Fire has many roles around the world. It is a naturally occurring ecological process in fire-prone regions and a
tool humans use for land and resource management. However, fires and their emissions continue to have more extreme
impacts as human settlements expand, climatic conditions become less predictable, and fire seasons lengthen (Jolly
et al. 2015). To better identify and quantify the effects of fires across the globe, it is vital to monitor fires
using various methods, including hand-drawn maps, ground-based sensors, GPS tracking, aerial surveys, imagery
collection, and satellite-based data (Andela et al. 2019; Archibald et al. 2009; Nogueira et al. 2016; Stinson et
al. 2011; Veraverbeke et al. 2014).
Different sources of satellite imagery can be used to visualize fire conditions and progressions, calculate band
ratios reflecting disturbance and fire severity, and map burned areas with training data-informed classification
algorithms. Many premade fire datasets are readily available for monitoring global fire locations, extents, and
progressions. In the case of existing fire datasets available for large spatial and temporal extents, remote sensing
scientists apply their robust classification algorithms to satellite imagery and other geospatial data. Earth Engine
makes fire monitoring more accessible by sharing multiple data sources in the data catalog so users can easily
access and process them to meet their desired objectives.