Training Sessions

USF College of Marine Science, Marine Science Lab (MSL) Room 134, 830 1^st St SE, St. Petersburg, FL 33701

The official NASA OB.DAAC geophysical products currently available and citable are produced by the same software contained in the publicly distributed SeaDAS application. The SeaDAS software enables users on their own computers and laptops, to visualize and analyze the NASA OB.DAAC data, as well as to precisely generate the OB.DAAC products directly from the original source satellite data.

Objectives. This one day course provides an overview of the SeaDAS processing software, both how to use it and the science behind it.

The course also provides the opportunity to meet the SeaDAS development team, to give feedback on the software and future features development, as well as to get guidance on any specific applications of SeaDAS in which attendees are involved.

Target audience. All. The beginner/intermediate user can benefit from this course in learning how to produce quality science imagery and analysis from the available OB.DAAC data. The advanced user, research scientist or algorithm developer can benefit by learning what tools and parameters are available for fine-tuning the processing.

Requirements. Although not a requirement, participants are encouraged to bring their own laptops with the latest SeaDAS version installed. This is an evolving course and the latest material is posted on the SeaDAS website. The SeaDAS Forum is also a good user resource.

USF Student Center, Regetta Room, 1st Floor, 200 6^th Ave S, St. Petersburg, FL 33701

The National Oceanic and Atmospheric Administration (NOAA) CoastWatch / OceanWatch / PolarWatch program (CoastWatch, https://coastwatch.noaa.gov) provides environmental satellite products to help understand, manage and protect ocean and coastal resources. CoastWatch offers several levels of service to users, including the easy discovery and download of satellite data products, productivity tools to assist with data processing, and tutorials and training. CoastWatch provides free and open access to these ocean products through a variety of platforms. However, identifying and using satellite data products appropriate for a given application can be challenging for users outside of the satellite community, resulting in underutilized ocean satellite data in both research and operational oceanography.

Objectives. The goal of the NOAA CoastWatch program is to build capacity in using satellite data by providing background knowledge, tools, tutorials and hands-on help to users.

This tutorial session will include:

1. Introduction to the NOAA CoastWatch program and ocean satellite products
2. Overview of data services available through CoastWatch
3. Demonstration of the CoastWatch Data Portal
4. Demonstration of ERDDAP
5. Introduction of the CoastWatch Utilities
6. Hands-on tutorial on the CoastWatch Utilities

Requirements. Participants will need to bring their own laptops with CoastWatch Utilities installed prior to the start of the tutorial, following these instructions:

1. Install the CoastWatch Utilities: https://coastwatch.noaa.gov/cwn/data-access-tools/coastwatch-utilities.html#downloads
2. Download example data files listed here: https://umd.instructure.com/courses/1336441/pages/course-overview?module_item_id=11619271

All CoastWatch training materials can be found online here.

USF Student Center, Ballroom 1, 200 6th Ave S, St. Petersburg, FL 33701

This advanced to expert level short course will focus on recent changes to data access for Sentinel-3 Ocean and Land Colour Instrument (OLCI) products, and introduce new tools and approaches for performing ocean colour match-up analyses. 

Objectives. The training will:

• present the available operational and reprocessed OLCI products through the EUMETSAT Data Store.
• detail web-based (GUI) and automated (API) methods to search for and retrieve these products
• introduce the ThoMaS (Tool to generate Matchups of OC products with Sentinel-3/OLCI) toolkit for automating OLCI ocean colour data acquisition and match-up analysis.
• demonstrate the use of the tool using multiple example use cases.

Target audience. All attendees are welcome. However, the course is predominantly aimed at established and early career ocean colour researchers, operational oceanographers performing validation studies, private and public sector specialists engaged in marine monitoring activities, and previous attendees of EUMETSAT Supporting Marine Applications courses that focussed on ocean colour applications

Requirements. Although not a requirement, participants are encouraged to create a EUMETSAT Earth Observation Portal account using this link. Participants are also encouraged to explore, install and test the ThoMaS toolkit in advance.

USF Student Center, Ocean Room, 200 6th Ave S, St. Petersburg, FL 33701

The main goal of this course is to increase awareness about the SeaHawk-HawkEye Ocean Color CubeSat mission that has been in orbit since 2018, providing high quality high resolution multispectral ocean color data for free.

