This review delves into the advancements in satellite-based wildfire detection systems, highlighting their pivotal role in monitoring remote areas. The evolution of fire detection technology can be traced from the early use of polar-orbiting meteorological satellites in the 1980s to the sophisticated sensors and algorithms employed today. A key breakthrough came with the introduction of the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra and Aqua satellites. MODIS and similar sensors like VIIRS can detect fires by measuring thermal radiation and spectral characteristics. The contextual algorithm used in MODIS fire detection plays a crucial role in improving accuracy by analyzing spatial and temporal factors.
However, these contextual algorithms face limitations, such as the potential to miss small fires and sensitivity to errors in background temperature estimation. To address these challenges, researchers are developing improved fire detection techniques. These include integrating multiple contextual tests, applying machine learning methods, and leveraging auxiliary data sources like topographical information and land cover maps. Efforts are also underway to create harmonized fire detection products across different satellite sensors and to adapt algorithms regionally for improved performance. Advancements in detecting smoldering fires, especially in peatlands, are crucial for better understanding greenhouse gas emissions. This review emphasizes the importance of these continuous improvements in satellite-based fire detection systems for enabling early wildfire identification, facilitating timely response, and ultimately reducing the devastating consequences of wildfires.