# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### ET Validation  
As a result, Table 2 shows the ET estimated according to Equation (12), here called ET SEBAL, and the ET according to the MOD16A2 product was underestimated in relation to the observed data (EC technique), except for ET SEBAL in DC, which overestimated the observed values and MOD16A2 in the dry period, as can be seen in Figure 2. The absolute differences, however, were greater for ET SEBAL (Table 2). This result may be due to an apparent underestimation in the estimated H values (not shown), which indicates that the rain occurred four days before the image was acquired for almost all the dates studied. Figure 2 shows ET SEBAL and ET MOD16A2 in relation to field measurements. Except for the ET SEBAL estimated in the DC area, the seasonality of ET is well-pronounced, especially for the rainy season (March to July). Emphasizing the ET MOD16A2, we observed values closer to the measured ET than the ETSEBAL. Although the MODIS data underestimated the observed data, these estimates were able to follow the seasonality of the study areas, with higher precision for the DC area. The low performance of the MODIS estimates in the SC area is related to the fact that the region has large portions of exposed soil, which makes the performance of the MODIS LAI algorithm difficult. This algorithm presents higher precision on dense vegetation and in areas with less soil influence, thus interfering with the MOD16A2 estimates. As described in [25], the amplitude of hot or cold pixels in an image, which is very subtle over drylands in dry conditions, is known to affect SEBAL. The hottest and coldest pixels are given the maximum and minimum sensible heat fluxes, respectively. Due to the inability to distinguish between the region's land cover, especially during the dry season, and the fact that bare soil and senescent vegetation have similar LAI values, the 500 m spatial resolution of MODIS images also makes it difficult to distinguish between hot and cold pixels. Regarding the evidence of seasonality, the authors of [25] found a good result for applying the SEBAL algorithm. However, that did not occur for the validation using field measurements. In that study, the estimate using ET MOD16A2 showed good performance in obtaining the spatial and temporal variability of water exchange in the region, which is in agreement with the results obtained here. The ET MOD16A2 also showed a better relationship with the EVI (Figure 2), indicating the differences between the two study areas. The relationship of ET with the increase in the vegetation index (EVI) is also pointed out by [7] in a study conducted in the Ziya-Daqing basin, China, using a model for estimating ET (the VIP model, Vegetation Interface Processes). They used the ET of the MODIS product (MOD16A2) compared to the ET measured by EC for different types of ground cover. Several studies have pointed for an increase in the vegetation index in semiarid regions causes an increase in ET [7; 25; 60]. The DC presented higher EVI values (reaching 0.6) than the SC (a minimum of 0.11), although both are dependent on the rainfall regime, which is more intense during the rainy season (Figure 2). This result corroborates a study carried out [61] in dense and open Caatinga areas, where they found values around 0.9 for dense Caatinga in the rainy season. In contrast, the values for open Caatinga were lower than 0.3 in the dry period.