The FireCCI project, as part of the ESA Climate Change Initiative (CCI), has developed and validated burned area (BA) algorithms and products with the objective to meet, as far as possible, GCOS (Global Climate Observing System) Essential Climate Variable requirements for global satellite data products from multi-sensor data archives.The current suite of global products include FireCCI51, whose algorithm uses as input MODIS NIR surface reflectance at 250 m and 1-km-resolution active fires, and currently covers a 20-year time series. An evolution of this algorithm uses the SWIR bands of the Sentinel-3 SLSTR sensor, provided at 300 m resolution by the Synergy products developed by ESA. This input is complemented by VIIRS active fire information at 375 m resolution. The resulting BA product, called FireCCIS310, takes advantage of the improved BA detection capacity of the SWIR bands and the higher resolution of the VIIRS thermal information, apart from upgrades in the algorithm itself. This product is currently available for 2019, and it is being further processed for the subsequent years. FireCCIS310 is capable of detecting 28% more BA than FireCCI51 for the same year.Complementary, a specific dataset has been created for sub-Saharan Africa, where more than 70% of the total global burned area occurs. This product, called FireCCISFD (SFD standing for Small Fire Dataset), uses surface reflectance from the Sentinel-2 MSI sensor at 20 m spatial resolution, supplemented by active fire information. Version 1.1 of this dataset (FireCCISFD11) covers the year 2016 and is based on Sentinel-2A data plus MODIS active fires, while the newer version (FireCCISFD20) has been processed for the year 2019, and takes advantage of the additional data provided by Sentinel-2B, duplicating the input data amount and temporal resolution, and the improved spatial resolution of VIIRS active fires. Due to the much higher spatial resolution of the input data, this product detected 58% more BA than FireCCI51 in 2016, and 82% more in 2019, mostly due to the enhanced detection of small burned patches, not detectable with coarser resolution sensors.All these datasets provide very valuable information regarding land cover change dynamics due to fires, and their associated aerosol and greenhouse gasses emitted to the atmosphere. Particularly, the SFD datasets show that current estimations of fire emissions have been underestimated, and that they should be re-assessed taking into account the capabilities of the information provided by medium to high-resolution sensors.

In recent years, the growing availability of global satellite-derived burned area (BA) products has led to the development of methods and protocols to rigorously estimate their accuracy metrics. These protocols are based on design-based inference and provide unbiased estimators of various dimensions of accuracy. Current procedures consider the spatial and temporal dimension when obtaining the independent reference data used to assess accuracy, commonly based on the Landsat imagery archive as the basic source. The protocol in which the temporal dimension is addressed in the reference data impacts the accuracy metrics. For example, the 8-16-day sampling units usually recommended in Stage 3 BA validation protocols may result in confounding of spatial and temporal classification errors. However, both errors have different implications from a user's perspective, depending on whether the spatial detection or the temporal dating are relevant. While maintaining the fundamentals of current validation protocols, this study presents a new approach based on long temporal reference units (> 48 days) to diminish the influence of temporal reporting (i.e., dating errors) on the spatial accuracy estimates. This methodology is applied to estimate the accuracy of several global BA products for the period 2017-2019, including two European BA products, the FireCCI51 and C3SBA10, and NASA's standard BA product, the MCD64A1 collection 6 (MCD64C6). Global estimates showed similar performance for the three products; BA commission errors ranged from 17.2% +/- 1.1% for C3SBA10 to 19.4% +/- 1.1% for FireCCI51, and BA omission errors ranged from 43.1% +/- 1.9% for FireCCI51 to 49.3% +/- 2.2% for MCD64C6 (+/- values are one standard error). The total burned area was consistently underestimated in all products. These errors are much lower relative to those obtained in recent Stage 3 validation exercises based on short temporal reference units, which estimated global commission and omission errors greater than 40% and 70%, respectively. Thus, this study demonstrates that using long reference units provides a method to address the impact of BA product dating errors on estimates of spatial accuracy metrics, particularly for those products with lower temporal resolution or for areas with greater cloud coverage. Validation methods developed in this study may contribute to improving future protocols adopted by the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup.

