


Forest managers rely on accurate burn severity estimates to evaluate post-fire damage
and to establish revegetation policies. Burn severity estimates based on reflective data acquired
from sensors onboard satellites are increasingly complementing field-based ones. However, fire
not only induces changes in reflected and emitted radiation measured by the sensor, but also on
energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo
(LSA) are greatly a ected by wildfires. In this study, we examine the usefulness of these elements of
energy balance as indicators of burn severity and compare the accuracy of burn severity estimates
based on them to the accuracy of widely used approaches based on spectral indexes. We studied
a mega-fire (more than 450 km2 burned) in Central Portugal, which occurred from 17 to 24 June 2017.
The o cial burn severity map acted as a ground reference. Variations induced by fire during the first
year following the fire event were evaluated through changes in ET, LST and LSA derived from
Landsat data and related to burn severity. Fisher’s least significant di erence test (ANOVA) revealed
that ET and LST images could discriminate three burn severity levels with statistical significance
(uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA
using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate ( = 0.63
and 0.57, respectively), similar to the accuracy of the estimate based on dNBR ( = 0.66). We conclude
that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting
as useful indicators of burn severity for mega-fires in Medit
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