###### Abstract
Optical data of different sensors and spatial resolutions have been used such as MODIS, Landsat, airborne laser scanning, Sentinel-2, and IkonosWorldView-2 data [29; 30; 31; 32; 33]. A recent study investigated how increasing spatial resolutions from Landsat, Sentinel-2, and WorldView-2 data affected plot-to-landscape estimates of windthrow tree mortality [33]. This study showed that while uncertainties decreased systematically with increasing spatial resolution, the use of Landsat-8 data at a similar scale of the plot data (10 \(\times\) 25 m) used to calibrate models was the most adequate for the wind damage estimates. To the best of our knowledge, synthetic-aperture radar (SAR) data were not used to study blowdowns in the Amazon. An early study has compared the capabilities of optical and SAR data for blowdown detection, although not in the Amazon region [34]. SAR offers data that are less affected by weather effects, but the study showed that it was not suitable for blowdown detection, especially due to the effects of layover and shadow effects in mountainous areas, and its accuracy is influenced by incidence angle and polarization. The most traditional approach for blowdown damage estimates relies on the use of Landsat-derived net change of non-photosynthetic vegetation (\(\Delta\)NPV) combined with an aboveground biomass distribution map collected in the field [26]. For instance, one study using this method showed that in 2005, over the Manaus region in the Amazon, disturbed forest patches resulting from a single squall line (an aligned cluster of convective storm cells) produced a mortality of 0.3-0.5 million trees. This was equivalent to 30% of the observed annual deforestation reported in 2005 or 23% of the mean annual biomass or carbon accumulation estimated for Amazon Forests [27]. Using the same approach, another study found that tree mortality was highly non-random within the blowdown event boundary, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation [28]. These authors suggested that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations but also to the biological differences between individuals that promote differential levels of mortality. Despite these advances in the use of remote sensing, field surveys, and modeling to investigate tree mortality and carbon loss, there are still issues to be resolved. Firstly, there is a lack of sufficient high-spatial-resolution data to identify small blowdowns. [PERSON] et al. [5] and [PERSON] et al. [6] showed that the majority of natural forest biomass loss from catastrophic winds is caused by small blowdowns (\(\leq\)0.1 ha). Although the commonly used Landsat multispectral data can monitor the extent of large blowdowns, its 30 m spatial resolution precludes the identification of small-size blowdowns. More critically, with a Landsat-8 revisit period of 16 days, it is unlikely that a sufficient number of cloud-free images of blowdown events will be available to detect damage and post-disturbance vegetation recovery in the tropical forest environment. The aforementioned previous studies have focused their analyses on pairs of images. Little is therefore known about the effect of timing of blowdown event occurrence on the spectral mixture signal considering the post-blowdown recovery. Until now, knowledge of the differences between Landsat-8 and other high-spatial-resolution orbital instruments for the potential mapping of blowdowns and vegetation recovery following disturbance is still scarce [12; 35]. Given the fast rates of vegetation recovery in the tropics, we hypothesize that determining the date of blowdown occurrence is a significant factor for the accurate estimation of damage based on spectral mixture fraction changes.