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Fire Detection Misalignments Between GOES ABI and VIIRS and Their Impact on GOES FDC Evaluation.

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Title: Fire Detection Misalignments Between GOES ABI and VIIRS and Their Impact on GOES FDC Evaluation.
Authors: Vanunu, Asaf1,2,3 (AUTHOR), Fonseca, Rodney2,4 (AUTHOR), Galun, Meirav3,5 (AUTHOR), Nadler, Boaz4,5 (AUTHOR), Karnieli, Arnon2,3,5 (AUTHOR) karnieli@bgu.ac.il
Source: Remote Sensing. Mar2026, Vol. 18 Issue 6, p906. 22p.
Subjects: Geostationary satellites, Constant false alarm rate (Data processing), Statistical accuracy, Environmental monitoring
Abstract: Highlights: What are the main findings? Spatial misalignments between GOES FDC and VIIRS detections occur in approximately 12% of fire events across diverse latitudes and ecosystems. Implementing a buffer significantly mitigates the misalignments impact, reducing estimated false alarm rates from 26–36% down to 7–15%. What are the implications of the main findings? Standard accuracy evaluations that treat VIIRS as ground truth without spatial buffering can yield biased and unreliable estimates. The proposed evaluation scheme is generalizable and can be applied to assess other geostationary and Low Earth Orbit sensor combinations. Wildfires cause major damage, and their accurate detection is crucial. A common approach to near-real-time detection uses Geostationary (GEO) satellite algorithms. A standard scheme for evaluating the accuracy of a GEO-based algorithm is to compare its detections with higher-resolution Low Earth Orbit (LEO) images, considering the latter as ground truth. The primary objective of this study is to quantify the prevalence of GOES ABI/VIIRS fire detection misalignments and assess their impact on the accuracy evaluation of the GOES Fire Detection and Characterization (FDC) product. Thus, the key question is how this evaluation should be performed. To this end, a large dataset of matching FDC/VIIRS fire detections across Western U.S., Amazonas, and Patagonia was constructed. Our finding is that for nearly 12% of fire events, there are spatial misalignments between FDC and VIIRS detections. Next, we show that using VIIRS as ground truth without considering these misalignments yields highly biased estimates. This affects the evaluation of the FDC product detection capabilities. Finally, we demonstrate that using a GOES FDC/VIIRS buffer window substantially mitigates the effect of misalignments. For example, the estimated false alarm rate ranges between 26% and 36% without a window, whereas using a 3 × 3 window yields values between 7% and 15%. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Fire Detection Misalignments Between GOES ABI and VIIRS and Their Impact on GOES FDC Evaluation.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 6, p906. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Geostationary+satellites%22">Geostationary satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Constant+false+alarm+rate+%28Data+processing%29%22">Constant false alarm rate (Data processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink>
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  Data: Highlights: What are the main findings? Spatial misalignments between GOES FDC and VIIRS detections occur in approximately 12% of fire events across diverse latitudes and ecosystems. Implementing a buffer significantly mitigates the misalignments impact, reducing estimated false alarm rates from 26–36% down to 7–15%. What are the implications of the main findings? Standard accuracy evaluations that treat VIIRS as ground truth without spatial buffering can yield biased and unreliable estimates. The proposed evaluation scheme is generalizable and can be applied to assess other geostationary and Low Earth Orbit sensor combinations. Wildfires cause major damage, and their accurate detection is crucial. A common approach to near-real-time detection uses Geostationary (GEO) satellite algorithms. A standard scheme for evaluating the accuracy of a GEO-based algorithm is to compare its detections with higher-resolution Low Earth Orbit (LEO) images, considering the latter as ground truth. The primary objective of this study is to quantify the prevalence of GOES ABI/VIIRS fire detection misalignments and assess their impact on the accuracy evaluation of the GOES Fire Detection and Characterization (FDC) product. Thus, the key question is how this evaluation should be performed. To this end, a large dataset of matching FDC/VIIRS fire detections across Western U.S., Amazonas, and Patagonia was constructed. Our finding is that for nearly 12% of fire events, there are spatial misalignments between FDC and VIIRS detections. Next, we show that using VIIRS as ground truth without considering these misalignments yields highly biased estimates. This affects the evaluation of the FDC product detection capabilities. Finally, we demonstrate that using a GOES FDC/VIIRS buffer window substantially mitigates the effect of misalignments. For example, the estimated false alarm rate ranges between 26% and 36% without a window, whereas using a 3 × 3 window yields values between 7% and 15%. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – TitleFull: Fire Detection Misalignments Between GOES ABI and VIIRS and Their Impact on GOES FDC Evaluation.
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              Text: Mar2026
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