1. Overview of Joint Agency Commercial Imagery Evaluation (JACIE)
Presented to ASPRS,
13 March 2009
Mike Benson
Remote Sensing Technologies

2. Joint Agency Commercial Image Evaluation (JACIE)
Multi–Agency JACIE team formed in 2000
National Aeronautics and Space Administration (NASA), National National Geo-spatial Intelligence Agency (NGA), and U.S. Department of Agriculture (USDA), and U.S. Geological Survey (USGS)
Annual workshops and documentation
Perform system assessments to meet requirements by leveraging each agency’s expertise
Validation
Teams
U of Maryland
U of Arizona
S. Dakota State U
SCIENCE
USERS
JACIE TEAM

3. Joint Agency Commercial Image Evaluation (JACIE)
JACIE supports Commercial Remote Sensing Space Policy
Provides a data quality assessment and validation model
Support science, civilian, and DoD applications
Supports US National Imaging role in terrestrial monitoring
Provide imagery users with an independent assessment and validation with respect to product quality and usability
Provides an understanding and characterization of new sensors
Scope include all civil and commercial sensors useful to the U.S. remote sensing
National or international
Aerial or satellite
Optical, Light Detection And Ranging (LiDAR), Interferometer Synthetic Aperture Radar (IfSAR), hyperspectral, Multispectral, etc…

4. Joint Agency Commercial Image Evaluation (JACIE)
CRSSP Requirements database and archive
Database of remotely sensed data requirements and data archive
Benefits:
Improved product characterizations
Improved commercial products
Improved communication with industry, both vendors and users
Cost efficiencies by both Government and industry – reduced duplication of effort
Provide imagery users with an independent assessment and validation with respect to product usability
Help support use remotely sensed information/applications
Supports new applications and understanding of remotely sensed data

5. USGS, NGA, USDA, and NASA Collaboration
Joint Agency Commercial Imagery Evaluation (JACIE) 8th Annual Workshop held March 31 – April 2, 2009
USGS, NGA, USDA, and NASA Collaboration
Fairfax Marriott at Fair Oaks, Fairfax, VA
Workshop
Scope includes to High & Medium Resolution Satellite & Aerial sensors useful to the remote sensing community – U.S. and International systems
Additional session on other sensors
Independent assessment and validation of product quality and usability
New applications and understanding of remotely sensed data

6. System/Product Characterization
System Characterization is related to understanding the sensor system, how it produces data, and the quality of the produced data
Imagery attempts to accurately report the conditions of the Earth's surface at a given the time.
Assessed by product characterization categories:
Geometric/Geodetic: The positional accuracy with which the image represents the surface (pixel coordinates vs. known ground points)
Spatial: The accuracy with which each pixel represents the image within its precise portion of the surface and no other portion
Spectral: The wavelengths of light measured in each spectral "band" of the image
Radiometric: The accuracy of the spectral data in representing the actual reflectance from the surface
Dataset Usability: The image data and understanding of the data is easily usable for science application
Linearity
Calibration Accuracy and Stability
Detector-to-Detector Uniformity
Dark Noise Artifacts (e.g. Banding, Stray Light)

7. Examples of USGS JACIE type work
Landsat Data Gap and Remote Sensing Technologies Support and Testing
Landsat Data Gap mission/applications assessments and reports
Characterization of many satellite and aerial remote sensing systems
Understanding new technologies and applications
IRS-P6 Test Downlink performed on Aug 27, 2007
CBERS-2 Test Downlink performed on Mar 30, 2006
CBERS-2B Test Downlink at USGS EROS on Nov 27, 2008

8. Technical Report Report Sections Background and Sensor overview
Data Characterization
Science Utility
Mission Assessment
Many Appendixes
Report available:

9. Landsat Cross-calibration Activities
Recently completed or continuing Cross-calibration Activities
L7 ETM+ and L5 TM sensor
L5 TM and L4 TM sensor
L7 ETM+/L5 TM and EO-1 ALI sensor
L7 ETM+/L5 TM and Terra MODIS sensor
L7 ETM+/L5 TM and IRS-P6 AWiFS/LISS-III sensor
L7 ETM+/L5 TM and CBERS-2A CCD sensor
L7 ETM+/L5 TM and ALOS AVNIR-2 sensor
On-going or planned Cross-calibration Activities with L7 and L5
Beijing1, CBERS-2B, DMC,THEOS, ResourceSat, RapidEye, SPOT
QuickBird, Worldview, GEOEYE, Topsat
AVHRR MetOP, ENVISAT MERIS, MODIS
ASTER DEM, Cartosat-1 and -2

10. DMC Support
Performed Geometric Assessment of TopSat and BEIJING-1 Data
BEIJING-1 (Pan): I2I Vector Residual Plot
TopSat (Pan): I2I Vector Residual Plot

11. Cross-calibration of IRS-P6 & Landsat

12. Cross-calibration of MODIS & ETM+
Overall, a very good long-term agreement (± 2%) is seen between Terra MODIS and L7 ETM+ over seven years of operation.
The maximum change seen in ETM+ Band 1 and MODIS Band 3 which can be attributed to the long-term degradation especially at shorter wavelengths
Constant shifts in the ETM+ and MODIS trends is due to the significant spectral mismatch between the two sensors

13. Multiple Satellites Used in Science
Example of data to support Sagebrush study in Wyoming, USA Data included:
Landsat-5
Landsat-7
EO-1 ALI
EO-1 Hyperion
ASTER
IRS AWiFS
IRS LISS-III
Surrey DMC
DG Quickbird
This shows the approximate footprints of data used by Collin Homer & Mike Coan in their study of sagebrush cover in Wyoming. In some cases more than one instance of the each particular scene was collected. Note that the Quickbird collects, due to their relatively small footprint, only show up as small square “dots” at this scale, they are hard to see. The Quickbird footprints also have been cut back to the size of a quarter-quad, reducing them to about one-fourth the footprint of a full QB scene.

15. JACIE 2009 Civil Commercial Imagery Evaluation Workshop
March 31 –April 2, 2009
Agenda
Remote Sensing Technologies Project Web Page
JACIE Presentations