In Australia, several remote sensing studies have used vegetation indices to monitor productivity and phenology changes, reporting differences in the timing, magnitude, and duration of vegetation response according to hydrological variability [11; 19; 22; 23]. In general, these studies, at coarse temporal scales, reported certain lags between rainfall events and an increase in the indices, suggesting limitations for the early monitoring of the impact of those events. Other spectral bands, such as short-wave infrared (SWIR) and thermal infrared (TIR), have been less explored in semi-arid Australian areas. Spectral indices such as the Land Surface Water Index (LSWI), which take into account near-infrared (NIR) and SWIR bands, have been widely associated with vegetation water content and vegetation water stress, given the absorption of liquid water in SWIR [24; 25; 26]. They can be an alternative to traditional vegetation indices. However, the temporal response in semi-arid, natural regions to rainfall wet pulses has scarcely been explored. It should be noted that, for a certain rainfall pulse, spectral indices can show different values during a season with different general hydrological conditions (e.g., normal or dry seasons). On the other hand, thermal data have been considered to study the vegetation status based on the partitioning of incoming solar radiation into latent (ET) and sensible heat fluxes under different levels of soil water available for ET [27]. The estimation of vegetation water status is based on the following simplified surface energy balance equation:  
\[\text{Rn}=\text{LE}+\text{H}+\text{G} \tag{1}\]  
where Rn is the net radiation, LE is the latent heat flux, H is the sensible heat flux, and G is the soil heat flux (G is almost equal to 0 over maximum vegetation cover). All these terms are expressed in w m\({}^{-2}\). Different variables are associated with LST. First, incoming solar radiation is one of the main drivers [27]. Atmospheric water demand and surface roughness control the flux of heat from the surface to the atmosphere [28]. Thus, potential ET is mainly related to radiative and atmospheric factors. ETA is a key variable determining LST through the surface energy balance depending on changes in SM. LST has been widely used as a simple indicator of vegetation water status, given that, over vegetated areas, the distribution of incoming solar radiation into H and LE mainly depends on stomatal resistance to respiration. For a region under homogeneous atmospheric forcing and surface roughness, LST is strongly related to SM in the root zone available for the ET process [27; 29; 30; 31]. In this context, changes in LST should be associated not only with surface SM but also with deep SM, depending on vertical root distribution and the sensitivity of the vegetation to that variable [12; 30]. These concepts have scarcely been applied to monitor dryland natural areas of Central Australia. This study aimed to analyze the vegetation response to in situ root zone SM during wet rainfall events in an area of Mulga savanna woodland in Central Australia through LSWI and LST data from AHI Himawari 8 and the Moderate Resolution Imaging Spectro-radiometer (MODIS), respectively. The novelty of this study lies in its identification of a satellite-derived indicator of vegetation status, which is related to root zone SM increases during wet rainfall events. Given the scarcely explored potential of this variable to monitor and predict its impact on vegetation [12], an early proxy associated with increases in water in the soil profile can provide a source for monitoring and predicting vegetation condition and productivity. This will contribute to the understanding and spatial monitoring of wet pulse impact on vegetation in the complex semi-arid systems of that region with limited spatial data coverage.