## 2 Materials and Methods
### Satellite Data  
We tested the relationship between remotely sensed LST and the LSWI and tested daily field measurements of SM and GPP to analyze the response of vegetation to SM during wet rainfall pulses. Remotely sensed day LST from MODIS Aqua and Terra products (MYD11A1 and MOD1A1 version 6.1, respectively) at 1 km resolution was considered. These products include atmospherically corrected per-pixel temperature and emissivity values. Surface temperature was also corrected from the emissivity effect. Given the long series and low data gap of MODIS MCD43A4, this nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) version 6 dataset was considered to obtain NDVI and then emissivity values for thermal field data correction (Equation (2)). This dataset was produced daily using 16 days of Terra and Aqua MODIS data at 500 m resolution. The product is created daily and represents consistent BRDF retrieval for nadir view and solar noon zenith angle over a 16-day series, with the day of interest weighted as a function of quality, observation coverage, and temporal distance from the day of interest ([[https://lpdaac.usgs.gov/products/mcd43a4v061/](https://lpdaac.usgs.gov/products/mcd43a4v061/)]([https://lpdaac.usgs.gov/products/mcd43a4v061/](https://lpdaac.usgs.gov/products/mcd43a4v061/)), accessed on 18 December 2024). On the other hand, for the calculation of the LSWI (Equation (5)), we used reflectance data from the recent Japanese Himawari-8 GEO satellite, which carries the AHI, giving a temporal resolution of 10 min. As mentioned, GEO satellites have the advantage of consistency in geometry observation. However, Ref. [5] demonstrated the incidence of sun angle variations on the NDVI and EVI from the AHI, reporting that this effect caused more than one week of uncertainty in retrieving most phenological metrics in South-eastern Australia. In our study, seasonal and spatial variations in sun angles were considered through a BRDF model [43; 44]. Daily reflectance (averaging records from 10:30 a.m. to 2:30 p.m.) data were obtained at 10 km resolution adjusted to a solar zenith angle = 45\({}^{\circ}\)and view zenith angle = 0\({}^{\circ}\). In spite of the spatial resolution, this dataset was considered because of the comprehensive temporal coverage spanning from 1 August 2017 to 28 May 2022. This extended time period offers potential to test wet pulses of different magnitudesin comparison with previous AHI non-BRDF corrected datasets. In addition, Ref. [11] reported that the correlation between the GPP and EVI in ASM was almost constant with footprints of satellite observations from 0.25 km to 35 km, showing the small improvement of using a better spatial resolution. In addition, the tower site was located in an extensive homogeneous cover of Mulga woodland [34]. The LSWI, as a spectral index of vegetation water status [24; 45; 46], was calculated as follows:  
\[LSWI=\frac{\rho_{NIR}-\rho_{SWIR}}{\rho_{NIR}+\rho_{SWIR}} \tag{5}\]  
where \(\rho\) is the surface reflectance in NIR (band 4 of AHI Himawari 8, 0.85-0.87 \(\upmu\)m) and SWIR (band 5 of AHI Himawari 8, 1.60-1.62 \(\upmu\)m). The index has a generalized equation based on the normalized difference between NIR and SWIR, with this last band being the main indicator of vegetation water status [24]. This index takes values between \(-1\) and 1. Typically, negative values are observed on bare soils or sparse vegetation cover, being almost equal to 0 in humid soil; values increase in the function of vegetation cover and vegetation water content [24]. The EVI was calculated [5; 47] as follows:  
\[EVI=2.5\frac{\rho_{NIR}-\rho_{RED}}{\rho_{NIR}+6\rho_{RED}-7.5\rho_{BLUE}+1} \tag{6}\]  
where \(\rho\) is the reflectance in NIR (band 4 of AHI Himawari 8, 0.85-0.87 \(\upmu\)m), RED (band 3 of AHI Himawari 8, 0.60-0.68 \(\upmu\)m), and BLUE (band 1 of AHI Himawari 8, 0.49-0.53 \(\upmu\)m) bands, respectively.