The FireCCI project has developed, during the last decade, a suite of burned area products based on remote sensing tailored to the climate researchers and also useful for different fire applications. The different products span from 1982 to 2020, and include global products at 250 m, 300 m, and 0.05-degrees spatial resolution, with derived grid products at 0.25-degree resolution. An additional burned area product at 20 m spatial resolution is available for Sub-Saharan Africa for 2016 and 2019. All products include ancillary information related to the uncertainty of the detection, the observational limitations, and the land cover burned. This presentation describes the characteristics of these products, called FireeCCILT11, FireCCI51, FireCCIS310 and FireCCISFD, and summarizes their similarities and differences, as well as their intended applications.

After more than two decades of successful provision of global burned area data the MODIS mission is near to its end. Therefore, using alternative images to generate moderate resolution burned area maps becomes critical to guarantee temporal continuity of these products. This paper presents the development of a hybrid algorithm based on Copernicus Sentinel-3 (S3) Synergy (SYN) data and Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fires for global detection of burned areas. Using the synergistic and co-located measurements of OLCI and SLSTR instruments on board S3A and S3B, the SYN product offers global, near-daily surface reflectance data at 300 m for both sensors. Our algorithm relied on SYN shortwave infrared (SWIR) bands to compute a multi-temporal separability index that enhanced the burn signal. Active fires from the VIIRS sensor were used to generate spatio-temporal clusters for determining local detection thresholds. Active fires were filtered from those thresholds to obtain the seeds from which a contextual growing was applied to extract burned patches. The algorithm was processed globally for 2019 data to generate a new burned area product, named FireCCIS310. Based on a stratified random sampling, error estimates showed an important reduction of omission errors versus other global burned area products while keeping the commission errors at a similar level (Oe = 41.2% +/- 3.0%, Ce = 19.2% +/- 1.7%). The new FireCCIS310 dataset included 4.99 million km2 for the year 2019, which implied around 1 million more than the precursor FireCCI51 product, based on MODIS 250 m reflectance values. Temporal reporting accuracy was improved as well, detecting 53% of the burned pixels within a 0-1 day difference. Besides, the new product was much less affected by the border effects than FireCCI51, as a result of an improved active fire filtering process. The FireCCIS310 product is accessible through the CCI Open Data Portal (https://climate.esa.int/es/odp/#/dashboard, last accessed on July 2022).

Abstract. This paper presents a new global, operational burned area (BA) product at 300 m, called C3SBA10, generated from Sentinel-3 Ocean and Land Colour Instrument (OLCI) near-infrared (NIR) reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data. This product was generated within the Copernicus Climate Change Service (C3S). Since C3S is a European service, it aims to use extensively the European Copernicus satellite missions, named Sentinels. Therefore, one of the components of the service is adapting previous developed algorithms to the Sentinel sensors. In the case of BA datasets, the precursor BA dataset (FireCCI51), which was developed within the European Space Agency\u0027s (ESA) Climate Change Initiative (CCI), was based on the 250 m-resolution NIR band of the MODIS sensor, and the effort has been focused on adapting this BA algorithm to the characteristics of the Sentinel-3 OLCI sensor, which provides similar spatial and temporal resolution to MODIS. As the precursor BA algorithm, the OLCI\u0027s one combines thermal anomalies and spectral information in a two-phase approach, where first thermal anomalies with a high probability of being burned are selected, reducing commission errors, and then a contextual growing is applied to fully detect the BA patch, reducing omission errors. The new BA product includes the full time-series of S3 OLCI data (2017–present). Following the specifications of the FireCCI project, the final datasets are provided in two different formats: monthly full-resolution continental tiles, and monthly global files with aggregated data at 0.25-degree resolution. To facilitate the use by global vegetation dynamics and atmospheric emission models several auxiliary layers were included, such as land cover and cloud-free observations. The C3SBA10 product detected 3.77 Mkm2, 3.59 Mkm2, and 3.63 Mkm2 of annual BA from 2017 to 2019, respectively. The quality and consistency assessment of C3SBA10 and the precursor FireCCI51 was done for the common period (2017–2019). The global spatial validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 14–22 % and omission errors from 50 to 53 %, similar to those presented by the FireCCI51 product. The temporal reporting accuracy was also validated using 4.7 million active fires. 88 % of the detections were made within 10 days after the fire by both products. The spatial and temporal consistency assessment performed between C3SBA10 and FireCCI51 using four different grid sizes (0.05o, 0.10o, 0.25o, and 0.50o) showed global, annual correlations between 0.93 and 0.99. This high consistency between both products ensures a global BA data provision from 2001 to present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository (DOI: https://doi.org/10.24381/cds.f333cf85 , Lizundia-Loiola et al. (2020a)).

