## 4 Discussion
The authors suggest that the failure of the MODIS algorithm to represent the monthly and annual variability of GPP may be associated with the inadequacy of light use efficiency (LUE) models in capturing changes in canopy physiology over time. The tendencies in the MODIS GPP in relation to the observed data stem from a lack of sensitivity to canopy density, which is spatially and temporally variable. Other studies [79] evaluated the performance of GPP-MODIS in areas of the Brazilian Amazon, and they found that the MOD17 GPP product presented limitations in estimating the seasonality, magnitude and spatial variations of GPP in the tropical sites studied. When comparing MODIS GPP products with tower data in the Amazon, [PERSON] et al. [80] argue that the relationship between tower and image data is influenced by the satellite observation conditions and differences in scale, and despite this, it is possible to observe indices such as NDVI and EVI following the pattern of GPP increases from the beginning to the end of the dry season, suggesting changes in the structure and amount of water in the canopy seen by the sensor during the period of water stress and greater availability of radiation. MOD17 overestimated GPP in low-productivity sites and underestimated it in high-productivity sites. Such results were also obtained in the literature [81; 82]. The average annual carbon balance, \(-569.7\pm 444.9\) g C m\({}^{-2}\) year\({}^{-1}\), is similar in magnitude to other tropical forest sites with high annual assimilation, as pointed out for the same site [17] in this study in a different period (\(-560\) g C m\({}^{-2}\) year\({}^{-1}\)), sites like the Cuieiras [34] reserve (\(-590\) g C m\({}^{-2}\) year\({}^{-1}\)) and a site [83] in Rondonia (\(-400\) g C m\({}^{-2}\) year\({}^{-1}\)). There are studies that show strong correlations between MODIS data and flux tower data (R\({}^{2}\) = 0.70 in Temperate grassland, [PERSON] et al. [84]), while there are also sites where there is no or very weak correlation (R\({}^{2}\) = 0.01 in Floodplain forest, [PERSON] et al. [7]; R\({}^{2}\) = 0.16 in tropical peatland, [PERSON] et al., [85]; R\({}^{2}\) = 0.17 in Alpine Grassland, [PERSON] et al. [84]). These differences between satellite and tower data correlation need to be further studied for each type of environment, then it will be possible to identify places where we have poor estimates so the scientific community can propose other tools and methods to improve these estimates to compensate for the lack of information for remote areas with low densities of measured data, such as the site under study. Viable options for solving this problem today range from changing the parameters used to calculate satellite GPP, which is often stationary throughout the year, to applying machine learning techniques to improve estimates. Although other sites in Amazonia have been shown to be moderate sources of CO\({}_{2}\) to the atmosphere [36; 57], the Caixuana site has been shown to be a considerable sink, with circulation patterns consistent with the literature [86], although it appears that precipitation can bring it to a state of neutrality in drier years. It is important to emphasize that there are many uncertainties associated with nocturnal respiration with gap filling by u\({}^{\star}\) at times of weak flows, which is true for practically all studies using the eddy covariance technique. It is well known that the choice of u\({}^{\star}\) threshold for gap-filling corrections can lead to indirect measurements due to data substitution during periods of weak turbulence, and they can even change the final results of a site from source to sink or sink to source [87]. However, gap-filling is accepted by the scientific community as necessary, and the automation of these gaps with valid data under similar environmental conditions as the filled data [88] provide confidence that the uncertainty of the data does not exceed acceptable percentage limits for a gap-fill, hence its widespread use by the scientific community [89].