## 3 Results  
This section may be divided by subheadings and should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn. The biome-wide SPI time series shows that the years experiencing the most prolonged droughts were 2002, 2010, and 2019. SPI was highly variable among areas affected by drought and the years of occurrence, with the highest values being found for the southern, northeastern, and central regions of the Pantanal in 2002, 2010, and 2019, respectively (Figure 3). Consequently, the years 2014 and 2018 showed the lowest SPI values. When SPI values were highly positive, there was a lower incidence of fires. This is consistent with the lowest numbers of fire foci being found for particularly wet years (2014 and 2018; Figure 4). Figure 3: _Cont._  
Figure 4: Relationship between Standardized Precipitation Index (SPI) and counts of fire foci from 2001 to 2019 across the Brazilian Pantanal. Fire foci and SPI are indicated by the red and grey lines, respectively. Figure 3: Standardized Precipitation Index (SPI) across the Brazilian Pantanal wetlands from 2001 to 2019 based on CHIRPS data. 98% of the variation in aboveground live woody biomass (Figure 6). This relationship can provide an accurate biomass estimate with GPP data over a long time series, made necessary by the absence of LiDAR biomass data [35]. The GPP longitudinal data showed increasing monotonic behaviour, the highest value being attained in 2019 (Figure 7). However, there was only a weak correlation between GPP and fire incidence (r = -0.24) over the time series examined here (Figure 5). Figure 5: Heatmap of Pearson correlation values between fire foci, number of cattle heads, Gross Primary Productivity (GPP), annual precipitation, and SPI from 2001 to 2019 across the Brazilian Pantanal. Figure 6: Linear regression between gross primary productivity and aboveground live woody biomass for the Brazilian Pantanal. Figure 7: Gross Primary Productivity (GPP) from 2001 to 2019 across the Brazilian Pantanal. From 2002 onwards, cattle numbers in the Pantanal began to grow until 2005. The overall size of cattle herds increased by 33% over the time series, presenting a positive trend in relation to GPP, which also increased by \(\sim\)260% from 2001 to 2019 (Figure 8). A correlation heatmap was generated to demonstrate the dispersion and the relationship between the variables assessed over the time series (Figure 5). Fire incidence was, again, negatively correlated with rainfall and SPI (r = 0.93), whereas GPP was positively correlated with cattle density (r = 0.64). The results of the trend tests are shown in Table 1. Fire foci, rainfall, and SPI showed neither a positive nor a negative significant trend (\(p\)-value > 0.05) over the entire time series. However, the number of head of beef cattle and GPP were positively correlated and showed a significant upward trend (\(p\)-value < 0.05), with likely inflection points along the time series in 2012 and 2009, respectively (Figures 5 and 8). Figure 9 contains the PCA dimensions for the variables assessed over the time series. In agreement with the Pearson correlations, it can be seen that the cattle and GPP vectors converge onto the same quadrants; this is also the case for the rainfall and SPI vectors. Our measure of fire incidence is, however, located in a different quadrant due to its low and/or negative correlation with the other variables. In this biplot, we highlight the proximity between fire foci and particularly dry years, such as 2002, 2010, and 2019, in which the largest burnt areas were recorded. Accordingly, this is consistent with the spatial distribution of fires across the Brazilian Pantanal in 2010 and 2019 (Figure 10). In both of these years, the highest number of fire foci was detected in the southern region of the Pantanal, located in Mato Grosso do Su