## 2 Data and Pre-Processing
### Sentinel-2 Image Pre-Processing  
The Sentinel-2 L1C tiles have a complex structure. In particular, L1C tiles from the same MSI swath overlap spatially and a significant proportion may be defined in different UTM zones, i.e., in separate map projections, making reprojection and resampling of the data quite complicated [40; 41]. However, in this study, only pairs of Sentinel-2A images sensed over the same L1C tile location were used, so no reprojection was necessary. The pixels for each image pair aligned within the Sentinel-2A geolocation performance specification of 12.5 m (3\(\sigma\)) [42].  
The L1C products are provided as TOA reflectance. Atmospheric correction from TOA to surface reflectance is usually considered as a requirement for change detection applications [43]. The impact of the atmosphere is variable in space and time, and is dependent not just on the atmospheric constituents, but also on the surface reflectance. This is because multiple scattering of reflected radiation between the surface and the atmosphere introduces a dependency between the surface reflectance and the atmospheric contribution to the TOA reflectance [44; 45]. Consequently, atmospheric correction of the data was undertaken as both the atmosphere and the surface changes between the Sentinel-2A acquisition dates. Simple image-wide atmospheric correction methods, such as the dark-object subtraction (DOS) method [46], are inappropriate for this study as smoke aerosols and pyrogenic emissions may be quite dynamic and spatially heterogeneous [24]. Consequently, the data were atmospherically corrected to surface reflectance using the recent radiative transfer based SEN2 COR atmospheric correction software (Version 2.2.1, released 4 May 2016) run using the default parameter settings [47]. The SEN2 COR code also produces a 20 m cloud product that was used qualitatively to help screen cloudy pixels. To date, no quantitative validation of the SEN2 COR atmospheric correction and cloud mask product has been published. The Sentinel-2 has a 20.6\({}^{\circ}\) field of view (FOV) and so bi-directional reflectance effects (due to viewing and solar geometry variations and surface reflectance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Location** & **L1C Tile ID** & **Acquisition** & **Acquisition** & **Predominant** \\  & **Date 1** & **Date 2** & **Vegetation Type** \\ \hline Australia, Northern Territory, near Fish River Block Gorge National Park & 52 LFK & 10 April 2016 & 30 April 2016 & Savannas \\ \hline Cambodia, Kraté region, near Snoul & 48 PXU & 7 January 2016 & 17 January 2016 & Woody savannas \\ \hline Canada, Alberta, Fort McMurray & 12 VVH & 2 May 2016 & 12 May 2016 & Evergreen needle leaf forest \& Mixed forests \\ \hline Guinea, Faranah Prefecture, Parc National du Haut Niger Colombia, Casanare Department, northern side of the Rio Cravo Sur river & 18 NZL & 17 January 2016 & 6 Feburary 2016 & Savannas \& Grassland \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2A Multi Spectral Instrument (MSI) L1C tile data used.  
anisotropy), which are present in similar spatial resolution and smaller field of view SPOT [48] and Landsat [49] data are likely to be present. However, bi-directional reflectance effects were not corrected for in this study.