Users should note the following data quality considerations impacting the WF-ABBA and FDC products:

• The WF-ABBA and FDC data are generated by automated algorithms and posted “as is” to the public ftp in near real-time. To access the data, visit the fire products archive and search for “ABBA” product). For a quality controlled product, users are referred to the HMS product created by satellite data analysts. An individual active fire detection location describes a GOES image element (pixel) therefore the fire pixel’s area/centroid may not coincide with the actual fire perimeter/center coordinate. In fact, the vast majority of fires detected will be sub-pixel in size (see omission error comments below). As a result, users must apply satellite fire detection coordinates judiciously understanding the actual fire could be anywhere within the relatively large GOES pixel footprint.
• Commission errors (false alarms) may be observed in the satellite fire products due to ambiguity between actively burning fires and other thermal anomalies predominantly found during the sunlit part of the day. Those occurrences are typically associated with fresh burn scars, exposed sandy soils and impervious surfaces in the urban environment. Other false alarm instances may be associated with Sun glint occurrence over optically bright and/or specular surfaces (e.g., large solar panel clusters, clouds, and water bodies). Users must also note that thermal anomalies linked to industrial activities (e.g., steel mills, gas flaring) and other structural fires in urban environment may be present in the WF-ABBA and FDC data; those occurrences are normally removed from the quality-controlled HMS product.
• Omission errors will vary depending on the observation conditions and the fire characteristics. Detection performance is directly affected by the effective pixel area on the ground. Normally, active fires (mean temperature >800 K) must occupy a portion greater than 0.01% of the effective pixel area in order to generate a distinguishable signal. Pixel areas can grow substantially away from nadir requiring larger/higher temperature sources in order to produce a high confidence fire detection; users should expect a decrease in detection performance with distance from the sub-satellite point. The presence of optically thick clouds will also prevent the emitted fire radiance from reaching the satellite sensor, leading to additional omission errors. Smoke from biomass burning is rather transparent to mid- and long-wave infrared radiation, therefore it will not prevent a fire detection. However, large plumes containing high amounts of water vapor (e.g., pyrocumulus) may be confused for optically thick clouds leading to additional omission error.
• Fire size (km2) and temperature (K) retrievals calculated using the bi-spectral method of Dozier [1981] can show large random errors resulting from intrinsic limitations with the technique [Giglio and Kendall, 2001; Schroeder et al., 2010]. Effectively, only a small portion of the retrievals may be considered of sufficient accuracy based on sensitivity analysis implemented for similar retrievals using MODIS data [Giglio and Schroeder, 2014]. Alternatively, users might resource to the companion FRP (MW) retrievals that provide proxy fire intensity information of higher consistency across different fire/observation conditions and also among data sets [Roberts et al., 2011; Schroeder et al., 2014]. A value of “-9” indicates a failed sub-pixel fire characterization retrieval due to insufficient/saturated data.
• All data files processed are posted online. Observation hours without fire pixels will show a single line file containing the header information (field labels) only.

References

Dozier, J. (1981). A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment, 11, 221-229pp.

Giglio, L., and Kendal, J.D. (2001). Application of Dozier retrieval to wildfire characterization: A sensitivity analysis. Remote Sensing of Environment, 77, 34-49pp.

Giglio, L., and Schroeder, W. (2014). A global feasibility assessment of the bi-spectral fire temperature and area retrieval using MODIS data. Remote Sensing of Environment, 152, 166-173pp.

Prins, E.M., Feltz, J.M., Menzel, W.P., and Ward, D.E. (1998). An overview of GOES-8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America. Journal of Geophysical Research-Atmospheres, 103, 31,821-31,835pp.

Prins, E. and Menzel, W.P (1992). Geostationary satellite detection of biomass burning in South America. International Journal of Remote Sensing, 13(15), 2783-2799pp.

Roberts, G., Wooster, M.J., Freeborn, P.H., Xu, W. (2011). Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory. Remote Sensing of Environment, 115, 2047-2061pp.

Schroeder, W., Csiszar, I., Giglio, L., Schmidt, C.C. (2010). On the use of fire radiative power, area, and temperature estimates to characterize biomass burning via moderate to coarse resolution remote sensing data in the Brazilian Amazon. Journal of Geophysical Research, 115(D21121), doi: 10.1029/2009JD013769.

Schroeder, W., Ellicott, E., Ichoku, C., Ellison, L., Dickinson, M.B., Ottmar, R.D., Clements, C., Hall, D., Ambrosia, V., Kremens, R. (2014). Integrated active fire retrievals and biomass burning emissions using complementary near-coincident ground, airborne and spaceborne sensor data. Remote Sensing of Environment, 140, 719-730.

Schroeder, W., Prins, E., Giglio, L., Csiszar, I., Schmidt, C., Morisette, J., and Morton, D. (2008a). Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data. Remote Sensing of Environment, 112(5), 2711-2726pp.

Schroeder, W., Ruminski, M., Csiszar, I., Giglio, L., Prins, E., Schmidt, C. and Morisette, J. (2008b). Validation analyses of an operational fire monitoring product: The Hazard Mapping System. International Journal of Remote Sensing, 29 (20), 6059-6066pp.

Wooster, M.J., Zhukov, B., and Oertel, D. (2003). Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products. Remote Sensing of Environment, 86, 83-107pp.

For questions concerning the WF-ABBA/FDC product, you may contact the fire team at SPSD or the algorithm developer.