GOES-R ABI Fire Detection and Characterization Algorithm Assessment Using MODIS and ASTER Data

The future GOES-R ABI fire detection and characterization algorithm builds on the Wildfire Automated Biomass Burning Algorithm (WF-ABBA) which was designed and implemented for the GOES imager series starting with GOES-8. The WF-ABBA product provides routine detection and characterization of sub-pixel active fires serving the fire management community as well as the scientific community, therefore demanding quality data with well characterized sources of errors. Validation of satellite fire products requires simultaneous observations in order to reduce the effects of short term variations in fire conditions. Previous studies have used higher spatial resolution satellite data to validate moderate-to-coarse resolution fire products derived from sensors mounted on the same orbital platform (e.g., MODIS and ASTER) as well as on separate platforms by limiting the time difference between acquisitions (e.g, GOES and Landsat ETM+). By adapting the validation methods developed for GOES and MODIS fire products to GOES-R, this study uses higher resolution remote sensing data to assess and validate the ABI fire detection and characterization algorithm. MODIS simulated ABI proxy data produced by CIMSS is used to generate fire detection and characterization for fires simultaneously imaged by ASTER at 60m resolution. A large selection of test sites is analyzed covering a broad range of vegetation fire conditions across the western hemisphere. Detailed fire algorithm performance is derived for GOES-R ABI, including probability of detection curves, omission and commission errors, as well as preliminary assessment of sub-pixel fire characterization (size).