1. Satellite Active Fire Product Development and Validation: Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments Wilfrid Schroeder 1, Ivan Csiszar 2, Louis Giglio 3, Evan Ellicott 3, Christopher Justice 3, Christopher Schmidt 4 1 ESSIC/CICS, UMD 2 STAR NOAA/NESDIS 3 Dept of Geography, UMD 4 CIMMS, UW-Madison

2. Team Background Ongoing CICS Projects : GOES-R: “Validation and Refinement of GOES-R ABI Fire Detection Capabilities” (GOES-R AWG) MODIS & VIIRS: “Active Fire Product Evaluation and Development from MODIS and VIIRS” (NASA) “Development of an Enhanced Active Fire Product from VIIRS” (IPO – includes NPP active fire product validation program activities also) Linkages and collaborations: Christopher Schmidt (UW-Madison) – GOES Imager/ GOES-R ABI Fire Product PI (GOES-R AWG) Christopher Justice and Louis Giglio (UMD/Geography) – MODIS Active Fire Product PIs (NASA) Ivan Csiszar (NESDIS/STAR) and Christopher Justice (UMD/Geography) – NPP/VIIRS Active Fire Product PIs (NASA, IPO) Wilfrid Schroeder, Christopher Schmidt, Ivan Csiszar, Elaine Prins, Christopher Justice – fire product evaluation in the Amazon and long-term fire data record (NASA LBA-ECO – recently concluded)

3. Progress in the Last Three Decades Major Data Sets** Adv Very High Res Radiometer (AVHRR) 1kmx12h within antenna range 1980 1990 2000 2010 GOES East Imager 4kmx30min Western Hemisphere Tropical Rainfall Monitoring Mission (TRMM) 2.4kmx12h ±38º Mod Res Imaging Spectroradiometer (MODIS/Terra) (MODIS/Aqua) 1kmx12-24h global Reprocessed in 2009 GOES VAS 13.8kmx30min Western Hemisphere EOS/Terra EOS/Aqua NOAA-12 ** Excluding nighttime sensors such as ATSR, DMSP Simple Threshold (single or multi-band) Contextual methods (x,y) (dynamically adjusted) Contextual methods (x,y,t) (dynamically adjusted) A few dozen images 400K+ images from GOES only

4. Essentials in Active Fire Monitoring Fires are highly dynamic events Fires may/not leave detectable scars behind

5. ETM+ 10am ASTER 10:30am Active Fire Reference Data Derived from ASTER and ETM+ Imagery ASTER ETM+ ASTER bands 3 and 8 and ETM+ bands 4 and 7

6.  Sample Size: 18 ASTER scenes  Region: South Africa  Proof of concept using fixed threshold method applied to ASTER band 9 to derive 30m resolution active fire masks  Morisette et al. 2005  Sample Size: 100 ASTER scenes  Region: Global  Development of robust active fire detection algorithm for ASTER  Giglio et al. 2008  Sample Size: 131 ASTER scenes  Region: Northern Eurasia  Development of active fire validation protocol  Csiszar et al. 2006 MODIS/Terra Active Fire Validation C3-C4 Algorithm Version

7.  Sample Size: 115 ASTER scenes  Region: CONUS  Validation of NOAA/NESDIS operational fire monitoring system including analyst data  Schroeder et al. 2008  Sample Size: 167 ASTER + 123 Landsat ETM+ scenes  Region : Brazilian Amazonia  Generalization of moderate-coarse resolution fire data validation (MODIS + GOES) using higher resolution imagery  Schroeder et al. 2008 MODIS/Terra Active Fire Validation C3-C4 Algorithm Version  Sample Size: 24 ASTER + 8 Landsat ETM+ scenes  Region : Brazilian Amazonia  Assessment of short-term variation in fire behavior – implications to active fire validation  Csiszar and Schroeder 2008

8. MODIS/Terra C5 Algorithm Stage 3 Fire Validation  Sample Size: ~2500 ASTER scenes  Region : Global  Stage III validation of MOD14  Schroeder et al. (in preparation) Daytime & nighttime data Data equally distributed across the globe Multi-year analysis (2001-2006) ASTER SWIR anomaly May ‘07 Omission/commission errors derived as a function of percent tree cover