This article presents the burned area (BA) product of the Copernicus Climate Change Service (C3S) of the European Commission. This product, named C3SBA10, is based on the adaptation to Sentinel-3 OLCI images of a BA algorithm developed within the Fire Climate Change Initiative (FireCCI) project, which used MODIS data. We first reviewed the adaptation process and then analysed the results of both products for common years (2017–2019). Comparisons were performed using four different grid sizes (0.05°, 0.10°, 0.25°, and 0.50°). Annual correlations between the two products ranged from 0.94 to 0.99. Global BA estimates were found to be more similar when the two Sentinel-3 satellites were active (2019), as the temporal resolution was closer to that of the MODIS sensor. Global validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 16 and 21% and omission errors from 48 to 50%, similar to those found in the FireCCI product. The temporal reporting accuracy was also validated using 19 million active fires. In total, 87% of the detections were made within 10 days after the fire by both products. The high consistency between both products ensures global BA data provision from 2001 to the present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository.

The validation of low-resolution remote sensing products using high-resolution data may be affected by different error factors that would eventually imply unrealistic accuracy estimations. The usual validation methodologies were designed for high or medium resolution but may be not very adequate for coarse resolution, particularly when trying to separate those errors associated to classification from those related to the actual pixel size. The Pareto Boundary methodology can be a good alternative to discriminate between those two sources of errors. We tested its application to a recently released global burned area product based on AVHRR data. This product was developed within the Fire_cci project of the European Space Agency (ESA). The product, named FireCCITL11, has the coarsest resolution (0.05°) and the longest time series (1982-2018) compared to all other global BA products. Furthermore, FireCCILT11 is the only global BA product without a dichotomy classification which detects BA proportions. The accuracy of the FireCCILT11 was validated by Pareto Boundary and an independent reference dataset of Landsat at 0.05°. FireCCILT11 was usually close to boundary curve or below it, which indicates suitable performance. Commission errors (Ce) were usually lower than Omission errors (Oe) in the time series, like other BA products such as those based on MODIS sensor. Both types of accuracy errors present low values, although there were unbalanced years. Year 2014 showed the lowest errors for the entire time series with balanced errors (Ce = 0.12 and Oe = 0.14).

