A new research paper by Joshua Lizundia-Loiola et al. has been published in Remote Sensing of Environment, describing the FireCCI51 algorithm and product. This product detects more area than other MODIS-based datasets.
This paper presents the generation of a global burned area mapping algorithm using MODIS hotspots and near-infrared reflectance within ESA's Fire_cci project. The algorithm is based on a hybrid approach that combines MODIS highest resolution (250 m) near-infrared band and active fire information from thermal channels. The burned area is detected in two phases. In the first step, pixels with a high probability of being burned are selected in order to reduce commission errors. To do that, spatio-temporal active-fire clusters are created to determine adaptive thresholds. Finally, a contextual growing approach is applied from those pixels to the neighbouring area to fully detect the burned patch and reduce omission errors. The algorithm was used to obtain a time series of global burned area dataset (named FireCCI51), covering the 2001–2018 period. Validation based on 1200 sampled sites covering the period from 2003 to 2014 showed an average omission and commission errors of 67.1% and 54.4%. When using longer validation periods, the errors were found smaller (54.5% omission and 25.7% commission for the additional 1000 African sampled sites), which indicates that the product is negatively influenced by temporal reporting accuracy. The inter-comparison carried out with previous Fire_cci versions (FireCCI41 and FireCCI50), and NASA's standard burned area product (MCD64A1 c6) showed consistent spatial and temporal patterns. However, the new algorithm estimated an average BA of 4.63 Mkm2, with a maximum of 5.19 Mkm2 (2004) and a minimum of 3.94 Mkm2 (in 2001), increasing current burned area estimations. Besides, the new product was found more sensitive to detect smaller burned patches.