SHORT COURSES AND WORKSHOPS
The registration system for workshops and short courses has closed. If you have questions or would like to request to be added to the waitlist, please contact Jenny Ramarui, Conference Coordinator ([email protected])
SHORT COURSE
Training on HyperCP: A Best Practice Community Processor for Above-Water Radiometry
WHEN: Sunday, October 6, 09:00 – 13:00
WHERE: Palacio de Congresos, Fuerteventura Room
LED BY: Dirk Aurin (NASA), Agnieszka Bialek (UK National Physical Laboratory), Juan Ignacio Gossn (EUMETSAT), Hayley Evers-King (EUMETSAT), and Ben Loveday (Innoflair UG)
HyperCP (HyperInSPACE Community Processor) is a toolkit designed to provide automated processing of above-water, hyperspectral ocean color radiometry data using state-of-the-art methods and protocols for quality assurance, uncertainty estimation/propagation, sky/sunglint correction, convolution to satellite wavebands, and ocean color product retrieval. This short course, designed for advanced-to-expert level attendees, will focus on teaching how to operate HyperCP. Data outputs are formatted to text files for submission to the NASA SeaBASS and OCDB and saved as comprehensive HDF5 records (Hierarchical Data Format version 5) with automated processing reports. The package is designed to facilitate rigorous, flexible, and transparent data processing for the ocean color remote sensing community. Radiometry processed in HyperCP are used for water optical characterization, ocean color product retrieval algorithm development, and orbital platform validation.
Currently, HyperCP supports Sea-Bird Scientific HyperOCR and TriOS RAMSES above water radiometry packages with and without autonomous data collection platforms such as SolarTracker and pySAS. HyperCP is an open science, open-source collaboration (involving NASA, EUMETSAT, the FRM4SOC-2 Project, the University of Victoria and University of Maine). HyperCP stems from NASA’s HyperInSPACE, initially designed to adhere to the best practices detailed in the legacy NASA Ocean Optics Protocols (Mueller et al., 2003) and to incorporate the advances defined in the IOCCG Optical Radiometry Protocols (Zibordi et al., 2019). In addition, HyperCP also allows incorporation of advancements proposed by other partners of the HyperCP team, such as those proposed by the FRM4SOC-2 team, University of Maine, etc. Participants may wish to register for the THoMaS course taking place in the afternoon, which will show how to matchup outputs from HyperCP with satellite data and conduct validation according to community protocols, also developed in the frame of FRM4SOC-2.
SHORT COURSE
Introduction to Lidar for Ocean Color Applications
WHEN: Sunday, October 6, 09:00 – 13:00
WHERE: Palacio de Congresos, Tenerife Room
LED BY: Cédric Jamet (Université du Littoral-Côte d’Opale -Laboratoire d’Océanologie et de Géosciences – LOG), Davide Dionisi (Istituto di Scienze Marine – ISMAR-CNR), Kelsey Bisson (NASA), and Brian Collister (NASA)
Passive remote sensing of the ocean color 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. To this day, lidar has not gained a lot of attention from the ocean color community, 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 scattering layers or the seawater’s inherent optical properties and biogeochemical parameters over the vertical up to 60 meters from airplanes or ships of opportunities.
To increase lidar literacy for our ocean community, it is necessary to provide comprehensive background and training on the technique but also on the ways to process the lidar signal. This is the aim of this course. 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. The content of the course is:
- Fundamentals of lidar (1h): description of the instrument, lidar equation, description of the different types of lidar, description of lidar algorithms
- Oceanic applications (1h): airborne, shipborne and space-borne. Scattering layers, estimation of chl-a and POC. Polar regions. Profiles of IOPs
- Practical exercise (2h): 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 part requires a laptop and programming skills. Most of the codes will be in python.
