Analysing the sensitivity of Sentinel-1 SAR to vegetation water dynamics using a combined model approach

Changes in plant phenology as for example earlier leaf unfolding and delayed autumn senescence can result in variations in the carbon and water cycle. Studies investigating the impact of phenological shifts on biophysical processes such as water availability are still limited. Due to the sensitivity of radar satellite observations to both, structural and dielectric properties of the scattering materials, microwave remote sensing offers the potential to analyse structural (i.e. canopy biomass) and physiological (i.e. water status) dynamics in vegetation.

Here, we aim to derive annual water dynamics of vegetation canopies from the Sentinel-1 C-band radar backscatter signal by removing the influence of vegetation structure on the backscatter seasonality. To decouple the phenology of vegetation structure from the moisture content dynamics, a semi-empirical backscattering model (Water Cloud Model, WCM) is combined with a canopy water balance model. The WCM aims to separate contributions of soil and vegetation to the total backscatter. When introducing physical parameters for vegetation structure like leaf area index (LAI) and and moisture like leaf fresh moisture content (LFMC) to describe the vegetation backscatter, the effect of the seasonal variability of both variables on the radar signal can be assessed. The canopy water balance model estimates interception and changes in the canopy saturation and storage capacity of the vegetation using precipitation and throughfall measurements. Both models are combined to iteratively estimate measures of vegetation moisture. To calibrate the two models, we use measurements of LFMC and of canopy interception for the Tharandt ecosystem site in Germany in 2022, which is part of the ICOS and FLUXNET network. The calibrated model is then used to analyse the individual effects of both vegetation descriptors, LAI and LFMC, by fixing either one and looking at the changes in the seasonality of the S1 signal. The combined use of both models will allow to remove the structural-related changes in the Sentinel-1 radar backscatter to finally retrieve vegetation water dynamics over larger areas.