Mapping crop types and assessing their characteristics is critical for monitoring food production, enabling optimal use of the landscape, and contributing to agricultural policy. Remote sensing methods based on optical and/or microwave 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 four-part, advanced training builds on the previous ARSET agricultural training. Here we present more advanced radar remote sensing techniques using polarimetry to extract crop structural information. We also present Sen4Stat – an open source system demonstrating the potential of optical and SAR satellite Earth observations for monitoring and reporting of the SDG targets related to agriculture. Sen4Stat also combines Earth observation data with national statistical data sets and surveys to support National Statistical Offices in the uptake of satellite Earth observations for agricultural statistics.
This series will focus on the use of dual polarization C-band SAR from Sentinel-1, fully polarimetric C-band SAR from the RADARSAT Constellation Mission (RCM), fully polarimetric L-band SAR from SAOCOM (SAtélite Argentino de Observación COn Microondas), and optical imagery from Sentinel-2 to map and monitor crop types and assess their biophysical characteristics. This series will also cover the theory of SAR Polarimetry and include a practical exercise using the Sentinel Application Platform (SNAP) and Python code written in JupyterNotebooks, 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), United Nations Office for Outer Space Affairs (UNOOSA), University of Stirling, Université Catholique de Louvain (UCLouvain), and the CEOS Working Group on Capacity Building & Data Democracy (WGCapD).
This training is also available in Spanish.
By the end of this training attendees will be able to:
- Explain the theory behind SAR Polarimetry, especially as related to crop characteristics
- Generate polarimetric parameters using open source imagery/software and perform a time series analysis of crop growth
- Identify how Sen4Stat can support National Statistical Offices in the uptake of satellite Earth observations for agricultural statistics
- Perform a time series analysis of crop types using Sentinel-2 derived LAI index
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.
- Four, 2.5-hour sessions
- Training sessions will be held on Tuesdays from April 12 to May 3
- The morning session will be presented in English: 10:00 AM – 12:30 PM ET
- The afternoon session will be presented in Spanish: 13:00 – 15:30 PM ET
Trainers: Sarah Banks (Env. & Climate Change Canada), Heather McNairn, & Laura Dingle Robertson (AAFC)
- SAR Polarimetry Theory
- Polarimetry Practical Part 1: Intensity Derived Parameters for Agriculture Monitoring
- Generate Intensity parameters such as Span, Radar Vegetation Index, co-pol and cross-pol ratios derived from Sentinel-1 using SNAP (practice)
If you would like to follow along with the demonstrations in Part 1, please:
If you would like to familiarize yourself with the code used in Part 2, you may try to comprehend and complete the missing sections of code in the Part 2 Practice Code. We recommend that you attempt this before attending Part 2. The solutions are provided in the Part 2 "Python Code" folder.
Trainers: Laura Dingle Robertson (AAFC) & Armando Marino (Univ. of Stirling)
- Generate pseudo-polarimetric parameters derived from SLC dual polarimetric Sentinel-1 using SNAP and PolSARpro (cont.)
- Analyze a time series of fully polarimetric RCM and SAOCOM images using Python Jupyter Notebooks to identify crop characteristics with different polarimetric observables
- Presentation Slides
- Q&A Transcript
- Part 2 Code and Data: We advise that you download each Sentinel data measurement file individually.
- For those interested in working with the original CONAE SAOCOM files, refer to the SAOCOM 1A Test Products. Product 11 was used in this ARSET training—Area around Córdoba, Córdoba Province, Argentina.
Trainers: Pierre Defourny (UCLouvain)
- Overview of Sen4Stat open source system to process Sentinel-1 and Sentinel-2 data at country level
- Explore how Sen4Stat combines Earth observation data with national statistical data sets and surveys for agricultural statistics
- Crop type classification combining SAR and optical time series
Trainers: Pierre Defourny (UCLouvain) & Fabrizio Ramoino (ESA)
- Retrieval of crop specific LAI time series from Sentinel-2 using SNAP
- Quality control of the LAI time series using QGIS
- Time series analysis of crop types using Sentinel-2 derived LAI index
- Anomalies detection and intra-parcel heterogeneity assessment for different agricultural fields using optical data using Python Jupyter Notebooks