@article{fdi:010091890,
  title = {{E}nhancing burned area monitoring with {VIIRS} dataset : a case study in {S}ub-{S}aharan {A}frica},
  author = {{O}uattara, {B}. and {T}hiel, {M}. and {S}ponholz, {B}. and {P}aeth, {H}. and {Y}ebra, {M}. and {M}ouillot, {F}lorent and {K}acic, {P}. and {H}ackman, {K}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}ince 2001, the {M}oderate {R}esolution {I}maging {S}pectroradiometer ({MODIS}) sensor on board the {A}qua and {T}erra platforms has made great strides in providing information on global burned areas ({BA}). {H}owever, the {MODIS} mission is nearing its end. {T}he {V}isible {I}nfrared {I}maging {R}adiometer {S}uite ({VIIRS}) sensors, presented as the {MODIS} {A}qua heritage, could be an excellent alternative to ensure the temporal continuity of this information at a moderate resolution. {T}his paper describes and evaluates the effectiveness of our developed hybrid algorithm, which utilizes {VIIRS} reflectance and active fire products on the {G}oogle {E}arth {E}ngine platform, in producing efficient information about {BA}. {T}he study investigates the algorithm's performance in sub-{S}aharan {A}frica as the region of interest in 2019, using biweekly outputs and a spatial resolution of 250 m. {T}he algorithm encompasses several steps, including pre-processing individual scenes, creating cloud-free composites, generating binary reference data for burned and non-burned areas, conducting a supervised classification using random forest, and performing region shaping. {T}he {VIIRS}-{BA} final product, which includes three confidence levels (low, moderate, and high) known as the uncertainty layer, is compared to four other burned area products. {T}he validation is conducted against 27 reference sampling units from the {S}entinel-2 {B}urned {A}rea {R}eference {D}atabase dataset, allowing for a comprehensive uncertainty assessment across five various biomes. {T}he {VIIRS}-{BA} product identified 5.1 million km(2) of {BA}, which was significantly larger than other global coarse resolution {BA} products such as {F}ire{CCI}51, {F}ire{CCIS}310, and {MCD}64{A}1 and close to the fine resolution {F}ire{CCISFD}20 with a difference of 7.3%. {T}he differences were less significant in biomes such as "{T}ropical {S}avannas" and "{T}emperate {G}rasslands" which are characterized by persistent biomass burning. {B}ased on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the {VIIRS}-{BA} product across different confidence levels. {T}he commission error ({CE}) ranges from 7.8% to 23.4%, while the omission error ({OE}) falls between 29.4% and 58.8%. {N}otably, there is a significant reduction in {OE} (ranging from 40.7% to 50.5%) compared to global {BA} products like {F}ire{CCI}51, {F}ire{CCIS}310, and {MCD}64{A}1. {W}hen compared to {VIIRS}-{BA}, the {F}ire{CCISFD}20 regional product has a 37% better {OE} performance. {W}hile {VIIRS}-{BA} shows great potential in detecting fires that global products miss, the {VIIRS}-{BA} with low confidence level tends to overestimate {BA} in regions with high fire activity. {T}o address this, future versions of the algorithm will integrate the updated {VIIRS} reflectance data alongside {VIIRS} active fire from the {N}ational {O}ceanic and {A}tmospheric {A}dministration to reduce {CE} and improve understanding spatial patterns.},
  keywords = {{B}urned area mapping ; {VIIRS} ; {A}ctive fires ; {G}oogle earth engine ; {A}frica ; {AFRIQUE} {SUBSAHARIENNE}},
  booktitle = {},
  journal = {{S}cience of {R}emote {S}ensing},
  volume = {10},
  numero = {},
  pages = {100165 [19 p.]},
  ISSN = {2666-0172},
  year = {2024},
  DOI = {10.1016/j.srs.2024.100165},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091890},
}