EGU2020-1005, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Detecting changes in root zone soil moisture from radar vegetation backscatter

Coleen Carranza1, Tim van Emmerik2, and Martine van der Ploeg2
Coleen Carranza et al.
  • 1Soil Physics and Land Management Group, Wageningen University, Wageningen, Netherlands (
  • 2Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, Netherlands

Root zone soil moisture (θrz) 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 θrz thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σsoil), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σveg) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σveg 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 (Δθrz) shows corresponding changes in vegetation backscatter (Δσveg) 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 θrz 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 (Hrms) and σsoil during vegetated periods. Afterwards, the Water Cloud Model was used to derive σveg by subtracting σsoil from S1 backscatter (σtot). 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 Δσveg against Δθrz were used investigate the dynamics over a growing season. Our main result indicates that Δσveg - Δθrz 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 (Kc) 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 Δσveg 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 Δσveg does not match well with Δθrz. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσveg - Δθrz relation compared to grass. The sensitivity of Δσveg to Δθrz 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.

How to cite: Carranza, C., van Emmerik, T., and van der Ploeg, M.: Detecting changes in root zone soil moisture from radar vegetation backscatter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1005,, 2019


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