SeaDAS Workshop: Focus on PACE Data
WHEN: Sunday, October 6, 09:00 – 13:00
WHERE: Palacio de Congresos, Gran Canaria Room
LED BY: Daniel Knowles and Bing Yang (NASA)
This workshop teaches participants how to use SeaDAS to visualize, analyze, and process satellite data, with specific focus on PACE data.
We will explore what tools SeaDAS has to offer:
- Show how to load PACE data and understand it.
- Show how to use tools, such as spectrum view, angular view, animation, and statistics to analyze hyperspectral data.
- Understand how to reproject and aggregate data from different scenes.
- Understand the science processing levels and how to use SeaDAS to process files through different data levels.
- Understand how to create true color imagery.
- How to customize SeaDAS to improve performance for large files.
SHORT COURSE
Lagrangian Structures, Satellite Imagery, and Prediction of the Evolution of Harmful Algae Blooms
WHEN: Sunday, October 6, 09:00 – 13:00
WHERE: Palacio de Congresos, Lanzarote Room
LED BY: Ana Mancho (CSIC-Instituto de Ciencias Matemáticas), Guillermo Garcia Sanchez (Digital Earth Solutions), Alejandro Garcia Mendoza (Instituto ECOAQUA-Universidad de Las Palmas de Gran Canaria), and Antonio Ramos (Universidad de Las Palmas de Gran Canaria)
Harmful algal blooms (HABs) are an increasingly growing environmental and public health problem due to their ability to rapidly proliferate in aquatic ecosystems, often fueled by nutrient pollution from human activities. These blooms can release toxins harmful to aquatic life, leading to fish kills and ecosystem disruption. Optical imaging can detect HABs at very high and moderate ground resolution and aid in their management. Recently, complementary services predicting the evolution of these blooms on the ocean surface have emerged as crucial for mitigation efforts. In this framework this course comprises two parts: The first part delves into fundamental mathematical concepts underpinning the prediction of the evolution of HABs, supported by COPERNICUS and other available products. In the second part, the SPOT-Algae platform is introduced. This platform assists interested stakeholders, such as authorities and environmental companies, in estimating the evolution of algae patches detected on the ocean surface through satellite imagery or low-altitude flight cameras. The workshop will include presentations, discussions, and hands-on activities introducing SPOT-Algae, covering data inputs, available features and outputs, practical usage tips, and limitations. Participants will have access to a demo version of SPOT-Algae.
SHORT COURSE
ThoMaS: Extracting the Satellite Data You Need
Optimised Data Access and Matchup Workflows for Pan-Sensor Validation of Satellite Ocean Colour Products
WHERE: Palacio de Congresos, Fuerteventura Room
LED BY: Juan Ignacio Gossn, Hayley Evers-King, and Ben Loveday (EUMETSAT)
The ThoMaS (Tool to generate Matchups of OC products with Sentinel-3/OLCI) toolkit provides a comprehensive set of routines and methods for extracting matchups and validating satellite ocean colour products with bio-geophysical in situ measurements. While originally developed to support the standard suite of EUMETSAT Sentinel-3 OLCI products, it has since been expanded to support a broader range of sensors and processors. This short course, designed for advanced-to-expert level attendees, will focus on learning how to configure and use the ThoMaS package to extract matchups for your needs. Simple workflows showing how to extract matchups for intercomparisons with other data sources will be presented. Advanced workflows to support validation activities will include: preparing in situ data in NASA SeaBASS or EUMETSAT OCDB format, accessing satellite data from remote and local archives, recommended match-up protocols, application of corrections and the outputs of match-up analyses. If available, participants are encouraged to bring their own in situ data, and, where possible, we will work with you to co-develop ThoMaS validation approaches that support your requirements. This session will be supported by a preparatory webinar where we will discuss the installation and configuration of the toolkit, to better support its use in the classroom. This activity also represents a natural follow-on to the morning session on processing of hyperspectral in situ data using Hyper-CP, as data from this can be used as inputs to the ThoMaS toolkit.
In the meantime, give ThoMaS a try!
