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

jiali.shang@agr.gc.ca

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