Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. The demonstrated approach successfully differentiates fire pixels and smoke plumes from background imagery, enabling the generation of a per-instrument smoke and fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has a potential to enhance operational wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification and tracking and could improve climate impact studies through fusion data from inditendent instruments.
Use:
On the data input, input geometrically and radiometrically calibrated radiance data has been pulled from various NASA and Planet archives. For instruments that have multiple different spatial resolutions within their spectral bands (GOES), all bands have been resampled to the lowest collective spatial resolution.
Geometric and radiometric calibration has been done by the science data processing pipelines of the various missions, and would not need to be done by anyone else looking to curate the same data. Further information for each instrument can be found in each of the publicly available Level-1 algorithm theoretical basis documents (ATBDs)
All input and label data have been put in GeoTiff format. Each band is in a separate raster band and each scene is in a separate GeoTiff file. Label files and input files are in separate tar files, labeled respectively, and the file names match for input and labels, with the exception of an additional .fire and .smoke in the respective label filenames and subfolders.
The GeoTiff data format natively contains geolocation metadata internally, and can be interfaced with via C/C++/Python GDAL packages, or other python packages that wrap GDAL, like rasterio and rioxarray . The documentation for SIT-FUSE , the package with which the labels were generated, also has examples on how to read and interface with various data formats, including GeoTiffs. Lastly, this data can be interfaced with using Geographic Information Systems (GIS), like the free and open-source QGIS.
An example of programmatic data access and usage can be found in the dataset's associated GitHub repository.
Timing information can be found in the file names, which all use the standard formats from the various instruments' L1B datasets.