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.

In this letter, the relationship between temperature vegetation dryness index (TVDI) from the Moderate Resolution Imaging Spectroradiometer and subsurface soil moisture (SM) over crop and native grassland of the Argentine Pampas is analyzed. High correlation (R ² > 0.69) between TVDI and SM measurements was found at different soil depths. In addition, we found that the potential of this index to reflect subsurface soil wetness fluctuations depends on root system depth, root distribution in the soil, and physical soil characteristics. Results indicate that thermal and reflectance data combination could be used to monitor subsurface SM below vegetated areas.