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TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE
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TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE ESTIMATES: A CANADIAN EXAMPLE

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  • The land is non-irrigated dry land To capture the key signatures of the growth cycle. The data were collected cover two consecutive years, 2006 and 2007
  • Transcript

    • 1. Jiali Shang, Heather McNairn, Xianfeng Jiao, Catherine Champagne Agriculture and Agri-Food Canada, Ottawa, Canada 960 Carling Avenue, Ottawa, Canada, K1A 0C6 [email_address] Integration of RADARSAT-2, TerraSAR-X & ALOS PALSAR for In-Season Crop Acreage Estimates: A Canadian Example
    • 2. Monitoring Agriculture in Canada
      • 33 million people
      • 680,000 km 2 farmland (7% of total area)
      • Provides 1 in 8 jobs and accounts for 8% of Canada’s GDP
      • 5 th largest exporter of agriculture and agri-food products
      • Accounts for about 20% of the total world exports of wheat and wheat flour (10 year average)
      • Agriculture land use information supports programs and policies and risk mitigation for the agriculture sector
    • 3. AAFC Pilot Studies: method development for an operational crop inventory (2004-2008)
      • Optical data can provide satisfactory classification accuracies when available at key growth stages
      • Cloud cover causes data gaps, reduces classification accuracies
      • SAR can fill data gaps, and can improve classification accuracies of certain crops
      • The first generation of SAR sensors (ERS-1, RADARSAT-1) were single-frequency and single-polarization, not adequate for crop classification
      Swift Current Winnipeg
    • 4.
      • SAR data provide complementary information to optical sensors
      • The availability of multi-frequency and polarimetric SAR offers richer information content which could lead to greater separation among crops
      Crop Inventory using multi-frequency SAR
    • 5. Radar-based Crop Classification
      • Study sites
        • Casselman, Ontario (corn, cereal, soybean, and pasture/forage) (2008-2009)
        • Carman, Manitoba (canola, flax, sunflower, soybean, corn, dry bean, peas, cereal, potato, fallow, pasture/forage ) (2009)
      • Methodology
        • 2 field visits were made over the growing season to ensure data quality and to note variations in growth stage, harvest time, and changes in crop type (due to recording errors and under-seeding)
        • 50% of the fields selected randomly for training, and remaining 50% left for validation
        • A decision-tree classification followed by a post classification object-based filter
    • 6. X- & C-Band Classification Results: Casselman 2008 Comparison of single-date RADARSAT-2 and TerraSAR-X crop classification accuracies over Casselman, Ontario
      • X-band radar (shorter wavelength) provides higher overall accuracies
      • X-band radar are better for lower biomass crops (wheat, pasture)
      Sensors Frequency Polarization Used for Comparison Date Pasture/ Forage Soybean Corn Wheat Overall Accuracy TSX X-band VV/VH July 16 69.1 58.2 48.0 85.2 59.9 RSAT-2 C-band VV/VH July 19 47.1 64.7 50.7 42.8 54.2 TSX X-band VV/VH Aug 9 59.1 79.4 71.0 61.8 71.0 RSAT-2 C-band VV/VH Aug 10 39.6 73.8 56.2 36.8 57.4
    • 7. Casselman 2008 Crop Map Water Urban Shrubland Wetland Hay-Pasture Soybean Corn Cereal Buckwheat Coniferous Roads Barren Legend Broadleaf 0 2 4 km Classification using 3-date SPOT Classification using 6-date TerraSAR-X
    • 8. X- & C-Band Integration: Casselman 2008
      • Integration of X- and C-band SAR provides improved separations among crops. An overall accuracy of over 85% can be achieved.
      • At individual crop level, small grains, often the most problematic to identify, can also achieve higher than 85% accuracies.
    • 9. C- and X-Band Results: Casselman 2009 User’s Accuracies RADARSAT-2 TerraSAR-X Pasture/Forage Corn Soybean Cereal Potato Overall Before July 8 38.7 76.6 73.9 63.4 70.0 71.9 Before July 8 Before June 25 41.2 81.2 81.3 75.4 73.9 78.2 Before Aug 7 39.9 84.7 76.7 75.6 93.7 77.9 All (10 dates) 41.5 96.1 83.7 79.6 90.6 85.6
    • 10. L-, C- & X-Band Classification Results: Carman 2009
      • Higher frequencies have highest accuracies
      • Classification benefits from multi-frequency SAR
      • Later season multi-frequency SAR offers good accuracy
      User’s Accuracies ALOS RS2 Tsx Canola Flaxseed Beans (Soy and Dry) Corn Cereal Overall All (10) 93.4 87.4 88.4 78.3 95.1 89.4 11 Aug 9 Aug 15 Aug 94.1 73.6 88.0 83.5 93.1 89.4 All All All 95.3 91.6 92.2 83.7 92.7 91.4
    • 11. Crop Map Generated Using All Multi-Frequency SAR Images Carman 2009 (91.4% overall accuracy) Water Urban Shrub land Wetland Hay-Pasture Soybean Corn Cereal Coniferous Roads Barren Legend Broadleaf Canola Soybean Sunflower Flaxseed Fallow Potato Field peas Beans N 0 3 6 km
    • 12. Polarimetric SAR for Crop Classification RADARSAT-2 (June 15, July 6, August 9, September 2): Casselman 2008 RADARSAT-2 (May 29, June 22, July 16, August 9, September 2): Carman 2009 Pasture Soy Corn Wheat Overall Cloude-Pottier 77.5 85.2 86.4 97.6 85.8 Freeman-Durden 85.1 93.7 89.9 93.3 90.9 Linear Polarizations (HH, HV, VV, VH) 60.4 81.8 73.6 83.8 75.4 Canola Flax Beans Corn Cereals Overall Cloude-Pottier 91.9 57.4 76.5 68.6 82.8 79.8 Freeman-Durden 94.1 81.0 80.1 69.2 88.8 83.6 Linear Polarizations (HH, HV, VV, VH) 83.1 51.3 73.7 53.5 89.4 75.5
    • 13.
      • The penetration depth of the radar signal is related to the radar frequency
      • Higher frequency SAR primarily captures volume scattering near the top of the crop canopy. X-Band provides the best overall classification accuracy.
      • C-band and L-Band penetrate deeper and interact with other canopy components, and help to improve accuracies of higher biomass crops. Too great of penetration, especially with low biomass crops, results in interactions with the soil.
      • Multi-frequency SAR provides the best crop specific accuracies
      • Polarimetric scattering parameters appear to outperform multi-polarization data
      • Operational crop mapping and in-season crop acreage estimates could be supported by multi-temporal, multi-frequency and polarimetric SAR
      Conclusions
    • 14. Acknowledgements
      • Financial support for this project is provided by AAFC’s A-base research funding and Canadian Space Agency’s (CSA) GRIP funding
      • The TerraSAR-X data were provided by DLR through research project LAN0337
      • RADARSAT-2 data were provided by CSA
      • ALOS PALSAR data were provided by JAXA
      • Thank goes to John Fitzmaurice, Maciej Jamrozik, Colin Schut, Jiangui Liu, Carl Puddy and Patrick Rollings for field data collection and data processing

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