# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 2 Methodology
### Data Analysis  
The data analysis was divided into three linked steps: (i) identifying the driest and wettest growing season cycles at the ENP during the period of analysis (2002-2023); (ii) investigating the correlations between accumulated precipitation during each of the 21 growing season cycles and the corresponding MODIS LSP metrics; and (iii) exploring the impact of fire and vegetation recovery in burned areas on the determination of both the MODIS and HLS LSP metrics. The first part of the analysis, which supported the subsequent examination of the relationships between rainfall and LSP metrics, used rainfall data from the meteorological station (1983-2023) near the ENP. To identify dry and wet growing season cycles, the boxplot approach was adopted. Following the procedures outlined by [PERSON] and [PERSON] [48], we used a box plot to categorize the park's rainfall regime into five distinct categories: severely dry, dry, regular, wet, and severely wet. In interpreting the boxplot, each quartile represents 25% of the data in the time series and corresponds to a specific category. Values that exceed the thresholds (upper and lower fences) indicate cycles classified as severely wet or severely dry, respectively. The hydrological cycle was defined as ranging from October of a given year to September of the following year, thus covering a full seasonal cycle. Consequently, the total rainfall for each seasonal cycle from 1983 to 2023 was used to generate the boxplot and to define the dry and wet cycles for the ENP during the MODIS data analysis from  
Figure 3: Example of the seasonal NDVI variations in woodland savanna from the Emas National Park (ENP) with a graphical representation of the TIMESAT metrics used in the data analysis. The abbreviations are: SOS (Start of Season), EOS (End of Season), TMS (Time for the Mid of the Season), AMP (Amplitude), BL (Base Level), LDV (Largest Data Value for the fitted function during the season), VES (Value for the End of the season), VSS (Value for the Start of the season), LOS (Length of Season), LSI (Large Seasonal Integral), and SSI (Small Seasonal Integral). The Rate of Increase at the Beginning of the Season (RIBS) and the Rate of Decrease at the End of the Season (RDES) were omitted for better graphic representation. 2002/2003 to 2022/2023. This approach provided a comprehensive analysis of rainfall patterns across the seasonal cycles, accounting for the Climatological Normal. In the second step of the data analysis, we correlated the accumulated rainfall during the rainy season (October to May) across each of the 21 seasonal cycles (2002/2003 to 2022/2023) with the corresponding average values of the 13 MODIS TIMESAT metrics calculated for grassland and woodland areas (Table 1). To obtain the MODIS LSP means and calculate Pearson's correlation coefficients (r values), we applied a random computational sampling of 500 pixels per vegetation class (grassland and woodland). Our analysis focused specifically on the rainy season of each growing season cycle, which typically accounts for approximately 95% of the total precipitation from the cycle at the ENP. To complement the correlation analysis and minimize the impact of fire and burned areas on the results, the LSP metrics most strongly correlated with precipitation were selected and their relative frequency histograms were plotted for the driest and wettest cycles, excluding those affected by large fire events at the ENP. Then, we evaluated statistical differences between the two population means (driest and wettest) for each LSP metric at the 0.05 confidence level. In the final step of the data analysis, we investigated the effects of fire and resultant vegetation recovery in burned areas on phenological metrics derived from the MODIS MOD13Q1 and HLS products.