This paper presents the generation of a global long-term Burned Area (BA) product based on Advanced Very High Resolution Radiometer (AVHRR) images. The BA product was derived from the Land Long Term Data Record (LTDR), which provides a continuous dataset of geometrically and radiometrically corrected AVHRR images at 0.05° resolution (≈5 km). The BA algorithm improves a Beta version of this dataset (named FireCCILT10) previously released. The new version incorporates an enhanced Random Forest (RF) classification process based on two models, one using a global sample and another one using only Boreal regions. Solar zenith angle (SZA) corrections were introduced to mitigate the impact of satellite orbital drift. Binary classifications were obtained applying probability thresholds, and BA proportions were assigned to each burned pixel. The final product includes the date of detection at 0.05° resolution and the total burned area at 0.05° and 0.25° resolution, both covering the period from 1982 to 2018 (excluding 1994). The resulting product, called FireCCILT11, estimated that 165.26 Mkm2 were globally burned between 1982 and 2018, with an annual average of 4.59 Mkm2. The largest BA was found in 2011 with 5.18 Mkm2 and the lowest was observed in 1991 with 4.09 Mkm2. The month with the highest mean BA was August, with 0.63 Mkm2, and the one with the lowest was March with 0.15 Mkm2. Africa included 66% of total BA. Inter-comparison showed high correlation values with MODIS BA products for annual BA of the common years (r > 0.6, %MAE < 14%). Comparison with national fire statistics of Australia, Canada and Alaska showed also high correlation values (r > 0.8, %MAE < 42%).

Abstract. This paper presents a new global, operational burned area (BA) product at 300 m, called C3SBA10, generated from Sentinel-3 Ocean and Land Colour Instrument (OLCI) near-infrared (NIR) reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data. This product was generated within the Copernicus Climate Change Service (C3S). Since C3S is a European service, it aims to use extensively the European Copernicus satellite missions, named Sentinels. Therefore, one of the components of the service is adapting previous developed algorithms to the Sentinel sensors. In the case of BA datasets, the precursor BA dataset (FireCCI51), which was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI), was based on the 250 m-resolution NIR band of the MODIS sensor, and the effort has been focused on adapting this BA algorithm to the characteristics of the Sentinel-3 OLCI sensor, which provides similar spatial and temporal resolution to MODIS. As the precursor BA algorithm, the OLCI's one combines thermal anomalies and spectral information in a two-phase approach, where first thermal anomalies with a high probability of being burned are selected, reducing commission errors, and then a contextual growing is applied to fully detect the BA patch, reducing omission errors. The new BA product includes the full time-series of S3 OLCI data (2017–present). Following the specifications of the FireCCI project, the final datasets are provided in two different formats: monthly full-resolution continental tiles, and monthly global files with aggregated data at 0.25-degree resolution. To facilitate the use by global vegetation dynamics and atmospheric emission models several auxiliary layers were included, such as land cover and cloud-free observations. The C3SBA10 product detected 3.77 Mkm2, 3.59 Mkm2, and 3.63 Mkm2 of annual BA from 2017 to 2019, respectively. The quality and consistency assessment of C3SBA10 and the precursor FireCCI51 was done for the common period (2017–2019). The global spatial validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 14–22 % and omission errors from 50 to 53 %, similar to those presented by the FireCCI51 product. The temporal reporting accuracy was also validated using 4.7 million active fires. 88 % of the detections were made within 10 days after the fire by both products. The spatial and temporal consistency assessment performed between C3SBA10 and FireCCI51 using four different grid sizes (0.05º, 0.10º, 0.25º, and 0.50º) showed global, annual correlations between 0.93 and 0.99. This high consistency between both products ensures a global BA data provision from 2001 to present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository (DOI: https://doi.org/10.24381/cds.f333cf85, Lizundia-Loiola et al. (2020a)).

Tropical forests are known for hosting about half of the global biodiversity, and therefore are considered to be a fundamental part of the Earth System. However, in the last decades, the anthropogenic pressure over these areas has been continuously increasing, mostly linked to agricultural expansion. This has created great international concern, which has crossed the limits of national policies. A clear example was the last crisis suffered this year (2019) in the Amazon, and in general, in tropical South America (SA), due to the increasing fire activity in the region, which is strongly linked to deforestation and forest degradation. International media extensively informed the world about fire activity based upon active fire data, which provided quick but incomplete information about the actual fire-affected areas. This short paper compares fire occurrence estimations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data of active fires and from burned area products for the first 10 months of 2019 in SA. Results show a significant increase in fire activity over the full-time series (2001–2018) in Bolivia, Paraguay and Venezuela, while Brazil shows a much higher BA than in 2018, but with values around the average burned area of the whole time series.

