Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing

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