## Materials and Methods
### Burnt Areas Detection
The NDVI is calculated using the NIR band 8 (842 nm) and the Red band 4 (665 nm). The recent passage of fire causes a drop in the two indices' values (the respective thresholds for the difference value are set to 0.18 for NDWI and 0.19 for NDVI). Very few fires occurred before the 12 January 2016 and the few burnt areas present on the 1 January 2016 image have been delimitated on that image with a specific threshold for NDWI and NDVI values. \[NDWI=\frac{NIR-SWIR}{NIR+SWIR} \tag{1}\]  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{2}\]  
Figure 2: The progression of burnt areas (appear in red-purple color on the images) in the Marantaceae forests in the north of the Republic of Congo imaged by the Sentinel 2 Multi Spectral Instrument. Sentinel 1  
With a temporal resolution of 10 days, S2 images should allow a precise mapping of the timing and extent of fire event. However, the Congo Basin region has extensive cloud cover at various times of the year [25] limiting the use of optical sensors. Observations from S1 can complement S2 optical acquisitions due to the high sensitivity to structural properties of vegetation and day and night observing capabilities of the radar sensor. S1 is a two satellite constellation with prime objectives of Land and Ocean monitoring. The satellites carry a C-SAR sensor, which offers medium and high resolution imaging in all weather conditions [47]. The C-band SAR instrument supports operation in single polarization (HH or VV) and dual polarization (HH + HV or VV + VH). The first satellite, Sentinel-1A, currently has a revisit time of once every \(\sim\)15 days over Congo. After its sister Sentinel-1B (launched in April 2016) comes into service, coverage will be further improved, allowing for more frequent updates. Severe fires remove vegetation constituents such as trunks and branches which results in changes in the SAR intensity signal. Multi-temporal SAR images have been often used for burned area detection by computing the difference in backscatter before and after fire events [19; 21; 48; 49; 50; 51; 52]. [PERSON] [53] developed a method to detect fire affected areas using SAR series in C band (such as S1) with the co-polarized VV channel. Similarly, [PERSON] et al., [54] used cross-polarized (i.e., VH) C-band to assess woody structure and changes in deciduous savannas. The basic assumption of this study is that most of the C-band radar backscatter of intact forests comes from surface, volume and multiple volume-surface scattering. After a fire, the surface scattering becomes the major contributor to the return signal [55; 56]. At this stage, the difference between radar backscatter before and after the fire can be used to delineate the fire-affected area from its surroundings. However, changes in the ground moisture can also contribute to the SAR backscatter signal [57]. Hence limitations in the use of SAR are the soil moisture changes and the fact that the vegetation structure must be severely modified in order for a fire to be detected. The S1 images available in this study were acquired with the Interferometric Wide swath (IW) acquisition mode at 25 m spatial resolution. In total, 24 images are available from December 2015 to April 2016. The methodology employed to delineate the fire-affected area is carried out in the following steps and summarized in Figure 3: (1) map Gamma filter correction; (2) temporal aggregation of S1 imagery; (3) calculation of backscatter differences between consecutive periods; and (4) threshold application and consistency test. All SAR images are first corrected using Map Gamma filter technique described in [PERSON] et al., [58] to reduce speckle. Map Gamma is an adaptive filters that reduces the variance (i.e., speckle) within the image while preserving both radiometric and spatial resolution. The filter is based on the assumption that the (unspeckled) intensity of the scene is gamma distributed. The filter minimizes the loss of texture information and it is thus suitable for a wide range of gamma distributed scenes, such as forest and agriculture areas.