5B.3 A Pathway to Optimize GOES-R ABI Hot Spot Detection and Fire Monitoring Using ABI L1Beta Imagery

Tuesday, 30 January 2024: 9:00 AM
301 (The Baltimore Convention Center)
Monica Cook, GeoThinkTank LLC, Miami, FL; and F. Padula, E. Bacon, D. Pogorzala, A. J. Pearlman, M. Pavolonis, and D. Lindsey

The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series includes spectral bands in both the visible and infrared that are used to locate and retrieve sub-pixel characteristics of fires. Forecasters use this data to detect hot spots and monitor changes in wildfires in near real-time. ABI ground processing to produce L1b radiance imagery includes a process to resample calibrated and navigated ABI data to a fixed grid such that every pixel in the image represents the same location on the earth in every L1b and L2 data product produced. While the data resampling step is beneficial to many ABI products, the operationally implemented resampling algorithm is not optimized for the detection of point sources such as fire or hot spot detection. As a result, artifacts in the L1b imagery (such as artificial cold pixels around fires), as well as some information captured in the L1Beta imagery (calibrated and navigated, but not resampled to the fixed grid), may be not be best represented in L1b after the resampling process. This work explored ways to maximize the information content of GOES-R ABI observations for fire detection and monitoring through the use of L1Beta imagery, generated using offline ground processing methods to calibrate and navigate ABI swath imagery, prior to the operational resampling algorithm being applied to the data. An ABI image processing chain was developed to systemically produce L1Beta swath imagery. Using this approach, multiple wildfire and hot spot cases were investigated. The differences in radiometric, spatial, and temporal fidelity between L1Beta and L1b imagery are summarized to illustrate the advantages of a potential L1Beta product stream for hot spot detection and fire monitoring. Use of this approach provides a pathway to optimize the observational capabilities of ABI and future geostationary instruments (such as GeoXO’s GXI) for fire monitoring and potential enhancements to a critical Earth surface product from geostationary orbit.
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