JECAM SAR Inter-Comparison Experiment

Crop Type Identification & MappingLaura Dingle Robertson, Andrew Davidson, Heather

McNairn, Mehdi Hosseini, Scott Mitchell, & Michael H. CoshFebruary 15, 2018

Agenda1. JECAM SAR Inter-

Comparison Experiment2. Crop Type Identification

& Mapping Activities3. Partners4. Field data received5. EO data pre-processing6. SAR speckle filter testing7. Next steps

RADARSAT2

Sentinel 1

JECAM SAR Inter-Comparison Experiment

1. Crop Type Identification & Mapping

Compare and develop crop mapping methods that use SAR only and SAR/optical combination(s) over JECAM sites around the world to come up with best practices for EO crop type identification & mapping

2. LAI & Biomass (Mehdi Hosseini)Compare models that use SAR for LAI & Biomass

AAFC 2016 Annual Crop Inventory• Optical and SAR-based decision

tree classifier• Classified Alberta, Saskatchewan

& Manitoba since 2009• Classified agricultural extent since

2011

Crop Type Identification & Mapping Goals

Activity 1a. Applying Agriculture and Agri-Food Canada (AAFC) Earth Observation Crop Inventory Method to other JECAM Sites

Activity 1b. Applying JECAM Member Sites’ SAR and Optical, OR SAR only (single frequency) Classification Methodologies to Multiple Regions

Activity 2. Reducing the Impact of Cloud Cover on Operational Crop Inventories

Activity 3 and 4. Multi-frequency SAR imagery for Crop Type Mapping AND Compact polarimetry and/or polarimetric decomposition variables for Crop Type Mapping.

Participation Document

JECAM Partners

Partner Survey data LAI & Biomass 2018 Field Plan

Argentina (3 Crops) Survey & LAI/Biomass

Bangladesh (LAI & Moisture: Rice) LAI

Belgium (~22 Crops, ~1004 fields)

Brazil – Sao Paulo (~14 Land cover Types, ~1441 polygons)

Survey only

Brazil –Tocantins (9 Landover Types, ~900 polygons)

Survey & LAI/Biomass * plus new site Cerrados

Canada – Carman (Survey) (LAI, Biomass, SM: Corn, Wheat)

Survey only

Canada –Casselman

(8 Crops, ~810 fields) Survey only

France – Toulouse (8 Crops, ~985 fields Survey & LAI/Biomass/Soil Moisture

Germany(DEMMIN)

(7 Crops, ~42 fields) (LAI & Biomass (in progress))

Survey & LAI/Biomass/Soil Moisture

India (5 Crops, ~64 fields) (LAI: Biomass, Rice, Cotton, Banana, Sugarcane)

Survey & LAI/Biomass/Soil Moisture

Italy – Apulia Tavoliere

(5 Crops, ~53 fields) (LAI: Wheat, Barley & Oats)

Survey & LAI/Biomass/Soil Moisture

Poland (LAI: Wheat & Corn) LAI/Biomass/Soil Moisture

Taiwan (LAI/Biomass: Rice) LAI/Biomass/Soil Moisture

Ukraine (Survey)* (LAI: Wheat, Corn, Soy)

USA – Georgia (~14 Crops, ~586 fields)

Survey & LAI/Biomass/Soil Moisture

USA – Iowa (Based on CDL) (SMAPVEX 2016 data) Survey (CDL)

USA –Massachusetts

(Based on CDL) Survey (CDL & Survey)

USA-Michigan (Based on CDL) Survey (CDL) & LAI

USA – North Dakota (Based on CDL) (LAI, SM, Biomass) (LAI, SM, Biomass + CDL)

• 18 Partners provided field data including 10 for LAI/Biomass

• 16 partners committed to collecting field data in 2018 to go along with RADARSAT2 acquisitions

– RADARSAT2 quad polarization data will be collected to support the compact polarimetry portion of the experiment

– A new Partner -Brazil – Cerrados!

Field Data from JECAM Partners for 2014 to

2017 &2018 Collection Plan

EO SAR Data Preprocessing• SAR-Simulation Terrain Correction

– Literature shows support for both SAR Simulation Terrain Correction and Range Doppler Correction (Bayanudin & Jatmiko 2016; Jiang et al., 2016) with SAR Simulation having slightly more rigorous results. As a matter of best practice SAR Simulation Terrain Correction was selected

• Normalization to incidence angle– Only processed for Component 1. Component 2 uses Local Incidence angle as a parameter in the

model– Variations of normalization to:

• local incidence angle;• projected local incidence angle;• derived angle based upon ellipsoid; and • no normalization

– were tested. While there were no significant differences between these for small-area, relatively flat sites, as a matter of best practice it was decided to normalize all images to local incidence angle.

• Order of operations testing– Typically literature recommends filtering before ortho-rectifying /terrain correction. Testing of order

of operations showed slightly degraded overall classification accuracies and longer processing time with filtering first.

