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Radar’s Potential to Estimate Crop Bio-Physical Parameters & Beyond


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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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Radar’s Potential to Estimate Crop Bio-Physical Parameters & Beyond

  1. 1. Radar’s Potential to Estimate Crop Bio-Physical Parameters & Beyond Jiali Shang1, Heather McNairn1, Catherine Champagne1, Xianfeng Jiao2 1Agriculture 2Natural and Agri-Food Canada, Ottawa, ON, Canada Resources Canada, Ottawa, ON, Canada December 14-15, 2013, Mexico City, Mexico
  2. 2. Monitoring agriculture production is a global issue • Rising national, regional and global challenges in food supply – Food production must double by 2050 to meet global food demand – Competing land use and increasing climate fluctuations pose challenges to food production • Sound policies and risk management strategies require appropriate, timely and cost-effective geospatial information • Earth observing (EO) satellites offer an efficient means to acquire accurate information on the locations, extent and conditions of crops • Many new satellites are scheduled to launch and will provide viable means for operational application
  3. 3. Agriculture in Canada • Canada’s Agricultural landscape is large and complex 67.6 million hectares of total farm area across diverse climate and soil zones – Average farm 150 hectares in crops – • Agriculture is an important sector – Employs 2.2% of Canada's total population – 8.1% of total GDP – 6th largest exporter of agricultural products in the world – Contribute 20% of the total world exports of wheat & canola
  4. 4. Earth Observation research & development in Canada • Canada has rich expertise in EO research & development • More recently EO has been used to offer operational solutions • AAFC has been conducting research on EO applications for over 30 years and is strong in radar R&D – – – – – – SAR soil moisture mapping: Led by Heather McNairn Passive microwave soil moisture anomaly mapping: Led by Catherine Champagne National crop land inventory: Led by Thierry Fisette and Andy Davidson National crop growth condition monitoring: Led by Andrew Davidson National NPP mapping: Led by Ted Huffman and Jiali Shang National yield forecast: Led by Aston Chipanshi
  5. 5. 1. National crop land inventory (2012) (2012)
  6. 6. SAR Contribution to crop classification Optical + Single-Frequency SAR Landsat 5: 2010-06-20 RSAT-2: 2010-05-28 2010-06-21 2010-07-15 2010-08-08 • insufficient optical data were available and thus SAR used to fill the gap • overall accuracy – – with Landsat only (< 70%) including RADARSAT-2 ScanSAR (VV, VH) data (89.1%) – When using multi-frequency (X, C and L-band) SARs, achieved 91.4% accuracy.
  7. 7. SAR Contribution to crop classification • Satisfactory crop classification (over 85% accuracy) can be produced using SAR data alone Crop Map Generated Using X-, C- and L- Band SAR: Carman, Manitoba, Canada (overall accuracy 91.4%)
  8. 8. 2. SAR sensitivity to crop biophysical parameters • AAFC is focusing on enhancing cropland productivity while maintaining environmental health • Mapping NPP of agricultural landscapes in representative eco-regions across Canada using an integrated SAR and optical remote sensing approach • In concert, the Canadian Space Agency is also a supporter of the NPP mapping activity • Currently we are developing an EO-based methodology to trace the historical course of Canadian agricultural land productivity, to map the states of crop growth backed up by yield records, and to offer insight for future development strategies.
  9. 9. Estimate corn LAI from RADARSAT-2 Corn LAI vs linear backscatter coefficient of HV at FQ6 4 3.5 LAI (m2/m2) 3 2.5 2 1.5 observed LAI >3 observed LAI 0-3 1 linear fit y=137*x-0.5 R2=0.93,RMSE=0.28 0.5 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Backscatter coefficient (power) 9
  10. 10. Estimate spring wheat LAI from RADARSAT-2 Spring wheat 1 RS-2 Entropy 0.9 0.8 0.7 0.6 0.5 y = 0.0903x + 0.48 R2 = 0.8923 0.4 0.3 0.2 0.1 0 0 1 2 3 Derived LAI 4 5 6 10
  11. 11. Radar responses to crop LAI R2 between RADARSAT-2 parameters and derived LAI Spring wheat Soybean 0.83 0.79 0.91 0.85 Entropy 0.88 0.81 0.86 0.74 Pedestal Height 0.90 0.90 0.95 0.89 Volume scattering  Corn Intensity HV  Oat 0.83 0.79 0.92 0.85 Several SAR parameters are sensitive to LAI, entropy performs well for all crop types tested Corn is most suitable for using SAR to derive LAI 11
  12. 12. 3. SAR for surface soil moisture retrieval • AAFC developed models to estimate field-level soil moisture using Canada’s RADARSAT-2.
  13. 13. 4. Passive microwave to map national soil moisture and agricultural risk conditions 2010 Soil Moisture Difference from Average 2011 < -10% -10 to -7.5 % - 7.5 to 5% -5 to -2.5% -2.5 to 0% 0 to 2.5% 2.5 to 5% 5 to 7.5% 7.5 to/10% > 10% No Data 2012 2013 • Passive microwave satellites can capture extreme wet and dry conditions at national scales 13
  14. 14. Drought – Passive Microwave Satellites • Soil moisture extremes have a large impact on Canadian agriculture. For example the 2001-02 drought in Western Canada resulted in a $5.8B GDP in damages to the Canadian economy. • We now use satellites to map the status of soil moisture through the growing season on a weekly basis using passive microwave satellites.
  15. 15. 5. SAR sensitivity to land management activities © DLR, 2008 • TerraSAR-X data reveals tillage occurrence August 26, 2008 (Spotlight - VV)
  16. 16. Summary • Earth observation satellites provide a viable means for crop inventory and growth condition monitoring; • Optical sensor and radar offer complementary information about the crops; • Methodology development is needed to integrate optical and radar for enhanced performance.
  17. 17. Joint Experiments for Crop Assessment & Monitoring (JECAM.ORG) • EO has become a global joint effort. • AAFC is leading the JECAM coordination of research sites sharing data & science to develop better agricultural monitoring capabilities around the world • Sites in development
  18. 18. G20 Global Agricultural Monitoring (GEOGLAM) • • • Remote sensing benefits from joint effort In 2011, the G20 launched the GEOGLAM initiative to provide better information to reduce market volatility & in turn support global food security. Canada plays an active role in implementing the initiative and welcomes international collaboration.
  19. 19. THANK YOU MERCI GRACIAS 谢谢 DANKE ありがとう GRAZIE ‫ﺑﺎ ﺗﺷﮑﺭ ﺍﺯ ﺷﻣﺎ‬