Comparing RADARSAT 2 and TerraSAR-X Quad-Pol SAR Imagery of Grasslands Joseph R. Buckley 1 and Anne M. Smith 2 1 Royal Military College of Canada, Kingston, ON, Canada 2 Agriculture and Agri-Food Canada, Lethbridge, AB, Canada National Défense Defence national
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Comparing RADARSAT 2 and TerraSAR-X
Quad-Pol SAR Imagery of Grasslands
Joseph R. Buckley1 and Anne M. Smith2
1Royal Military College of Canada, Kingston, ON, Canada 2Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
National DéfenseDefence national
Outline
Setting The Scene Experimental Design and Procedures Some Results Conclusions and Continuing Work
“Developing Earth Observation Tools to Measure the Current and Future Spatial Extent and Productivity in Grasslands of Western Canada”
A Multi-Year, Multi-Agency ExperimentCanadian Space Agency Funded
The Big Picture
• 350 M ha North America, • 30 M ha Western Canada• 25% left• Cattle production*• Wildlife• Tourism• Biodiversity*• Greenhouse gas emissions*• Water resources*• Agriculture, oil & gas, urbanization, over grazing, invasive species
Enhancing environmental performance of the Canadian agricultural
system, enhancing economic benefits for all stakeholders
Native grasslands
Native grassland
Photographs courtesy of: G. Bourgeois, M. Didkowsky R. Bourchier, J. Nicholsen, G. Larson, C. Kloppenburg.
Landscape cumulative effects, Land Use Framework, BMP
ASRD, AFSC Health assessment Wildlife habitat
ACA
Risk assessment/PolicySpecies at risk
Pasture managementWeed biocontrol
Program evaluation (BMP, NCGAVS, NAHARP)
AAFC (Research Branch/AESB, EC (DND,
INAC, CFIA) Grasslands Project Team
To develop multispectral, hyperspectral and radar earth observation tools to address mapping and quantification of:
1. the spatial extent and fragmentation of grasslands,2. the net primary productivity of grasslands,3. the invasive plant species, leafy spurge, on grasslands.
Objectives
Progress to Date
We are in the third year of this two year project Test sites
– Throughout Southern Alberta Ground data
– Surveys, crop insurance and irrigation databases (2009, 2010, 2011) Remote sensing data
DLR graciously accepted our request to participate in the spring 2010 DRA campaign, and acquired two three image quad-pol stripmap sets over two of our 2009-2010 test areas.
Three RADARSAT-2 fine mode quad pol images were acquired in as close to matching incidence angles and dates as could be arranged.
In mid-December 2010 we received the 6 SSC images from DLR Both RADARSAT-2 and TerraSAR-X images have been ingested,
speckle filtered (Lee Sigma) and decomposed (Yamaguchi4) with PolSARpro, georeferenced with ENVI and the tiepoints provided with the imagery, then fine co-registered by the usual cross-correlation method.
Preliminary comparative analysis has been done on the imagery.
General Conditions
Data Acquisition: 17 April to 11 May 2010 On the southern Alberta prairies
– Snow had melted– Lakes were no longer ice covered– No rain– No significant natural growth– Preparing and planting crops had not yet started
Natural rangelands were covered in senescent vegetation
Cultivated lands were either stubble, fallow or plowed
Observation Programme
TerraSAR-X April 12, 23, May 4 Stripmap Near 19.9 – 21.7
RADARSAT-2 May 1FQ17 36.4 – 38.0
RADARSAT-2 April 27FQ2 19.8 – 21.8
TerraSAR-X April 18, 29, May 10 Stripmap Far 30.8 – 32.3
RADARSAT-2 April 17FQ8 26.9 – 28.7
TerraSAR-X April 18 Stripmap Far 30.8 – 32.3
RADARSAT-2 April 17FQ8 26.9 – 28.7
Yamaguchi 4 Decomposition
SurfaceVolumeDouble
All scaled -30 0dB
RADARSAT-2 : April 27 19.8 – 21.8 TerraSAR-X: April 23 19.9 – 21.7
TerraSAR-X
RADARSAT-2
V
V
S
S
D
D
Yamaguchi 4 Decomposition
Cyan: TSXRed:R2
Surface scattering
RADARSAT-2 : April 17 26.9 – 28.7 TerraSAR-X: April 18 30.8 – 32.3
TerraSAR-X
RADARSAT-2
V
V
S
S
D
D
Yamaguchi 4 Decomposition
Cyan: TSXRed:R2
Surface scattering
RADARSAT-2 : May 1 36.4 – 38.0 TerraSAR-X: April 29 30.8 – 32.3
TerraSAR-X
RADARSAT-2
V
V
S
S
D
D
Yamaguchi 4 Decomposition
Cyan: TSXRed:R2
Surface scattering
Scatter Plots: April 17-18, Rangeland ROI
X axis: TerraSAR-XY axis: RADARSAT-2
Surface Volume
Dihedral
Observations
Both RADARSAT-2 and TerraSAR-X show the same general structure
TerraSAR-X is always brighter than RADARSAT-2 There is no correlation at the pixel level between images
from the two sensors
Sub-area for continued testing• Contains
• Water• Native rangeland• Irrigated cropland
RADARSAT-2 FQ8 April 17
TerraSAR-X April 18
TerraSAR-X April 29
RADARSAT-2 FQ17 May 1
TerraSAR-X May 10
TerraSAR-XApril 29
TerraSAR-XApril 29
April 18
April 29
May 10
RADARSAT-2 ROI Histograms
FQ8 April 17
FQ17 May 1
T33
T22
T11
TerraSAR-X May 10
April 18
April 29
May 10
Entropy:TerraSAR-X
FQ8 April 17
FQ17 May 1
Entropy: RADARSAT-2
April 18
April 29
May 10
Alpha: TerraSAR-X
FQ8 April 17
FQ17 May 1
Alpha: RADARSAT-2
FQ8 April 17
FQ17 May 1
April 18 April 29 May 10
More Observations
TerraSAR-X shows much higher entropy than RADARSAT-2
T22, T33 close to or at noise floor for TerraSAR-X cross-pol, double bounce contain little information
TerraSAR-X shows more volume scattering – Shorter wavelength or higher entropy?
Much greater dynamic range for RADARSAT-2
Conclusions
Qualitatively, RADARSAT-2 and TerraSAR-X produce similar imagery.
TerraSAR-X imagery is visibly noisier. Very high entropies for TerraSAR-X imagery indicate
little statistically significant polarimetric information
These results are specific to the environmental conditions– Is the environment of the prairies in early spring too subtle for