# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 3 Results  
Figure 3 shows the total number of valid observations (pixel without clouds) by trimester for the period 2014-2019 (Landsat 7 and 8) over the Brazilian territory, that is, how many times each pixel was presented in an image without cloud interference. It can be observed that data availability varies significantly in space and time. In the Amazon, it is worth noting the lack of valid observations during a large part of the year (November to May). To a lesser extent, Caatinga and Mata Altantica biomes also present a similar pattern (Figure 4).  
Figure 2: Brazilian biomes.  
Another interesting aspect observed in Figure 3 is the presence of north-south oriented strips in which there are approximately twice as many valid observations. These areas correspond to the overlap between two satellite paths and are narrower at the equator (approximately 20 km wide) and wider at high latitudes (60 km in the south of Brazil).  
Figure 4: Average number of valid monthly observations by Brazilian biome (2014–2019).  
Figure 3: Number of valid observations by trimester during the period 2014–2019.  
Figure 5 shows the image classified using the number of valid monthly observations. The clusters 3, 4, 8 and 10 correspond to overlapping areas between Landsat scenes and have a larger number of data during the period 2014-2019.  
In order to employ a realistic phenological curve to model the effect of the lack of valid information on metrics, we constructed a reference curve from a dry tropical forest plot located on the overlapping of two Sentinel paths. The reference curve was obtained fitting the Sentinel derived NDVI values using the generalized sigmoid formula proposed by [PERSON] et al. [34], and equation parameters were determined (Figure 6). The variability of Landsat NDVI values around the reference curve enables us to evaluate the noise related to data acquisition (standard deviation of the residuals). Then acquisition dates were modelled for each pattern of image availability (cluster), and time series of NDVI values were generated using the equation of the reference curve and adding the random noise.  
Phenological metrics, along with their uncertainty, were computed from the NDVIs. Table 1 shows the confidence interval of metrics for each pattern of image availability. It can be observed that the unique pattern of observation availability which enables the method to estimate the four metrics with confidence is pattern 3 which corresponds to the \"exceptional\" case of overlapping between two satellite paths with frequent observations. The dates of senescence and dormancy, which correspond to the dry season and a larger number of observations, are determined without uncertainty. At the opposite, greenup and maturity dates based on a lower number of observations (rainy season) and the steeper part of the curve present a considerable uncertainty.  
Figure 5: Map of clusters based on monthly availability of valid observations (**left**), heatmap showing the average availability pattern for each cluster (**right**). Each cluster represents a different pattern of valid observations availability over the year.  
Figure 6: NDVI curve fitted from Sentinel observations (black circles) and four phenological metrics used in this study (1 November 2018–30 October 2019).