants must bring a laptop and, if possible, test the software and review its README in advance. ThoMaS, official repository.

Trainers: Cédric Jamet (U. Littoral), Kelsey Bisson (NASA)

Passive remote sensing of the ocean colour fundamentally changed our vision of the distribution of the phytoplankton and other optically active constituents. However, these observations have limitations that can be overcome using the active remote sensing technique called LIDAR (light detection and ranging). This technique has led to many ocean discoveries despite not having an ocean-optimized LIDAR satellite in orbit. Recent pioneering work in 2013 provided global images of phytoplankton from space-borne LIDAR for the first time. Since then, oceanic applications using LIDAR have developed at a high speed for the detection of seawater’s inherent optical properties and biogeochemical parameters over the vertical up to 60 meters, from airplanes or ships of opportunities.

This course aims to increase LIDAR literacy in the ocean colour community, by providing a comprehensive background and initial training on LIDAR techniques, and the ways to process the LIDAR signal. Fundamentals of the LIDAR will be provided, followed by examples of oceanic applications. A practical exercise will explain how to process airborne and satellite LIDAR data for the estimation of apparent and inherent optical properties of seawater.

Course content includes:

• Fundamentals of LIDAR: description of the instrument, LIDAR equation, description of the different types of LIDAR, description of LIDAR algorithms
• Oceanic applications: airborne, shipborne and space-borne. Scattering layers, estimation of chl-a and POC. Polar regions. Profiles of IOPs
• Practical exercise: data processing of spaceborne (IceSat-2) and airborne LIDAR data for the estimation of profiles of IOPs and chl-a. Where do we find the data? What do they look like? What are the issues to deal with? Which algorithms?

The practical will be a major focus of the training session.

Participant prerequisites or requirements

The practical requires a laptop and programming skills. Most of the codes will be in Python.