Water availability for vegetation use has been associated with the relative amount of water in the plant and is a key factor for modeling variables related to the soil-plant system (e.g., net primary production, drought effects on vegetation). To the best of our knowledge, the integration of spectral proxies of vegetation water content (near-infrared (NIR), shortwave-infrared (SWIR) bands) and land surface temperature (LST) for estimation, not only of vegetation water content but also soil water available for the evapotranspiration process requires more analysis. This study aims to assess the relationship between NIR, SWIR reflectance, and LST data as indicators of water availability for crop use. For this purpose, vegetation water content, LST, and spectral reflectance over soybean, corn, and barley were measured in the field and the laboratory. Based on the consistency of satellite data from Moderate-Resolution Imaging Spectroradiometer (MODIS/Aqua) in relation to such measurements, a model is proposed, which can be parameterized from remotely sensed NIR-SWIR/LST scatterplots. The obtained results were tested in the Argentine Pampas, showing coherence with surface processes at regional scale associated with soil water availability. The comparison with soil moisture at different depths (R² > 0.7) showed that the method is sensitive to variations in root zone water availability. Given the reliance of the index on just satellite data, it can be pointed that the potential not only for vegetation water stress analyses but also in the context of hydrological modeling as an input of water availability.

Optimization of field techniques is crucial to ensure high quality soil moisture data. The aim of the work is to present a sampling method for undisturbed soil and soil water content to calibrated soil moisture probes, in a context of the SMOS (Soil Moisture and Ocean Salinity) mission MIRAS Level 2 soil moisture product validation in Pampean Region of Argentina. The method avoids soil alteration and is recommended to calibrated probes based on soil type under a freely drying process at ambient temperature. A detailed explanation of field and laboratory procedures to obtain reference soil moisture is shown. The calibration results reflected accurate operation for the Delta-T thetaProbe ML2x probes in most of analyzed cases (RMSE and bias ≤ 0.05 m3/m3). Post-calibration results indicated that the accuracy improves significantly applying the adjustments of the calibration based on soil types (RMSE ≤ 0.022 m3/m3, bias ≤ -0.010 m3/m3). •A sampling method that provides high quality data of soil water content for calibration of probes is described.•Importance of calibration based on soil types.•A calibration process for similar soil types could be suitable in practical terms, depending on the required accuracy level.

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands.