## 1 Introduction  
Fire activity is a global phenomenon characterized by strong spatial and temporal variability. Wildfires are an important disturbance to the ecosystem, resulting in land cover alteration and change, and atmospheric emissions on multiple time scales. Catastrophic wildfires in recent years have again given a clear message that, in the deployment of limited suppression resources, sound fire management decisions (including justifications of a possible severity ranking) rely very much on timely and detailed information on the location, intensity, direction and rate of spread of the fire fronts. In general, Earth observation information on wildfire activity, among others, is used for:  
* Global climate change research, estimating atmospheric emissions and developing periodic global and regional assessments (i.e., quantification of the fire impact on climate);* Fire management and ecosystem management planning, including post-fire recovery efforts and wildland management;
* Operational purposes (preparedness and wildfire suppression);
* The development of informed policies. Satellite Earth observation is appropriate to provide repetitive data at spatial and temporal scales necessary for the detection and monitoring of wildfires [1]. \"Fire Disturbance\" is one of several land-oriented Essential Climate Variables (ECVs) defined by the Global Terrestrial Observing System (GTOS), and this ECV comprises three sub-variables: Burned Area, Active Fire Detection (i.e., locating active fires) and Fire Radiative Power (FRP). This \"trinity\" reflects, among other things, data availability and the \"growing process\" of deriving higher-quality fire data products for estimating carbon emissions from vegetation fires:  
* There have been and continue to be many satellite sensors whose imagery data are suitable for determining burned areas from wildfires. However, fire emissions can only be estimated from the burned area data with uncertainty. * There are a major number of satellite sensors whose image data in the MIR band allow for the detection of active fires. However, their MIR band data are often not suitable for quantitative characterization of the fires, in part due to saturation effects in their signal tracts. * There are only a few infrared satellite sensors that: (i) Do not suffer from saturation effects when recording extended and flaming 1000 K (727 \({}^{\circ}\)C) hot fires; (ii) Simultaneously resolve the temperature of the \"cold\" backgrounds (of the fires) with an accuracy of or better than 0.5 K.  
Only data from the MIR and TIR bands of these few infrared satellite sensors allow the derivation of quantitative fire attributes, such as the FRP. From the FRP, carbon emissions from the fires can be estimated with much higher accuracy than from, for example, the burned area datasets [1]. Comment: Multiple FRP observations of a fire cluster and the consecutive time integration of these FRP values can provide an estimation of the Fire Radiative Energy (FRE), which is a direct measure of the total CO\({}_{2}\) emitted by the fire during the whole observation period. The ratio of the FRP of a single fire front to its length is called the Fire Line Strength (FLS), which is a measure of the fire's severity. The FLS is directly related to the fire's impact on the ecosystem. There are two crucial requirements for IR sensor (the term \"IR sensors\", as used in this paper, stands for imaging infrared sensors with at least one spectral band in the mid-IR band (MIR at 3.8 \(\upmu\)m) and one spectral band in the thermal IR band (TIR, between 8 and 12 \(\upmu\)m)) data to be used for the derivation of the FRP:  
* The signal must be free from saturation effects;
* The radiometric precision of the sensor must allow for the distinguishment between pixels occupied by fires, even small ones, and \"background pixels not touched\" by fires.