Detecting changes in root zone soil moisture from radar vegetation backscatter

<p>Root zone soil moisture (θ<sub>rz</sub>) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ<sub>rz</sub> thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ<sub>soil</sub>), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ<sub>veg</sub>) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ<sub>veg </sub>provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ<sub>rz</sub>) shows corresponding changes in vegetation backscatter (Δσ<sub>veg</sub>) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ<sub>rz</sub> up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H<sub>rms</sub>) and σ<sub>soil </sub>during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ<sub>veg</sub> by subtracting σ<sub>soil</sub> from S1 backscatter (σ<sub>tot</sub>). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ<sub>veg</sub> against Δθ<sub>rz</sub> were used investigate the dynamics over a growing season. Our main result indicates that Δσ<sub>veg </sub>- Δθ<sub>rz</sub> relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K<sub>c</sub>) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ<sub>veg </sub> matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ<sub>veg</sub> does not match well with Δθ<sub>rz</sub>. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ<sub>veg</sub> - Δθ<sub>rz</sub> relation compared to grass. The sensitivity of Δσ<sub>veg</sub> to Δθ<sub>rz</sub> in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.</p>