Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

Abstract. This paper presents a new global burned area (BA) product, generated from the MODIS red (R) and near infrared (NIR) reflectances and thermal anomalies 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 MODIS archive. The BA detection 20 algorithm was based on temporal 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 aiming to reduce omission errors by applying contextual analysis around the seed pixels. The product was developed within the European Space Agency's (ESA) Climate Change Initiative programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA products: monthly full-resolution continental tiles (http://doi.org/cpk7) and biweekly global grid files at a degraded resolution of 0.25 degrees (http://doi.org/gcx9gf). Each one includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and emission models. The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The estimated commission and omission error rates of the pixel product was 0.512 (0.020) and 0.708 (0.030), respectively, lower 30 than previous ESA products but higher than the latest NASA MCD64A1 BA dataset. Examples of potential applications of this product to fire modelling based on burned patches analysis are included in this paper. They show greater sensitivity of our product to small burn patch detection than existing BA products.