## 3 Results
### Correlations Between In Situ SM, GPP, and Satellite Information  
Figure 7 shows scatter plots between daily SM and spectral indices from the AHI for ASM. The general pattern is similar for both indices, showing a stronger correlation with SM in the deepest considered horizons. Here, the quadratic adjustment is mostly explained by the temporal dynamic of the analyzed variables rather than a causative relationship. That is, SM reaches maximum values quickly in these sandy soils due to low water retention capacity. Those maximums correspond to medium-high values of the indices during the greening stage. Later, maximum values of the indices (a consequence of high ET and antecedent SM) correspond to the decreasing curve of SM (left part of the quadratic regression; see also Figure 3). SSM has a less clear pattern, given the more pronounced fluctuations in that variable (mainly due to strong soil-atmosphere connection and low water retention in the surface horizon) and the vegetation response, mainly to significant wet events. Similar results were observed in the Ti Tree station for both indices (Figure A3), indicating no correlation with surface and 10 cm depth SM and quadratic adjustments for SM at 60 and 100 cm depths (\(p<0.005\)). Figure 6: Detailed temporal series of MODIS LST, daily in situ LST and SM in ASM during the 5 analyzed seasons: (**a**) 2016–2017 (extremely wet), (**b**) 2017–2018 (normal), (**c**) 2018–2019 (moderately dry), (**d**) 2019–2020 (normal), and (**e**) 2020–2021 (extremely wet). Regarding thermal data, in situ LST and SM were considered to evaluate the thermal response of vegetation to soil water content during wet rainfall pulses. The correlation was  
Figure 7: Relationship between daily in situ SM at different depths, LSWI (**left**) and EVI (**right**) from AHI in ASM (\(n=356\)). tested for the same day and with LST averaged up to 4 days after SM data. Data for rainy dates were removed to avoid noise because of rainfall. Figure 8 shows the scatter plots for ASM with the highest coefficient of determination for that temporal window (\(p<0.005\)). The correlation over time suggests the temporal influence of profile soil moisture. A less clear pattern is observed at a 100 cm depth because SM at that depth would have an effect on vegetation mostly during specific periods when the wetting point reaches that depth and/or the hardpan favors soil saturation. Similar results were obtained in the Ti Tree station (Figure A4), showing an asymptotic line for high SM with low correlation at 60 cm and 100 cm depths (\(p<0.005\)). These lower correlations can be explained by the less intense increase in SM in deep horizons in the Ti Tree station, with maximum values at 60 and 100 cm depths around 0.16 m\({}^{3}\)/m\({}^{3}\) during the analyzed seasons. Contrary to what was observed in ASM, in Ti Tree the absence of the hardpan did not favor the accumulation and permanence of high values of SM (see also Figure 3). Figure 9 shows the scatter plots between Terra and Aqua LST and daily in situ SM in ASM; rainy days were removed from the analysis. The adjustments and correlations (\(p<0.005\)) are consistent with field results (see Figure 8), showing that remotely sensed LST has the potential to reflect the thermal effect of SM on vegetation water conditions during wet pulses. A similar pattern is observed in the Ti Tree station, with a cooling effect of high SM being more evident in the shallow horizons (Figure A5). Given the high ecohydrological sensitivity of these drylands [5; 6], a higher correlation might be expected; however, in wet seasons, solar radiation can also be important for vegetation dynamics and atmospheric water demand [57]. Figure 8: Relationship between average in situ LST and SM at different depths (the best correlation up to 4 days is included) in ASM (\(n=475\)). Although correlation at 100 cm depth is included, most of the time LST fluctuates according to shallower SM.