## 4 Discussion
### General Conceptual Model of LST, SM, and Vegetation Productivity
Although optical/thermal sensors can have low data coverage during rainfall events due to cloud cover, our study shows that they are related to volumetric soil water content in depths explored by roots, being associated with the ET process. In this sense, they can be a complement to other existing remote sensing methods for the early monitoring of the SM effect on vegetation conditions. To the best of our knowledge, the integration of LST and LSWI values to develop a conceptual model of their relationship with root zone soil moisture and subsequent GPP in Central Australia has not been carried out. The conceptual model proposed in Figure 12 is consistent with previous studies reporting the high dependence of vegetation on accessible water storage in the drylands of Australia (e.g., [12; 55; 70; 72]). It is useful for monitoring vegetation water status and productivity in the study area, given the high dependence of GPP on root zone soil moisture during rainfall wet pulses. This usefulness is especially important in cases of large areas with low coverage from field measurements such as Central Australia, requiring few input data. An important result is the lag in the response of the LSWI (lags between 12 and  
Figure 12: Conceptual model of satellite-derived LST from MODIS (**left**) and daily in situ GPP (**right**) as a function of daily SM for the Mulga woodland area. For the GPP plot, the maximum GPP values and average of the maximum values of SM in the soil profile for each analyzed pulse were considered. GPP versus maximum LSWI from the AHI is included. Note that on the left plot, SM values correspond to ASM data (a similar pattern was observed in Ti Tree). 40 days in ASM and 7-31 days in Ti Tree between peaks in SM and the maximum index; see Figures 4 and A1) and the early response of LST. This suggests a different potential in cases of use for predicting the impact of SM on vegetation growth and the monitoring of changes in carbon sink associated with the increase in evapotranspiration during significant wet rainfall events. Certain limitations of this study should be taken into account. The considered region has a slow slope, about 0.2%, and a deep groundwater level, but in areas where water uptake from groundwater is significant, the strong effect of rainfall wet pulses on vegetation can be partially masked. It should be highlighted that different vegetation responses to SM can be expected according to vegetation sensitivity and root system depth. Thus, correlation parameters can differ from these presented in the analysis, and uncertainties are probably larger for heterogeneous land cover types, taking into account the medium resolution of satellite missions considered in this analysis. It would be of interest to evaluate this distinct response on a regional scale. Although some studies such as [5] reported the differentiated sensitivity of grass and trees to rainfall along the North Australian Tropical Transect, Ref. [33] specifically studied the Mulga woodland around Alice Springs and reported that there are few differences in vegetation growth responses to rainfall between trees and understory grasses. They observed a temporal synchronization between tussock grass understory and Mulga foliage after rainfall pulses. Our study focused on Mulga woodland, the most representative vegetation cover type in the study area. It should be noted that in situ LST measurements covered a mix of different covers (tree, grass, and soil), given the observation angle and the sensor height. Moreover, in situ SM was not measured at different vegetation covers in both considered stations. Considering the spatial resolution of satellite data, it is difficult to differentiate the signal of different vegetation types. However, the relevance of each component can be considered through more detailed in situ studies on LST, soil heat flux, and SM in different vegetation types and in bare soil. Our study was mainly focused on the contribution of satellite data for the integrated monitoring of the system.