## 3 Results
### Statistical Analysis
WSV showed higher biomass production compared to previous classes, of 39.84 gC m\({}^{-2}\), but absorbed carbon every year (CO\({}_{2}\)Flux \(<\) 0), as well as EBF; however, this latter class was that which obtained the highest carbon absorption by GPP and CO\({}_{2}\)Flux, combined with the highest amount of Rainfall compared to the other classes (Figures 4a-c and S3). In the Kruskal-Wallis and Dunn tests for comparison between years in the BLA, only the Rainfall variable showed a significant difference (\(X^{2}\) = 28.61, \(p\)\(<\) 0.05). The Dunn test showed that this difference was between 2013 and 2015 and between 2015 and 2018, which had values of 2092, 1689 and 2026 mm, respectively. The Rainfall values decrease for the different classes following this order: EBF, WB, WSV, UBL, PW, GL, SV, CL, MF and DBF, with maximums of 2792 (2013), 2453 (2013), 2182 (2011), 2120 (2018), 2098 (2011), 2091 (2009), 1983 (2013), 2074 (2018), 2016 (2013) and 1941 (2013), and lows of 2163 (2015), 1895 (2015), 1706 (2015), 1605 (2015), 1702 (2010), 1533 (201 WB class. The DBF and MF classes had minimums in 2013 and maximums in 2012 and 2010, respectively (Figures 4e and S3). The years 2014 and 2018/19 showed the highest GPP averages of 24.26, 26.24 and 26.03 gC m\({}^{-2}\), respectively, and the lowest CO\({}_{2}\)Flux. In addition, the years with the lowest Rainfall values, such as 2010 and 2015/16, demonstrated the dependence of these indices on climatic factors, with lower GPP averages, of 22.76 (2015) and 19.24 (2016) gC m\({}^{-2}\), and higher CO\({}_{2}\)Flux of 2.80 (2010) \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) (Figure 4d-f and Figure S3). The correlation between the CO\({}_{2}\)Flux and GPP variables was strong and negative, at \(-0.75\), which also means that the increase in one influences the decrease in the other, and was statistically significant for the CL and PW classes, at \(-0.65\) and \(-0.69\), respectively. Low correlation between the variables CO\({}_{2}\)Flux and GPP with Rainfall was observed and this was due to the non-linearity between them, seen the scatterplot at the lower left part of the graph (Figure 5). In the correlation between CO\({}_{2}\)Flux and Rainfall, the DBF and WSV classes had a strong negative correlation of \(-0.82\) and \(-0.66\), which demonstrates that the increase in Rainfall values causes a decrease in CO\({}_{2}\)Flux values. Only EBF and UBL showed a positive, though weak, correlation, which showed that the increase in Rainfall values can increase de CO\({}_{2}\)Flux values. In the correlation GPP with Rainfall variables, only EBF presented a negative correlation, also weak; a strong and positive correlation was observed for the GL class of 0.66 (Figure 5). Figure 5: Scatterplot of the distribution and the correlations between the GPP, CO\({}_{2}\)Flux and Rainfall variables in different types of land use and land cover. Values shown in gray indicate the overall correlation, while colored values indicate the correlation between land use and land cover. *, ** and ***: significant at 5, 1 and 0.1% probability by \(t\)-test, respectively. Land use and land cover (LULC); Croplands (CL); Deciduous Broadleaf Forests (DBF); Evergreen Broadleaf Forests (EBF); Grasslands (GL); Mixed Forests (MF); Permanent Wetlands (PW); Savannas (SV); Urban and Built-up Lands (UBL); Water Bodies (WB) and Wood Savannas (WSV). The first two principal components (Dim1 and Dim2), which analyzed the cluster in the classes and variables, accounted for 58.4% and 35.4% of the total variance between data. Greater affinity was observed for GPP with EBF and with WSB and was more distant for WB and UBL. The CO\({}_{2}\)Flux variable was more associated with the WB class, also being close to the UBL due to the higher values for this variable. The other classes with the presence of vegetation ranged between negative and positive or were close to zero, which may be related to the poor ability of the CO\({}_{2}\)Flux index to characterize these classes.