Machine Learning for Ocean Studies
WHEN: Sunday, October 6, 13:30 – 17:30
WHERE: Palacio de Congresos, Tenerife Room
LED BY: Roy El Hourany and Cedric Jamet (Université du Littoral-Côte d’Opale Laboratoire d’Océanologie et de Géosciences – LOG)
The proposed workshop brings together machine learning and ocean studies. Its focus is on providing clear guidelines for standardizing and harmonizing methodologies to ensure data integrity and quality.
The workshop will mix theory with hands-on practice. At the start, participants will dive into a quick, informative flash course. These sessions will cover key machine learning algorithms relevant to ocean studies, explaining both the theory behind them and how they’re used in practice.
Then, we’ll move into a practical session, making up the bulk of the workshop. Here, participants will deal with satellites and in-situ datasets, mainly on the topic of phytoplankton community structure from space. Through this exercise, they’ll learn how to treat their data and apply machine learning algorithms effectively.
An important part of the workshop will be tackling data treatment methods, followed by unsupervised and supervised classification problems. Participants will explore techniques, from clustering algorithms for unsupervised learning to classification models for supervised learning. Some expertise with coding languages is highly appreciated (MATLAB, Python). Each participant should have their own PC with the platform of their choice. The hands-on materials will be provided during the session.”
SHORT COURSE
Working with Aquaverse: Water Quality and Uncertainty Products for Inland and Coastal Waters
WHERE: Palacio de Congresos, Gran Canaria Room
LED BY: Ryan O’Shea, Arun Saranathan, and Akash Ashapure (Science Systems and Applications, Inc., and NASA Goddard Space Flight Center)
This course will cover the fundamentals of working with Aquaverse, a machine-learning-centered processing workflow to generate downstream products from remote sensing observations. Aquaverse is an aquatic inversion scheme for the estimation of biogeochemical parameters and inherent optical properties, as well as their associated prediction uncertainties, from remotely sensed optical data over inland and coastal waters. These products will be available for download, for global inland and coastal waters, from the Ocean Color Instrument (OCI) aboard the PACE mission. Alternatively, similar products can also be estimated for other multi- and hyperspectral missions, including Sentinel 2’s Multispectral Instrument (MSI), Landsat-8’s Operational Land Imager (OLI), the Ocean Land Colour Instrument (OLCI), the Earth Surface Mineral Dust Source Investigation (EMIT), and the PRecursore IperSpettrale della Missione Applicative (PRISMA), via user-installable machine learning models.
First, the course will introduce use cases of Aquaverse products via maps, scatterplots, and timeseries covering dynamic inland and coastal regions, including Lake Erie, the Chesapeake Bay, and small ecosystems across Europe. Second, the course will lead users through downloading, loading, and plotting of the available online products. Third, we will demonstrate installation and example scripts for the user-installable machine-learning model, termed mixture density network (MDN), intended for alternative multi- and hyperspectral sensors. Finally, there will be time for users to interact with course instructors to 1) learn the fundamentals of MDNs, 2) install the MDN, 3) run example scripts, 4) discuss potential projects, applications, and user needs. Users are encouraged to bring their own laptops to the workshop to install the MDN toolbox (available at: https://github.com/STREAM-RS/STREAM-RS).
SHORT COURSE
Best Practices for the Collection and Analysis of Optical Data Collected with Inline (Flow-Through) Systems
WHEN: Sunday, October 6, 13:30 – 17:30
WHERE: Palacio de Congresos, Lanzarote Room
LED BY: Emmanuel Boss, Guillaume Bourdin, Patrick Gray, and Nils Haentjens (University of Maine)
This short course will:
- Review the best practices associated with building an inline system.
- Introduce the open-source software Inlinino to record data from instruments.
- Introduce the open-source software InlineAnalysis to process and quality control such recorded data.
Questions?
Contact Jenny Ramarui,
Conference Coordinator,
at [email protected]
or (1) 301-251-7708