9. Temporal Consistency of MOD14 Detection Performance Using a subset of points covering the range of 20-40% tree cover  No statistically significant difference over time (i.e.,  D t = 0; p < 0.01)

10. Overall Probability of Detection Summary curve using all data points (125K MODIS pixels with >0 ASTER fire pixels including16K MOD14 fire pixels)

11. Daytime Probability of Detection as a Function of Percentage Tree Cover** ** average value calculated using a 20x20km window centered on the target pixel

12. ASTER (RGB 8-3-1) 21 June 2003 17:38:35UTC Manitoba, Canada

13. ASTER (30m Fire Mask) 21 June 2003 17:38:35UTC Manitoba, Canada

14. Commission errors Recently burned pixels with discernable scars constitute a large fraction of the false detections. Overall fire-unrelated commission error ~2% Nighttime commission error rate is zero. Results – Commission Errors Schroeder et al. (in preparation)

15. Results – Commission Errors Typical false alarm in MOD14 data 20 Jul 2003 1407UTC 21 Aug 2003 1407UTC Commission errors can occur multiple times at the same location MODIS/Terra was found to detect twice as many false positives as MODIS/Aqua

16. MIR – Initial Tests: Deriving MODIS L1B TOA Radiances using ASTER Surface Kinetic Temperature data + Radiation Transfer Model MODIS L1B Ch21 07 Aug 2004 1405 UTC 11.7 o S 56.6 o W UMD MODIS Ch21 Proxy Data 07 Aug 2004 1405 UTC 11.7 o S 56.6 o W Early Assessment of NPP/VIIRS Active Fire Data

17. MIR – Initial Tests: Deriving MODIS L1B TOA Radiances using ASTER Surface Kinetic Temperature data + Radiation Transfer Model

18. Initial Results MODIS/Terra (1kmx1km)VIIRS (750m x 750m)VIIRS (250m x 750m) Defining TIR Saturation Levels Results being used to support VIIRS hardware and software configuration to allow optimum fire detection capabilities

19. Early Assessment of GOES-R/ABI Active Fire Data Fire Mask Proxy ABI (derived from MODIS L1B) Selection of Coincident MODIS and ASTER L1B Data

20. Initial Results ABI Active Fire Product Validated using Reference ASTER Data Probability of Detection (omission) Defined as a Function of ASTER Fire Statistics Results being used to assess and refine pre-flight fire detection algorithm performance and to define routine fire validation strategy for implementation during the post-launch phase ABI GOES MODIS

21. Supporting Science Quality Data Development Regionally 19981999200020012002 20032004200520062007 0100% Fraction of observations obscured by clouds (JAS)

22. 19981999200020012002 20032004200520062007 Supporting Biomass Burning Emissions Products

23. UW-Madison CIMSS Supporting Science Quality Data Development Globally Global Geostationary Fire Monitoring Network

24. Final Remarks Development of MODIS active fire product continues after 10years – new versions incorporating refinements to account for problems identified during the validation analyses NPP/VIIRS pre-flight fire data analyses providing valuable information Thermal infrared band (M15) saturation issues being assessed Impact of pixel aggregation (M15) scheme on fire detection capabilities being quantified – results being used to support modification of platform configuration Results indicate that active fire product could perform better than originally thought GOES-R/ABI pre-flight active fire data assessment setting the stage for routine post-launch product validation Use of fine resolution data building on MODIS experience Science quality data being generated in support of regional and global fire monitoring systems Validation of fire characterization data (size, temp, fire radiative power) – moving beyond the binary (yes-no) fire detection information

25. Pending Support and Future Research ROSES 2010 Remote Sensing Theory: “Derivation of biomass burning properties based on the synergistic use of MODIS and ASTER global data” (PI: W. Schroeder) ROSES 2010 The Science of Terra and Aqua: “MODIS Collection 6 Active Fire Maintenance and Validation” (PI: L. Giglio) ROSES 2010 NPP Science Team for Climate Data Records: “The active fire data record from NPP VIIRS” (PI: I. Csiszar) GOESR3 : “Development of a blended active fire detection and characterization product from geostationary and polar orbiter satellite data” (Csiszar, Schroeder, Justice) Developement and support of fine resolution active fire products derived from Landsat TM, LDCM (2012), ESA Sentinel 2 (2012-2013), and HyspIRI (2017) instruments (Giglio, Csiszar, Schroeder)