Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published April 2024 | Published
Journal Article Open

Fire monitoring and detection using brightness-temperature difference and water vapor emission from the atmospheric infrared sounder

  • 1. ROR icon California Institute of Technology

Abstract

Radiance data from the Atmospheric Infrared Sounder (AIRS) on board NASA's Aqua satellite provide an opportunity for fire detection. As wildfires play an increasingly great role in the environments of the United States West Coast, emergency teams face an ever-challenging task of mitigation and prediction. Furthermore, the increasing rate of wildfires in the American West Coast places an ever-increasing strain on ecosystems and global climate. Of particular interest is the ability to create effective near real-time (NRT) imaging and prediction. Advances in this field can play a crucial role in assisting wildfire detection and monitoring. Typical sources for satellite fire imaging study are the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), the Advanced Very High Resolution Radiometer (AVHRR), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments, but we present AIRS as complementary to these instruments with a new method of hotspot analysis. In this study, we propose a new method of fire detection by using AIRS's cloud-cleared radiance to detect hotspots and find new applications for this method of retrieval. We present the fire detection algorithm and initial assessments showing AIRS's ability to detect fires. In addition, we notice that there is more water vapor from the surface to the upper troposphere during the fire event as a result of biomass burning and an increase in air temperature.

    Copyright and License

    © 2024 Elsevier.

    Acknowledgement

    The authors thank the referees and the editor for their time and constructive suggestions. Y. L. Yung is supported by the NASA OCO-2 project. X. Jiang is supported by NASA ROSES Cassini Data Analysis Program. AIRS Version 7 Level 2 cloud-cleared radiance can be downloaded from https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRS2CCF.7.0/. AIRS Version 7 water vapor mass mixing ratio data can be downloaded from https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRS2RET.7.0/. MODIS burn data can be downloaded from https://lpdaac.usgs.gov/products/mcd64a1v006.

    Contributions

    Jason Yu: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing, Conceptualization. Xun Jiang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Zhao-Cheng Zeng: Formal analysis, Investigation, Methodology, Software, Validation, Writing – review & editing. Yuk L. Yung: Conceptualization, Supervision, Writing – review & editing.

    Data Availability

    Data will be made available on request.

    Conflict of Interest

    The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

    Files

    Files (6.5 MB)
    Name Size Download all
    md5:6c771589426af15a873048989f02f0bb
    6.5 MB Download

    Additional details

    Created:
    March 25, 2024
    Modified:
    March 25, 2024