1. VALIDATION OF THE NPP VIIRS ACTIVE FIRE PRODUCT Ivan Csiszar1, Wilfrid Schroeder2, Louis Giglio2, Evan Ellicott2, Christopher O. Justice2 1NOAA/NESDIS Center for Satellite Applications and Research, Camp Springs, MD 2University of Maryland, College Park, MD

2. Outline VIIRS active fire product overview Active fire validation approach Reference datasets NPP VIIRS validation plan and recent results Conclusion I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

3. VIIRS: Visible Infrared Imager Radiometer Suite Primary fire bands http://www.ipo.noaa.gov/ I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

4. VIIRS fire product overview VIIRS will provide radiometric measurements that offer useful information for the detection and characterization of active fires principal bands: M13 (MIR) and M15 (TIR) Fire Mask Application Related Product (ARP) Baseline algorithm: moderate resolution M13 and M15 aggregated native resolution pixels MODIS heritage algorithm Fire detection capability is driven by fire fraction – no direct continuity with any heritage sensor Goal is continuation of (AVHRR-) MODIS heritage Real-time applications Long-term monitoring (GCOS Essential Climate Variable) I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

5. Example of expected VIIRS detection (based on modeling using ASTER fire masks) VIIRS (aggregated) MODIS 7 Aug 2004 1405 UTC ~11.7o S 56.6o W (Brazil) I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

6. Example of expected VIIRS detection (based on modeling using ASTER fire masks) VIIRS (aggregated) MODIS 7 Aug 2004 1405 UTC ~11.7o S 56.6o W (Brazil) I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

7. MODIS vs. VIIRS – simulation results 750 m 1000 m 90% probability of detection; boreal forest; nadir view L. Giglio I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

9. Example of application includes assessment of product performance immediately after launch when reference data aren’t available

10. Ideally, the secondary data set must be validated especially when using similar algorithms/methods

11. May complement primary validationI. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

12. Reference Data Requirements: spatial Reference data must provide good spatial coverage to include effective pixel area plus background PSF MODIS True Positive (MOD14) False Positive (MOD14) PSF GOES MODIS/Terra FRP (MW) A = Nominal pixel area B = Effective pixel area Adjusted Values (PSF): Left pixel = 69.81 MW Right pixel = 63.03 MW Credit: Schroeder et al, 2010 I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

13. Reference Data Requirements: temporal Maximum separation between satellite fire and independent reference data must be limited to ~15min Credit: Schroeder et al, 2008 Credit: Giglio, 2007 Credit: Csiszar and Schroeder, 2008 I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

15. Prescribed fire intensive monitoring site in the Florida Panhandle (Rx-CADRE/Eglin Air Force Base) being supported by the Joint Fire Science Program

16. Fire intensity (radiative power), temperature and size measurements

17. Smoke characterization (particulate matter, trace gases)

18. Precribed fires monitoring in National Parks

20. Fire intensity (radiative power), temperature and size measurements

23. Airborne (UAV) mapping of surface fires

24. Airborne (UAV) mapping of smoke plumes

25. Complemented by field survey data

26. Wildfires in Western US (Southern California) (NASA/Ames)

27. Airborne (BeechCraft Kingair) mapping of surface fires

28. Wildfires in Western US (Southern California) (USFS/Pacific Southwest Research Station)

29. Airborne (Beechcraft Kingair) mapping of surface fires (emergency response)

30. Wildfires in Wedstern US (Southern California) –Joint Fire Science Program (PI: Phil Riggan/USFS)

31. Airborne (Beechcraft Kingair) mapping of surface fires (includes validation component of spaceborne fire retrievals)I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

35. Targeted data acquisition @360m resolution (178km swath)

36. Fire-dedicated bands provide quality data for use in support of VIIRS and ABI active fire product development and validation

37. BIROS orbit configuration still open

38. BIROS technical team demonstrated interest in supporting validation efforts

40. Could augment targeted sampling capacity (increase data volume)I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

41. MODIS-BIRD Coincident Acquisition : Lake Baikal (Russia) MODIS middle-infrared (fire) band BIRD middlfe-infrared (fire) band MODIS fire detection pixels BIRD fire detection pixels High quality reference data (in addition to primary mission objective of stand-alone monitoring) Further improvement is expected with HyspIRI Credit: Zhukov et al., 2006 I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

42. Heritage: MODIS Global Fire Product Validation Near-nadir pixels (using ~2,500 coincident ASTER scenes) Off-nadir pixels (using ~3,700 near-coincident TM scenes) 17K MOD14 pixels sampled 120K MODIS pixels with 1+ ASTER fire pixel 12K MOD14 pixels sampled 270K MODIS pixels with 1+ TM fire pixel I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

43. Global Validation Data Sets (MOD14) Near-nadir pixels (using ~2,500 coincident ASTER scenes) Off-nadir pixels (using ~3,700 near-coincident TM scenes) ASTER 2001-2006 SWIR detector problem > May 2007 Landsat5 TM 2001-2010 Fire-related artifacts – saturation/bleeding MODIS/ASTER 19 Jan 2006 0852UTC (near nadir) MODIS/TM 04 Aug 2007 1533UTC (52o scan angle) I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

44. Global Validation Data Sets (MOD14) Near-nadir pixels (using ~2,500 coincident ASTER scenes) Off-nadir pixels (using ~3,700 near-coincident TM scenes) ASTER 2001-2006 SWIR detector problem > May 2007 Landsat5 TM 2001-2010 Fire-related artifacts – saturation/bleeding MODIS/ASTER 19 Jan 2006 0852UTC (near nadir) MODIS/TM 04 Aug 2007 1533UTC (52o scan angle) I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

45. View angle effects MODIS Fire Pixels Detected per Sample VIIRS Detector Aggregation Scheme Diurnal cycle Pixel size effect JPSS program MODIS probability of detection (off nadir) MODIS commission error (off nadir) I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

46. Airborne Data for Validation of Fire Detection and Characterization NASA/Ames AMS image of California fire in 2007 I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

47. Current status and plans Cal/val rehearsal – July 18-22, 2011 Establish data access Ingest and display of proxy VIIRS Active Fire EDR Comparison with Aqua/MODIS detections Reporting findings through Cal/val Findings Tool Monitoring SDR cal/val results Proposed post-launch algorithm updates Full fire mask Fire Radiative Power Compatibility with MODIS Collection 6 Explore potential of alternative and additional VIIRS bands Coordinated efforts with NASA NPP Science Team MODIS – VIIRS continuity I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

50. Problem likely to persist over the next years

52. Account for pixel spatial response

53. Use of more realistic surface conditions (temperature fields as well as morphology)

55. Costs can be prohibitive although by combining efforts with other groups the science output could pay offI. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)