# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 4 Discussion  
Phenological data obtained from remote sensing present a large and growing resource for monitoring global change and vegetation interaction. However, in tropical regions, the presence of clouds often limits the availability of remotely sensed observations during a part of the year. This study assessed the effect of the lack of available observations taking into account different temporal pattern of images availability over a semi-arid region in northeast Brazil. Our preliminary results indicate that the lack of valid observations has an adverse impact on the phenological metrics' certainty. The availability of Landsat data (two satellites with a 16 days revisiting period), in the climatic conditions of the Caatinga biome limits the accurate estimate of these metrics strongly. It is worth noting that the uncertainty of a remote sensing product accumulates and propagates over and over through the processing flow and ultimately affects the final results' reliability [40; 41]. The Sentinel constellation, which has a better temporal resolution, can be useful in improving these estimates. Moreover, the combination of different sensors such as the Harmonized Landsat Sentinel-2 Surface Reflectance Product [42] will permit to overcome many of these limitations [43; 44]. Another option can be using MODIS data, which permits a high temporal resolution but at the expense of spatial resolution [11; 45; 46]. However, MODIS and Sentinel 2 provided data from 1999 and 2015, respectively, (2002 and 2017 as two satellites based constellations). Therefore, studies aiming at monitoring vegetation over more extended periods to reveal long term land surface dynamics at a relatively high spatial resolution should use Landsat data. A similar impact of observation scarcity will likely be observed on land change detection techniques based on time series of vegetation indices such as, for example, BFAST. This algorithm decomposes the time series into seasonal, trend and remainder components to detect abrupt changes [47]. In further researches, a similar modelling approach will be used to assess this impact. [PERSON] et al. [34] found that the choice of analysis method affects the error, the magnitude of bias, and in some cases, the direction of the bias of the estimate of the metrics. [PERSON] [35] realised growth curve fitting using different methods and found that double logistic method, as [PERSON]', is adapted to model the vegetation growing process with a single growth peak. The spline method presents a good performance for fitting growth curves of vegetation with multiple growth seasons. In this sense, in further researches, other fitting methods and metrics should be explored in the case of Caatinga vegetation. For instance, as it can be noted in Figure 6, that the fitted curve does not follow the rapid steep greenup at the beginning of the rainy season. This article assessed the impact of observations' scarcity on vegetation phenology metrics obtained using a NDVI Landsat time series and a specific fitting method. However, there are additional sources of errors. [PERSON] [9] discussed the principal limitations that could lead to the misinterpretation of the remotely sensed data. First, the different \begin{table}
\begin{tabular}{c c c c c} \hline
**Cluster** & **Greenup** & **Maturity** & **Senescence** & **Dormancy** \\ \hline
1 & 58.5 & 50.4 & 0 & 0 \\
2 & 84.9 & 54.8 & 0 & 0 \\
3 & 13.4 & 22.7 & 0.1 & 4.1 \\
4 & 20.4 & 34.6 & 0 & 0.1 \\
5 & 44.8 & 46.9 & 0 & 0 \\
6 & 28.1 & 55.2 & 0 & 0 \\
7 & 42.4 & 46.5 & 0 & 0 \\
8 & 47.8 & 55.5 & 0 & 0 \\
9 & 38.8 & 55.8 & 0 & 0 \\
10 & 32.2 & 55.8 & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Uncertainty of phenological metrics (width of the confidence intervals including 95% of the estimated values expressed as a number of days). vegetation indexes have distinct responses to greenness dynamics and changes in wetness conditions and surface brightness.