radar’s potential to estimate crop bio-physical parameters & beyond
DESCRIPTION
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)TRANSCRIPT
Radar’s Potential to Estimate Crop Bio-Physical Parameters & Beyond
Jiali Shang1, Heather McNairn1, Catherine Champagne1, Xianfeng Jiao2
1Agriculture and Agri-Food Canada, Ottawa, ON, Canada
2Natural Resources Canada, Ottawa, ON, Canada
December 14-15, 2013, Mexico City, Mexico
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
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
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
1. National crop land inventory (2012)
(2012)
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.
SAR Contribution to crop classification
SAR Contribution to crop classification
Crop Map Generated Using X-, C- and L- Band SAR: Carman, Manitoba, Canada (overall accuracy 91.4%)
• Satisfactory crop classification (over 85% accuracy) can be produced using SAR data alone
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
Estimate corn LAI from RADARSAT-2
0.005 0.01 0.015 0.02 0.025 0.03 0.0350
0.5
1
1.5
2
2.5
3
3.5
4
Backscatter coefficient (power)
LAI (
m2 /m
2 )Corn LAI vs linear backscatter coefficient of HV at FQ6
observed LAI >3observed LAI 0-3linear fit y=137*x-0.5R2=0.93,RMSE=0.28
10
Estimate spring wheat LAI from RADARSAT-2
Spring wheat
y = 0.0903x + 0.48R2 = 0.8923
00.10.2
0.30.40.50.60.7
0.80.9
1
0 1 2 3 4 5 6Derived LAI
RS-2
Ent
ropy
11
Spring wheat Oat Corn Soybean
Intensity HV 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 0.83 0.79 0.92 0.85
R2 between RADARSAT-2 parameters and derived LAI
Radar responses to crop LAI
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
3. SAR for surface soil moisture retrieval • AAFC developed models to estimate field-level soil moisture using Canada’s
RADARSAT-2.
13
4. Passive microwave to map national soil moisture and agricultural risk conditions
2010 2011
2012 2013 • Passive microwave
satellites can capture extreme wet and dry conditions at national scales
No Data > 10% 7.5 to/10% 5 to 7.5% 2.5 to 5% 0 to 2.5% -2.5 to 0% -5 to -2.5% - 7.5 to 5% -10 to -7.5 % < -10%
Soil Moisture Difference from Average
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.
5. SAR sensitivity to land management activities
• TerraSAR-X data reveals tillage occurrence
August 26, 2008 (Spotlight - VV)
© D
LR, 2
008
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.
Sites in development
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
•
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.
THANK YOU MERCI
GRACIAS 谢谢
DANKE ありがとう
GRAZIE از شما تشکر با