SAR speckle filter testing:window size & filter type

Test parameters• Crop types of corn, soybean, wheat/no wheat/double crop

with three test partners:• Canada Casselman: small field size (~3 ha) (Complete)• Argentina: medium field size (~25ha, In progress)• USA Iowa: large field size (~30-60 ha → continuous fields resulting in

large homogenous areas of same crop types).

• Filters: Gamma Map (current AAFC operational filter), Touzi(Touzi, 2002) and Multi-Temporal (Quegan et al., 2000).

• Multiple window sizes and passes.

Gamma MAP & Touzi FiltersGAMMA Map Filter Current operational filter with a 7x7 window; reduces speckle as a function of the co-efficient of variation within the windowR is the center smoothed pixel, found as:R = I, for Ci less than or equal to Cu; OR

R = 𝐵𝐵∗𝐼𝐼+ 𝐷𝐷2∗𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴

, for Cu < Ci < Cmax; ORR = CP, for Ci greater than or equal to Cmaxwhere: NLOOK = Number of Looks VAR = Variance in filter window CP = Initial center pixel value I = Mean value in the filter window Cu = 𝐼𝐼

𝑁𝑁𝐴𝐴𝑁𝑁𝑁𝑁𝑁𝑁

Ci = 𝑉𝑉𝐴𝐴𝑉𝑉𝐼𝐼

Cmax = 2 𝐶𝐶𝐶𝐶

ALFA = 1+𝐶𝐶𝐶𝐶2

𝐶𝐶𝐶𝐶2−𝐶𝐶𝐶𝐶2B = ALFA-NLOOK-1

Touzi Filter is an adaptive filter where the underlying features are identified based upon different detectors and sub-windows. As features are identified and filtered they are ‘set aside’; Window size adapts as each filter step occurs.

5 iterative steps:1. Point Target Filtering2. Curvilinear Filtering3. Homogenous Area Filtering4. Multiresolution Edge Detection and

Filtering5. Stationary Area Filtering

Filtered RADARSAT2 VV Images Casselman, Ontario

Casselman RADARSAT2-VV, May 29, 2016Gamma MAP 13 x 13 Window

Casselman RADARSAT2-VV, May 29, 2016Touzi 25 x 25 Window

Casselman RADARSAT2-VV, May 29, 2016Gamma MAP 7 x 7 Window

Casselman RADARSAT2-VV, May 29, 2016Touzi 13 x13 Window

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Filter Type and Window Size

Comparison of different SAR filters and window sizes based upon the percent overall accuracy of AAFC's operational Decision Tree Classifier and SAR Only data.

Casselman

Iowa

Filter Testing Outcomes

Multi-Temporal filter has poor results with less dense temporal stacks.

Best Gamma MAP results Best Touzi results

JECAM Casselman Best Outcome (3 crops)

Wheat Corn Soybeans

Wheat 1476 8 44

Corn 58 26540 4033

Soybeans 103 2404 25030

User’s Accuracy Producer’sAccuracy

Wheat 96.6 90.2

Corn 86.6 91.7

Soybeans 91.0 86.0

Overall accuracy: 88.9%, Kappa: 0.79

Touzi Filter 25 x 25 window

Wheat Corn Soybeans

Wheat 1334 177 113

Corn 133 26558 5159

Soybeans 170 2217 23835

User’s Accuracy Producer’sAccuracy

Wheat 82.1 81.5

Corn 83.4 91.7

Soybeans 91.0 81.9

Overall accuracy: 86.7%, Kappa: 0.75

Gamma MAP 13 x 13 window

McNemar’s Chi-squared test with continuity correction (no statistical significance)McNemar's chi-squared = 122.63, df = 1, p-value < 2.2-16

JECAM Iowa Best Outcome (2 crops)

Corn Soybeans

Corn 205823 18831

Soybeans 10919 78595

User’s Accuracy Producer’sAccuracy

Corn 91.6 95.0

Soybeans 87.8 80.7

Overall accuracy: 90.5%, Kappa: 0.77

Touzi Filter 13 x 13 window

Corn Soybeans

Corn 206361 19817

Soybeans 10381 77609

User’s Accuracy Producer’sAccuracy

Corn 91.2 95.2

Soybeans 88.2 79.7

Overall accuracy: 90.4%, Kappa: 0.77

Gamma MAP 11 x 11 window

No statistical difference

Next Steps• Finalize filter selection• Finalize pre-processing automation• EO & field data stack creation and distribution

to JECAM Partners (February/March)• Running AAFC Crop Inventory Decision Tree

Method on all Stacks• Running Cloud Cover Iterations on All Stacks• www.jecam.org re-launch

Acknowledgements & Thanks

• Funding provided by the Canadian Space Agency under the Government Related Initiatives Program (GRIP).

• Many thanks to the Agriculture and Agri-Food Canada Earth Observation team especially Catherine Champagne, Patrick Rollin, Thierry Fisette, Ziad Aly, Elizabeth Eidness, and University of Guelph and University of Waterloo students: Hanna Holman, Benjamin Kovacs, Natalija Nikolic, and Holden Ciufo.