Description

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 hosted by NASA's Applied Remote Sensing Training Program (ARSET) builds on a previous ARSET 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 synthetic aperture radar (SAR) satellite Earth observations for monitoring and reporting of the UN Sustainable Development Goal (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 focuses on the use of dual polarization Sentinel-1 C-SAR, 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 also covers the theory of SAR polarimetry and include a practical exercise using the Sentinel Application Platform (SNAP) and Python code written in 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), United Nations Office for Outer Space Affairs (UNOOSA), University of Stirling, Université Catholique de Louvain (UCLouvain), and the CEOS Working Group on Capacity Building and Data Democracy (WGCapD).

Prerequisites

• Fundamentals of Remote Sensing
• Introduction to Synthetic Aperture Radar
• Agricultural Crop Classification with Synthetic Aperture Radar and Optical Remote Sensing

Objectives

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

Target Audience

Course Format

• Four 2-hour parts

Sessions

Part 1: SAR Polarimetry for Agriculture (Theory and Practice)

• 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
• Q&A

Guest Instructors

• Sarah Banks (Environment and Climate Change Canada)
• Heather McNairn (Agriculture and Agri-Food Canada (AAFC))
• Laura Dingle Robertson (AAFC)

Materials

• Part 1 Presentation Slides (PDF, 12.1 MB)
• Part 1 Q&A Transcript (PDF, 0.2 MB)
• Part 1 Data

To follow along with the demonstrations in Part 1, please download and install SNAP.

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 reviewing Part 2. The solutions are provided in the Part 2 "Python Code" folder. 

SAOCOM® product - ©CONAE – (2020). All rights reserved. CONAE is the Argentine Space Agency.

RADARSAT Constellation Mission Imagery © Government of Canada (year of acquisition). RADARSAT is an official mark of the Canadian Space Agency.

Part 2: Polarimetry Practical Part 2: SAR Polarimetry with Sentinel-1, RCM, & SAOCOM Imagery for Agriculture

• 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
• Q&A

Guest Instructors

• Laura Dingle Robertson (Agriculture and Agri-Food Canada (AAFC))
• Armando Marino (University of Stirling) 

Materials

• Part 2 Presentation Slides (PDF, 6.3 MB)
• Part 2 Q&A Transcript (PDF, 0.2 MB)
• 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.

SAOCOM® product - ©CONAE – (2020). All rights reserved. CONAE is the Argentine Space Agency.

RADARSAT Constellation Mission Imagery © Government of Canada (year of acquisition). RADARSAT is an official mark of the Canadian Space Agency.

Part 3: Sen4Stat Open-Source Toolbox (Theory and Practical)

• 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
• Q&A

Guest Instructor

• Pierre Defourny (UCLouvain)

Materials

• Part 3 Presentation Slides (PDF, 20.9 MB)
• Part 3 Q&A Transcript (PDF, 0.8 MB)

Part 4: Crop-Specific Time Series Analysis for Growth Monitoring

• 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
• Q&A

Guest Instructors

• Pierre Defourny (UCLouvain)
• Fabrizio Ramoino (European Space Agency (ESA))

Materials

• Part 4 Presentation Slides (PDF, 3.1 MB)
• Part 4 Q&A Transcript (PDF, 0.1 MB)
• Part 4 Code and Data

Homework

• Homework (Due May 17, 2022)