## 4 Results and Discussion
### SM and LST Time-Series: Coupling/Decoupling and Critical SM Estimation  
Figure 11 (top) shows time-series of in situ SM at the SMOS morning passes and \(T_{max}\) (blue and red solid lines, respectively), and SMOS SM at the morning passes and MODIS LST Aqua day (cyan and magenta dashed lines, respectively). Daily mean precipitation from the REMEDHUS network is also shown (bottom). Winter seasons defined as December, January and February (DFJ) are shaded in light green color. Although there is a different temporal resolution for in situ and satellite data (daily versus 3-day averages, respectively), both SM estimations are consistent with precipitation and capture wetting and drying events. The SMOS SM has a dry bias with respect to in situ SM. This dry bias has been previously reported in studies [(40; 48; 61)] and remains almost constant along time. However, the MODIS LST Aqua day is very similar to \(T_{max}\) in winter and is higher in other seasons, in agreement with results shown in Figures 7 and 10. Note that the SM-LST relationship presents two different behaviors in both datasets. On one hand, there is SM-LST coupling (when SM decreases LST increases and _vice versa_), approximately corresponding to the spring, summer and autumn seasons, in which a strong anticorrelation is obtained (R \(\approx-0.5\) to \(-0.7\)/R \(\approx-0.4\) to \(-0.7\) from in situ/satellite data in Figures 8 and 9, respectively). This is related to a water-limited ET regime. Figure 10: Normalized occurrence frequency density diagrams of in situ SM at the SMOS morning passes and \(T_{max}\) (**top row**), and for SMOS SM at the morning passes and MODIS LST Aqua day (**bottom row**) for each season (by columns): winter (December–January–February, DJF), spring (March–April–May, MAM), summer (June–July–August, JJA) and autumn (September–October–November, SON), respectively. Similar behavior is obtained at the afternoon passes and/or using Terra day (not shown). On the other hand, there is SM-LST decoupling (when SM and LST do not show a clear relation between them) approximately corresponding to the winter seasons, in which a weak correlation is obtained (R \(\approx\) +0.30/R \(\approx\) \(-\)0.3). This is related to an energy-limited ET regime. The discrepancy in the sign of the correlation during winter could be due to the different spatial scales represented by the remotely sensed data and ground-based data. In addition, SM-LST decoupling periods vary from one year to another and include, in most cases, around one month after and before the defined winter. Similar behavior is obtained at the afternoon passes and/or using Terra day (not shown). Minimum, maximum, mean and standard deviation (std) of SM and LST values obtained at the SM-LST coupling/decoupling transitions are summarized in Table 1. Note that the same mean SM is estimated in both datasets (\(\sim\)0.12 m\({}^{3}\)/m\({}^{3}\)), which corresponds to the critical SM over the study area. Taking into account its variability (in situ std SM\(\sim\)0.06 m\({}^{3}\)/m\({}^{3}\); satellite std SM\(\sim\)0.05 m\({}^{3}\)/m\({}^{3}\)), the estimated critical SM remains within reasonable boundaries (0.10-0.16 m\({}^{3}\)/m\({}^{3}\) vs. 0.08-0.22 m\({}^{3}\)/m\({}^{3}\)). In addition, the estimated critical SM lies between WP and FC of the REMEDHUS network (WP = 0.08 m\({}^{3}\)/m\({}^{3}\); FC = 0.16 m\({}^{3}\)/m\({}^{3}\)) and is between the 50% and the 80% of FC (\(\sim\)75%), in agreement with [22]. The proposed methodology could be an interesting technique to measure critical SM using remote sensing. In the SM-LST transitions, the mean LST from in situ (\(\sim\)16.4 \({}^{\circ}\)C) has a good agreement with the mean LST from satellite data (\(\sim\)15.2 \({}^{\circ}\)C). It is observed that spaceborne LST measurements have a higher upper limit (\(\sim\)32.7 \({}^{\circ}\)C) than ground-based measurements (\(\sim\)22.6 \({}^{\circ}\)C).