Objectives. Specific objectives include:

1. learning about the mission specifications, uniqueness and significance for ocean color research
2. learning how to submit image acquisition requests (they will learn about expectations, limitations and advantages)
3. learning how to access already collected data, 4) learning how to process an image with SeaDAS.

Target audience. The course is open to anyone interested in using this imagery.

Requirements. Basic remote sensing knowledge will be helpful but not necessary. A laptop with SeaDAS installed is recommended if you wish to participate in the hands-on exercise.

USF Student Center, Regetta Room, 1st Floor, 200 6th Ave S, St. Petersburg, FL 33701

Today drones are used in various applications, from infrastructure to mining and agriculture. Drones and lightweight cameras can be readily found on the market at low cost and can be easily operated without very specialized training. The collection of data above water and the retrieval of information on the water quality has lagged behind because of the higher complexity in data processing. The water case has some specific challenges that need to be tackled. For instance, water bodies often look dark on images resulting in a much lower signal-to-noise ratio compared to land applications. The water column is also a dynamic environment subjected to e.g. current or tide resulting in a rapidly changing scene under investigation.

Attendees will learn how to account for these challenges, the importance of flight protocols and important steps in the drone data processing workflow toward meaningful, quantitative information, like suspended sediments or chlorophyll concentrations of the upper water layers. You will be introduced to the MAPEO-water platform, a processing workflow in the cloud (AWS) for aerial drone images. MAPEO-water supports drone data upload towards the cloud environment with additional metadata, performs first quality checks on the raw data before the data processing starts. End products can be accessed through webservices. The outputs are georeferenced images with corrected quantitative values representative for the water column. This allows derivation of information such as, but not limited to, turbidity or chlorophyll concentrations.

The tutorial includes hands-on exercise where the attendees can work with raw and processed drone images and derive information on the water quality.

Objectives. Objectives of the tutorial are to learn:

• about the challenges and issues when acquiring drone images above water
• how to deal with these challenges from data acquisition towards data processing
• about the MAPEO-water platform

Requirements. A laptop with the following system requirements:

• Windows, Linux or MacOS
• QGIS installed
• Java installed, with minimally 1 GB (preferably 2 GB) of RAM.
  • Java version 11 or higher (17 LTS is recommended).
  • Go directly to https://adoptium.net to install the latest compatible Java version.
  • See section 10 Appendix A: Java/JDK setup (Windows), or section 11 Appendix B: Java/JDK setup (MacOS), for more information.
  • Allow internet access for Java – on first run some firewall/internal policies block access or present a pop up when launching Java for the first time, you must allow internet access for Java!

USF Student Center, Ballroom 1, 200 6th Ave S, St. Petersburg, FL 33701

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 HyperSAS packages with and without SolarTracker or pySAS platforms and hand-held TriOS Ocean Colour Radiometers.  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 advances proposed by other partners of the HyperCP team, such as those proposed by the FRM4SOC-2 team, University of Maine, etc.

Course outcomes. At the end of the course, participants will become familiar with 

1. the best practices to acquire above-water remote-sensing reflectance measurements, 
2. the necessary processing steps to obtain remote-sensing reflectance from radiometric raw data, 
3. the uncertainty sources underlying remote-sensing reflectance obtained from above-water measurements, 
4. the use of HyperCP to process such measurements (TriOS and SeaBird OCRs), 
5. how to acquire and configure the HyperCP toolkit, 
6. (optionally) how to implement HyperCP using built-for-purpose test and own datasets of raw measurements, and 
7. (optionally) the utility of the toolkit in the context of their own workflows.

Target audience. All attendees are welcome, however, the course is predominantly aimed at:

• established and early career ocean colour researchers
• operational oceanographers performing (or intending to perform) in situ above-water radiometry measurements
• private and public sector specialists engaged in marine monitoring activities

Requirements. A laptop is required for this training (e.g. Linux/MacOS/Windows, core i5, 16 GB RAM).

There are no prerequisites for this course, but to assure the execution of HyperCP during the course you are highly recommended to perform the following in advance:

• clone HyperCP git repository in advance into your local environment from NASA’s Github shared space.
• create the corresponding conda environment following the instructions in the readme file.
• (optional) run a first blind test of the code, following the readme. 

We look forward to seeing you at the course!