Biomass burning is one of the key processes affecting vegetation productivity, land cover, soil erosion, hydrological cycles, and atmospheric emissions. Fire is affected by climate, as burning is associated with high to extreme weather conditions. At the same time, fire affects climate too, due to its impacts on carbon budgets and greenhouse gas emissions. These mutual influences between fire and climate explain why Fire Disturbance is considered one of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS) programme GCOS-245, 2022.

The ESA Fire_cci project tackles multiple issues regarding fire disturbance, which is tackled through the analysis of Burned Area (BA). Fire_cci, as part of the ESA climate change initiative, feeds directly into the Copernicus Climate Change Service (C3S).

The products

The data produced within the Fire_cci and C3S projects provide global information of total burned area at full satellite sensor resolution and at reduced resolution. The full resolution (“pixel”) products provide for each pixel the burned area as the date of the first detection of the burned signal, the confidence level indicating the burn probability, and the land cover information. The reduced resolution (“grid”) product provides for each grid cell the total burned area, the standard error, and the burned area per land cover type within the grid cell, all in square meters. The land cover information is taken from the Copernicus Climate Change Service (C3S) land cover dataset, thus strengthening consistency between the datasets.

BC activities

Brockmann Consult have been contributing to the Fire_cci project since 2015. Since then, we produced many datasets within the scope of this project, which are all publicly available at ESA Climate Office.

A remarkable challenge and success within the Fire_cci project has been the production of the Small Fires Database (SFD). Those dataset cover Sub-Saharan Africa, and it is based on Sentinel-2 MSI images at 20m spatial resolution. For more information on the SFD see the respective peer-reviewed paper at

Starting in 2022, Brockmann Consult have produced global Sentinel-3 Synergy-based BA data within the frame of the Fire-CCI project, ranging from 2020 to present. Likewise, Brockmann Consult continues the production of a global MODIS-based BA dataset, ranging from 2001 to present.

C3S adds burned area products based on Sentinel-3 OLCI data, thus offering a consistent global burned area time series from 2001 to present via the Climate Data Store.

Some technical insights

Within Fire_cci and C3S, we at Brockmann Consult take care of different tasks. These are outlined below.

  • Pre-processing algorithm development

The pre-processing removes all radiometric, geometric and atmospheric disturbances, to retrieve the reflectance of the Earth surface. Cloud screening and cloud shadow identification is one of its key challenges, which is the specific domain of expertise of Brockmann Consult (see for example).

  • Algorithm and processing chain implementation

The algorithms that detect burned area from the satellite images have been developed by the scientists in the Fire_cci project team. To allow for production of large datasets, these algorithms have to be integrated into processing systems. This is one of the main tasks of Brockmann Consult within the Fire_cci and C3S projects.

  • Developing and extending processing environments

Since 2009, Brockmann Consult have developed the Calvalus processing system for efficiently processing large datasets. This processing system has been optimised to run different burned-area retrieval algorithms. Calvalus has also been migrated to third-party infrastructures.

  • Product generation

The datasets are of massive size, thus the product generation had to be carefully orchestrated and monitored, aiming to use the respective infrastructure with optimal efficiency to save costs, while also adhering a tight schedule and allowing for breaks within the production to foster quality control.

  • Quality Control

BC perform quality control on different levels. The ongoing production is automatically and manually monitored and supervised to identify anomalies, and to ensure correctness and completeness. On the thematic level we run automatic sanity checks, visualise the data, identify and analyse potential artefacts.

  • Pre-processing algorithm development
  • Algorithm and processing chain implementation
  • Developing and extending processing environments
  • Product generation
  • Quality Control

ESA Fire_cci

EC Copernicus Climate Change Service



ESA, University of Alcala, University of Basque Country, University College London, University of Leicester, School of Agriculture – University of Lisbon, Max Planck Institute for Chemistry, Research Institute for Development, Climate and Environmental Sciences Laboratory, Stichting VU-Vumc, Polytechnic University of Madrid, National Research Council of Italy – Institute for Electromagnetic Sensing of Environment, Max Planck Institute for Meteorology.


ECMWF, VITO, EOLAB, FastOpt, HYGEOS, King’s College London, METEO-France, University of Alcala, Université Catholique de Louvain, University College London


A good summary of the publicly available data is given on the corresponding data website of the ESA Fire_cci project. Additionally, the Fire  products since 2001 are provided via the C3S Climate Data Store.

Images contain modified Copernicus Service information [2023].
FireCCI51, January 2001, global.


FireCCIS310, March 2019, global.
C3S, August 2021, Europe.
Small Fires Database, January 2019, Ghana/Togo.