NASA offers a range of trainings for learners at all skill levels.
NASA offers live, instructor-led training series led by our experts and guest instructors featuring a mix of detailed explanations, case studies, demonstrations, exercises and a Q&A session. Those who attend all of the live webinar sessions and complete the assigned homework are eligible for a certificate of completion after the training. Recordings and training materials are available on the training page after the webinar concludes. Once the live training is complete, certificates of completion cannot be issued if a participant was unable to attend the live sessions.
Training length range: 2-4 part series, 60-90 minutes each, delivered over one or more weeks.
NASA offers asynchronous, self-paced training through our learning management system. Check back frequently as we build our catalog of flexible, interactive training courses that complement our live webinar offerings. Trainings features a mixture of lectures, demonstrations, and hands-on exercises. All online, self-paced trainings contain a final learning assessment that is available upon completion of all training modules. A certificate of completion is sent to participants who complete all modules and receive a passing score on the assessment.
Training length range: 60-180 minutes, varies by training.
NASA partners with organizations to host in-person trainings with curricula tailored to specific communities. Trainings feature a mix of detailed explanations, demonstrations, and hands-on exercises and culminate in a small-group case study exercise and presentation. Attendees learn how to access, interpret, and apply NASA data on local and global scales, with an emphasis on real-life case studies. Due to the in-depth nature of these sessions, attendance is often limited to about 50 participants.
Training length range: 2-4 days, 8 hours per day.
This level answers a question like, ‘How do satellites work?’
Prior Knowledge of Remote Sensing: None
Example concepts: How remote sensing works, characteristics of satellites and sensors.
Example skills: Use NASA Worldview to find and view true color imagery for a specific time and location, know where to go to find additional information and resources for a given application.
This level answers a question like, ‘What can satellite data tell me about floods?’
Prior Knowledge of Remote Sensing: Fundamentals level of knowledge
Example concepts: How remote sensing can be used for a given application, which sensors are available and commonly used for this application, and the strengths and limitations of remote sensing for a given application.
Example skills: Determine appropriate sensor for a given application, use NASA Worldview or other web portals or webtools to find and relevant datasets for a specific time and location, use web portals or webtools to find and visualize data for a specific time and location.
This level answers a question like, 'Which satellite-based flood mapping product should I use?'
Prior Knowledge of Remote Sensing: Introductory level of knowledge
Example concepts: Describe specific methodology for data analysis, use available quality assurance information to filter data, create a custom plot from a data product, identify strengths and limitations of available data products
Example skills: Choose most appropriate data products for a given application, use NASA Earthdata to find and download available data products for a specific time and location, apply quality control steps to create simple plots (maps, time-series) from a downloaded data product
This level answers a question like, 'How do I use satellite data to make a flood map?'
Prior Knowledge of Remote Sensing: Intermediate level of knowledge
Example concepts: Understand the method to create a new derived data product from an existing product, describe why and how to combine multiple data products together (e.g., satellite and model data)
Example skills: Use code-based data analysis techniques, data post-processing and visualization, adapt code to region and time period of interest, synthesize information from different datasets