Forest fires have a major effect on ecosystems in terms of disturbance and change. They significantly affect atmospheric chemistry, air quality, and land surface properties, triggering vegetation loss and potentially negatively affecting economies. Reduced vegetation cover could cause soil erosion, for example by making burned areas more vulnerable to runoff during rain events, thereby increasing the risk of downstream flooding. Furthermore, burned areas are also exposed to landslides caused by the loss of adhesion of plant roots to the soil. Thus, accurate quantitative detection of burned areas is crucial to assess damage and plan vegetation restoration to reduce the mentioned hazardous effects in advance. Satellite earth observation data are extensively used to map burned areas, where multispectral sensors play a major role due to the changing reflectance properties between vegetation and burned areas. Vegetation has a typical reflectance curve which makes it easy to distinguish from other types of surfaces. In the visible spectroscopy VIS it is a peak in the green caused by the absorption of chlorophyll in the blue and red, while in the near-infrared (NIR) the reflectance increases due to scattering processes in the leaves to the NIR-Plateau. This spectral shape is unique for vegetation. In the case of a fire with burning large area of vegetation, the reflectance curve will have a different shape, without a green peak or NIR Plateau. This may look like a soil spectrum with moderate reflectance in the VIS, somewhat higher in the NIR, and moderate reflectance in the shortwave infrared (SWIR). These reflectance changes will be detected by satellites covering the spectral range from VIS to SWIR. Operational services using multispectral images are now available, for example the Copernicus Emergency Management Service (CEMS), which provides burned areas at different spatial resolutions (from medium-high to very high) using optical sensors. Sentinel-2 multispectral data can systematically produce maps of burned areas at medium to high spatial resolution and at the same time mitigate the cloud cover problem in some geographic areas thanks to its high revisit time of 5 days.