@article{fdi:010074408,
  title = {{G}eneration and analysis of a new global burned area product based on {MODIS} 250 m reflectance bands and thermal anomalies},
  author = {{C}huvieco, {E}. and {L}izundia-{L}oiola, {J}. and {P}ettinari, {M}. {L}. and {R}amo, {R}. and {P}adilla, {M}. and {T}ansey, {K}. and {M}ouillot, {F}lorent and {L}aurent, {P}. and {S}torm, {T}. and {H}eil, {A}. and {P}lummer, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his paper presents a new global burned area ({BA}) product, generated from the {M}oderate {R}esolution {I}maging {S}pectroradiometer ({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. {T}he product includes the full times series (2001-2016) of the {T}erra-{MODIS} archive. {T}he {BA} detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. {T}he 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. {T}his product was developed within the {E}uropean {S}pace {A}gency's ({ESA}) {C}limate {C}hange {I}nitiative ({CCI}) programme, under the {F}ire {D}isturbance project ({F}ire_cci). {T}he 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. {E}ach 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. {A}verage annual burned area from this product was 3.81 {M}km(2), with maximum burning in 2011 (4.1 {M}km(2)) and minimum in 2013 (3.24 {M}km(2)) {T}he validation was based on a stratified random sample of 1200 pairs of {L}andsat images, covering the whole globe from 2003 to 2014. {T}he 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). {T}hese error values are similar to other global {BA} products, but slightly higher than the {NASA} {BA} product (named {MCD}64{A}1, which is produced at 500 m resolution). {H}owever, 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. {T}o understand the value of this product in detecting small fire patches (< 100 ha), an additional validation sample of 52 {S}entinel-2 scenes was generated specifically over {A}frica. {A}nalysis of these results indicates a better detection accuracy of this product for small fire patches (< 100 ha) than the equivalent 500 m {MCD}64{A}1 product, although both have high errors for these small fires. {E}xamples of potential applications of this dataset to fire modelling based on burned patches analysis are included in this paper. {T}he datasets are freely downloadable from the {F}ire_cci website (},
  keywords = {},
  booktitle = {},
  journal = {{E}arth {S}ystem {S}cience {D}ata},
  volume = {10},
  numero = {4},
  pages = {2015--2031},
  ISSN = {1866-3508},
  year = {2018},
  DOI = {10.5194/essd-10-2015-2018},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074408},
}