Radar satellites have potential to estimate crop parameters and monitor agriculture. SAR and optical data can be used together to create accurate crop inventories. Radar is sensitive to crop biophysical parameters like leaf area index. Models can estimate soil moisture from radar. Passive microwave identifies extreme wet/dry conditions nationally. New initiatives promote international collaboration on agricultural monitoring using earth observation.

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
jiali.shang@agr.gc.ca
December 14-15, 2013, Mexico City, Mexico

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. 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. 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. 1. National crop land inventory
(2012)
(2012)

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. 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. 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
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. 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. 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. 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
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. 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. 5. SAR sensitivity to land management activities
© DLR, 2008
• TerraSAR-X data reveals tillage occurrence
August 26, 2008 (Spotlight - VV)

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. 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. 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. THANK YOU
MERCI
GRACIAS
谢谢
DANKE
ありがとう
GRAZIE
‫ﺑﺎ ﺗﺷﮑﺭ ﺍﺯ ﺷﻣﺎ‬