This five-part, intermediate webinar series will focus on the use of synthetic aperture radar (SAR) from Sentinel-1 and/or optical imagery from Sentinel-2 to map crop types and assess their biophysical characteristics. The webinar will cover a SAR and optical refresher along with pre-processing and analysis of Sentinel-1 and Sentinel-2 data using the Sentinel Application Platform (SNAP) and Python code written in JupyterLab, a web-based interactive development environment for scientific computing and machine learning. The webinar will also cover an operational roadmap for mapping crop type, including best practices for collecting field data to train and validate models for classifying crops on a national level. The final session of this series will cover crop biophysical variable retrievals using optical data.

By the end of this training attendees will be able to identify:

• The information content in passive (optical) and active (microwave) remote sensing data and how it relates to agricultural parameters
• The characteristics of passive and/or active sensors used in operational crop mapping and biophysical retrievals
• Where to acquire satellite data for conducting agricultural analysis
• The steps for pre-processing optical and radar imagery
• The steps to classify crop types using supervised and unsupervised techniques and explain the difference among classifiers (e.g., Decision Tree and Random Forest)
• Best practices for collecting field-based training data
• Retrieval of crop biophysical variables

The course is organized in five 2.5-hour sessions.

Live sessions took place on 5-19 October 2021, but recordings are available.

The course is also available in Spanish (see 301).