SAR for Landcover Applications [Advanced]

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

This webinar series will build on the knowledge and skills previously developed in ARSET SAR training. Presentations and demonstrations will focus on agriculture and flood applications. Participants will learn to characterize floods with Google Earth Engine. Participants will also learn to analyze synthetic aperture radar (SAR) for agricultural applications, including retrieving soil moisture and identifying crop types.

Learning Objectives: By the end of this training, attendees will be able to: 

  1. analyze SAR data in Google Earth Engine
  2. generate soil moisture analyses
  3. identify different types of crops   

Course Format: 

  • This webinar series will consist of two, two-hour parts
  • Each part will include a presentation on the theory of the topic followed by a demonstration and exercise for attendees. 
  • This training is also available in Spanish. Please visit the Spanish page for more information.
  • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.

Prerequisites are not required for this training, but attendees that do not complete them may not be adequately prepared for the pace of the training. 

Part One: Monitoring Flood Extent with Google Earth Engine
This session will focus on the use of Google Earth Engine (GEE) to generate flood extent products using SAR images from Sentinel-1. The first third of the session will cover the basic principles of radar remote sensing related to flooded vegetation. The remaining time in the session will be dedicated to a demonstration on how to use GEE to generate flood extent products with Sentinel-1.
Part Two: Exploiting SAR to Monitor Agriculture
Featuring guest speaker Dr. Heather McNairn, from Agriculture and Agri-Food Canada, this session will focus on using SAR to monitor different agriculture-related topics, building on the skills learned in the SAR agriculture session from 2018. The first part of the session will cover the basics of radar remote sensing as related to agriculture. The remainder of the session will focus on the use of SAR to retrieve soil moisture, identify crop types, and map land cover.

Each part of 2 includes links to the recordings, presentation slides, and Question & Answer Transcripts.

Authoring Person(s) Name: 
Erika Podest
Sean McCartney
Heather McNairn
Xianfeng Jiao
Sarah Banks
Amir Behnamian
Authoring Organization(s) Name: 
NASA Applied Remote Sensing Training Program (ARSET)
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 2.0 Generic - CC BY 2.0
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
Also available in - other languages in which the learning resource has been translated or made available other than the primary: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Agriculture data
Capacity building
Data access
Data analysis
Environmental management
Flood mapping applications
Land management
Landcover applications
Remote sensing
Satellite imagery
Soil moisture analysis
Soils data assessment systems
Soils software and data management applications
Synthetic aperture radar data (SAR)
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Education: Science and Mathematics Education
Physical Sciences and Mathematics: Earth Sciences
Physical Sciences and Mathematics: Environmental Sciences
Published / Broadcast: 
Wednesday, August 28, 2019
Publisher - organization credited with publishing or broadcasting the learning resource: 
NASA Applied Remote Sensing Training Program (ARSET)
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.
Contact Person(s): 
Brock Blevins
Contact Organization(s): 
NASA Applied Remote Sensing Training Program (ARSET)
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Data manager
Data policymaker
Early-career research scientist
Mid-career research scientist
Research scientist
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)