Monitoring crop growth is important for assessing food production, enabling optimal use of the landscape, and contributing to agricultural policy. Remote sensing methods based on optical and/or radar sensors have become an important means of extracting information related to crops. Optical data is related to the chemical properties of the vegetation, while radar data is related to vegetation structure and moisture. Radar can also image the Earth’s surface regardless of almost any type of weather condition.
This three-part, advanced training builds on previous ARSET agricultural trainings. Here we present more advanced radar remote sensing techniques using polarimetry and a canopy structure dynamic model to monitor crop growth. The training will also cover how to apply machine learning methods to classify crop type using a time series of Sentinel-1 & Sentinel-2 imagery. This series will include practical exercises using the Sentinel Application Platform (SNAP) and Python code written in Python Jupyter Notebooks, a web-based interactive development environment for scientific computing and machine learning.
This webinar series is a collaboration between ARSET, Agriculture and Agri-Food Canada (AAFC), European Space Agency (ESA), University of Stirling, University of Ljubljana, and the CEOS Working Group on Capacity Building & Data Democracy (WGCapD).
Relevant UN Sustainable Development Goals:
- Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture
- Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality
Course Dates: April 4, 6, and 11, 2023
Times and Registration Information:
English Session: 10:00-12:30 EDT (UTC-4): https://go.nasa.gov/3DQqVrV
Spanish Session: 13:00-15:30 EDT (UTC-4): https://go.nasa.gov/40JSywn
Learning Objectives: By the end of this training, attendees will be able to:
- Monitor crop growth with polarimetric time series SAR data from Sentinel-1
- Examine crop growth using a canopy structure dynamic model and time series of Sentinel-1 imagery
- Classify crop type using a time series of radar and optical imagery (Sentinel-1 & Sentinel-2)
Audience: This webinar series is intended for local, regional, federal, and non-governmental organizations from agriculture and food security related agencies to use radar and optical remote sensing applications in the domain of agriculture for crop mapping and monitoring.
Course Format: Three, 2.5-hour parts
Retweet option (English): https://twitter.com/NASAARSET/status/1625233610335723527