A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine

A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those then used to estimate the burn probability for each scene. burning dates temporal evolution probabilities. processed, its quality assessed globally using reference data from 2019 derived 10 m, which involved 369 pairs consecutive images total located 50 20 × km2 areas selected stratified random sampling. Commissions were 10% with both satellites, although omissions ranged between 27 (Sentinel-2) 35% (Landsat), depending dataset, highest being croplands forests; their part, BA m most accurate fastest process. In addition, three 5 degree regions randomly biomes where fires occur, detected m. Comparison products coarse FireCCI51 MCD64A1 would seem show reliable extent that is procuring spatially temporally coherent results, improving detection smaller as consequence higher-spatial-resolution data. proposed has shown potential map medium-spatial-resolution (Sentinel-2 Landsat) 2000 onwards, when satellites launched.