The authors wish to make the following corrections to this paper [...]

Comprehensive fire surveillance will strengthen resilience and adaptation to climate change.

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 FireCC151), 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 (FireCC141 and FireCC150), 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 Mkm(2), with a maximum of 5.19 Mkm(2) (2004) and a minimum of 3.94 Mkm(2) (in 2001), increasing current burned area estimations. Besides, the new product was found more sensitive to detect smaller burned patches. This new product, called FireCC151, is publicly available at: http://cci.esa.int/data, last accessed on September 2019.

This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images. Daily images were combined in monthly composites using the maximum temperature criterion to enhance the burned signal and eliminate clouds and artifacts. A synthetic BA index was created to improve the detection of the BA signal. This index included red and near infrared reflectance, surface temperature, two spectral indices, and their temporal differences. Monthly models were generated using the random forest classifier, using the twelve monthly composites of each year as the predictors. Training data were obtained from the NASA MCD64A1 collection 6 product (500 m spatial resolution) for eight years of the overlapping period (2001–2017). This included some years with low and high fire occurrence. Results were tested with the remaining eight years. Pixels classified as burned were converted to burned proportions using the MCD64A1 product. The final product (named FireCCILT10) estimated BA in 0.05-degree cells for the 1982 to 2017 period (excluding 1994, due to input data gaps). This product is the longest global BA currently available, extending almost 20 years back from the existing NASA and ESA BA products. BA estimations from the FireCCILT10 product were compared with those from the MCD64A1 product for continental regions, obtaining high correlation values (r2 > 0.9), with better agreement in tropical regions rather than boreal regions. The annual average of BA of the time series was 3.12 Mkm2. Tropical Africa had the highest proportion of burnings, accounting for 74.37% of global BA. Spatial trends were found to be similar to existing global BA products, but temporal trends showed unstable annual variations, most likely linked to the changes in the AVHRR sensor and orbital decays of the NOAA satellites.

This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001-2016) of the Terra-MODIS archive. The BA detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one targeting to reduce omission errors by applying contextual analysis around the seed pixels. This product was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA files: monthly full-resolution continental tiles and biweekly global grid files at a degraded resolution of 0.25 degrees. Each set of products includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and atmospheric emission models. Average annual burned area from this product was 3.81 Mkm(2), with maximum burning in 2011 (4.1 Mkm(2)) and minimum in 2013 (3.24 Mkm(2)) The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The validation indicates an overall accuracy of 0.9972, with much higher errors for the burned than the unburned category (global omission error of BA was estimated as 0.7090 and global commission as 0.5123). These error values are similar to other global BA products, but slightly higher than the NASA BA product (named MCD64A1, which is produced at 500 m resolution). However, commission and omission errors are better compensated in our product, with a tendency towards BA underestimation (relative bias -0.4033), as most existing global BA products. To understand the value of this product in detecting small fire patches (< 100 ha), an additional validation sample of 52 Sentinel-2 scenes was generated specifically over Africa. Analysis of these results indicates a better detection accuracy of this product for small fire patches (< 100 ha) than the equivalent 500 m MCD64A1 product, although both have high errors for these small fires. Examples of potential applications of this dataset to fire modelling based on burned patches analysis are included in this paper. The datasets are freely downloadable from the Fire_cci website (https://www.esa-fire-cci.org/, last access: 10 November 2018) and their repositories (pixel at full resolution: https://doi.org/cpk7, and grid: https://doi.org/gcx9gf).

Uncertainty characterisation and validation are critical phases to generate any Essential Climate Variable (ECV), and therefore both have been included as key deliverables of the ESA CCI programme [1]. All products generated by the CCI are required to have an associated per pixel uncertainty characterisation. This paper describes both the uncertainty characterisation framework and the related uncertainty validation exercise of the Fire-CCI project.