ARSET – Agricultural Crop Classification with Synthetic Aperture Radar and Optical Remote Sensing

For years, mapping of crop types and assessment of their characteristics has been carried out to monitor food security, inform optimal use of the landscape, and contribute to agricultural policy. High-quality crop mapping has become a requirement for most nations given its importance in national and international economics, trade, and food security, and is a major topic of interest in the domains of policy, economics, and land management. Most countries or economic regions currently and increasingly use freely available satellite imagery for crop type classification and biophysical variable assessment as they provide a synoptic view, multi-temporal coverage, and are cost-effective. Remote sensing methods based on optical and/or microwave sensors have become an important means of extracting crop information as they explain vegetation structure and biochemical properties.

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.

This webinar series is a collaboration between ARSET, Agriculture and Agri-Food Canada (AAFC), European Space Agency (ESA), and United Nations Office for Outer Space Affairs (UNOOSA).

Agenda