# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 4 Discussion
Similarly, BL shows significant variations when vegetation is particularly sensitive to environmental changes [44; 53; 54]. In agriculturalmanagement, SOS, LOS, and EOS are essential for planning planting and harvest periods. Vegetation with higher LSI tends to exhibit greater productivity. When combined with other data, these LSP metrics are also effective for monitoring water or thermal stress in vegetation [29; 45; 55]. In our study, some MODIS LSP metrics in woodland areas exhibited higher positive correlations with precipitation than those observed in grassland areas. In addition to the difficulties of mapping savanna vegetation physiognomies and the differences in soil composition/texture at the SNP that influence soil water infiltration and water availability to plants, grassland areas have historically been more affected by fire disturbance than woodland areas. Compared to woodland areas, this greater fire disturbance in grassland areas likely contributed to the variability observed in the correlation results presented in Figure 7. For example, a report by [PERSON] et al. [35] on fire occurrence between 1973 and 2003 at the ENP indicated that the number of repeated fire events in grassland areas ranged from 10 to 18 times, having a lower observed frequency in woodland areas. Our analysis, based on three major fire events (2005, 2010, and 2021) and utilizing both the MODIS MOD13Q1 and HLS products, highlighted the sensitivity of LSP metrics to fire activity and subsequent vegetation recovery in burned areas. This type of disturbance explains also part of the significant color variability in grasslands observed in the false color composites of Figure 10 for post-fire image acquisition dates. Coupled trend effects of precipitation and fire were observed for metrics such as AMP, EOS, and SSI, which increased with both rainfall and the occurrence of fire. The effects of fire on these metrics were apparently more pronounced than those associated with precipitation, as indicated by a comparative analysis of effect sizes in savanna grasslands (Table A1 in Appendix A). Hedge's G values were higher for fire than for precipitation. Caution is warranted in this analysis due to the use of different remote-sensing products with distinct spatial resolutions and sample locations. In Table A1, Hedge's G values for precipitation were derived from the MOD13Q1 product (\(n\) = 500 pixels), based on a comparison between two contrasting seasonal cycles--dry (2006/2007) and wet (2019/2020)--both characterized by minimal fire disturbance. The difference in accumulated rainfall during the rainy season between these two cycles was approximately 550 mm. In contrast, fire-related Hedge's G values were calculated using the HLS product (\(n\) = 500 pixels), comparing a burned grassland site with a nearby unburned control site. By decoupling the effects of fire from rainfall influence using a control site near a burned grassland area, we identified the most sensitive LSP metrics to burning events among the 13 TIMESAT metrics: BL, VES, SSI, VSS, EOS, and AMP. For instance, in the effect's size analysis of the 10 July 2021 fire event (2020/2021 growing cycle) using Hedges' G metric, most of these TIMESAT metrics derived from the HLS product showed high sensitivity to fire events during the cycle in which the disturbance occurred and in the subsequent cycle following disturbance (residual effects due to vegetation recovery in burned areas). The exception was VSS, which marked the beginning of the next growing season. For the 2021 fire event, VSS was primarily influenced by fire effects in the subsequent 2021/2022 cycle, as expected. Among these metrics, AMP, EOS, and SSI increased during the fire-impacted seasonal cycle, while BL, VES, and VSS decreased. The use of the most sensitive LSP metrics to precipitation and fire detected in our analysis can support also ecosystem management and conservation strategies. For instance, changes in EOS and BL reflect vegetation resilience following disturbances like fires or droughts [53; 54].