# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### Spatial Analysis  
Remote sensing imagery provides a useful way to capture not only the magnitudes of different biophysical variables but also their distribution throughout a given region. In this study, the spatial distribution of ET and GPP is crucial because they can provide important insights, for example, on how water is being used to produce biomass, or how much carbon is absorbed considering different hydrological conditions in a specific year. This can bring benefits for future studies in the Caatinga region in order to establish priority areas for conservation and also agriculture purposes in the surrounding areas. The spatial distribution of annual ET from the MOD16A2 and SEBAL products for 2013 in Paraiba is shown in Figure 4. The annual ET ranged from 30 to 1200 mm\(\cdot\)year\({}^{-1}\), with higher values on the coast and western portion of the state. Comparing the two models (MOD16A2 (a) and SEBAL (b)) with the observed data, it is possible to observe an overestimation of the ET derived from SEBAL in the areas that presented values greater than 500 mm\(\cdot\)year\({}^{-1}\). In addition to the annual overestimation, ET SEBAL also presented an overestimation in the dry period, mainly in the DC area (Figure 2). In addition, the authors of [45] showed an overestimation of ET SEBAL (geoSEBAL) in the woodland savanna in the dry period when compared to the observed data. This comparison suggests that the SEBAL model may not accurately represent the actual ET levels in Caatinga regions. The overestimation of ET in SEBAL could be associated with various factors, including limitations in input data quality, incorrect model parameters, and/or limitations in the algorithm used. It is important to continue monitoring and evaluating ET models to ensure they accurately represent the water cycling processes in Caatinga ecosystems and can inform sustainable resource management practices. The annual cumulative maps of the GPP in 2013 in Paraiba are shown in Figure 5. The values vary between 0 and 3000 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\). The highest GPP values were observed in the eastern part of the map (Figure 5a), where the Atlantic Forest and the transition to the Caatinga are located. The central part of the map, where the Borborema mesoregion is located, showed low GPP values for the two models, MOD17A2H (a) and Modeled (b). In comparison with the two maps, Figure 5b (modeled) overestimated the GPP values when these values were above 1500 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) in the MOD17A2H model (a) and underestimated the values in areas where the GPP was low (<1000 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)). Figure 3: Monthly temporal distribution of GPP in the areas of DC and SC in 2013. There is a greater absorption of CO\({}_{2}\) in the eastern portion of the Agreste, with higher GPP values than in the western portion (where the Caatinga vegetation predominates), as was the case for the ET. This can be explained by the fact that the eastern portion is closer to the Zona da Mata, with a more humid climate than the western portion. At Zona da Mata, there is a predominance of Atlantic Forests. The eastern part of the Agreste showed higher GPP values on all days analyzed, showing the relationship of GPP with soil moisture content. Thus, rainfall within this region seems to be a limiting factor for the absorption of CO\({}_{2}\) by the Caatinga, since the dry months have the lowest rates. This factor can be explained by the phenological dynamics of the Caatinga, which quickly responds to greater water availability [13]. According to [10], in dry periods, or even in the rainy season of dry years, there is a decrease in the absorption of CO\({}_{2}\) by this biome. Figure 6 shows the annual spatial statistical analysis (SD, maximum-max, and median) of the ET derived from MOD16A2 for Paraiba in 2013. Figure 6a shows which areas had lower ET rates (Figure 4), which were those that did not suffer major variations throughout the year.