Transcript of TH4.L09 - INTEGRATION OF RADARSAT-2, TERRASAR-X and ALOS PALSAR DATA FOR IN-SEASON CROP ACREAGE...
- 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
- 680,000 km 2 farmland (7% of total area)
- Provides 1 in 8 jobs and accounts for 8% of Canadas 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
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- Casselman, Ontario (corn, cereal, soybean, and pasture/forage)
(2008-2009)
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- Carman, Manitoba (canola, flax, sunflower, soybean, corn, dry
bean, peas, cereal, potato, fallow, pasture/forage ) (2009)
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- 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)
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- 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 Users 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
Users 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 AAFCs A-base
research funding and Canadian Space Agencys (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