Agricultural production consumes the largest share of the world's water resources, using up to 90% of available freshwater. As demand for agricultural products is estimated to increases in the future, the accompanying intensification of production will put even more pressure on available water resources. This development makes the sector even more vulnerable to the increasing impacts of climate change on hydrological conditions and demands an even more efficient water use. Detailed knowledge of the spatial and temporal state of soil moisture, which is a key parameter in plant nutrition, agricultural production and environmental research, can help addressing these challenges. While in-situ measurement methods can be used for local monitoring, they are unsuitable for regional and even global application. In this regard, high resolution surface soil moisture data for regional and local monitoring (down to precision farming level) are still lacking at a global scale. Here, the increasing spatial and temporal resolution of current and future Synthetic Aperture Radar (SAR) satellite missions (e.g. Sentinel-1, ALOS-2/4, NISAR, ROSE-L) can help to overcome this problem. Nevertheless, the SAR missions come with individual limitations regarding soil moisture estimation. While the C-band SAR mission Sentinel-1 provides high temporal and spatial resolution, it has reduced sensitivity to soil characteristics under certain vegetation coverage due to its short wavelength. On the other hand, L-band SAR missions like ALOS-2 better penetrate covering vegetation, while its temporal and spatial resolution is reduced compared to C-band SAR missions.
Using low pass filtering as well as vegetational detrending, we developed an algorithm using high-resolution Sentinel-1 SAR timeseries for estimating soil moisture based on a change detection approach, so called alpha approximation (Balenzano et al. 2011). Tested and validated over the Rur catchment, comprising a diverse cropping structure and located in the federal state of North-Rhine Westphalia in the West of Germany, it showed encouraging results with a mean R² of 0.46 and an unbiased RMSE (uRMSE) of 5.84 %. Nevertheless, especially during the growing season, the estimated soil moisture shows a bias compared to the in-situ measured soil moisture for some crops. The reason for this is the changing sensitivity of the C-band backscattering signal to a uniform change in soil moisture occurring during the growing period. To overcome this problem, L-band ALOS-2 data is used, being less affected by vegetation cover and more sensitive to changes in soil moisture. By combining both timeseries, the high spatial and temporal resolution of C-band Sentinel-1 is used to fill the gaps between the sparse ALOS-2 recordings, while benefiting from the lower vegetation sensitivity of L-band. In this regard, this study evaluates different methods for assembling both microwave frequency bands, e.g. using L-band ALOS-2 soil moisture estimations as boundary conditions for C-band Sentinel-1 soil moisture estimation or matching changes in C-band backscattering signal to the co-located changes of L-band backscattering signal. The resulting algorithm for soil moisture estimation will be integrated within an automated workflow, using a cloud-computing platform (e.g. Google Earth Engine, CODE-DE). This enables fast processing without the use of local computational infrastructure. It will be validated over an Apulian test site, reflecting the diverse agricultural landscape of the Mediterranean region.