###### Abstract  
The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m\({}^{3}\)/m\({}^{3}\). soil moisture; C-band; SAR; short-term change detection; multi-orbit; vegetation correction 2023152822315282315  
By using Synthetic Aperture Radar (SAR) satellite missions, this knowledge gap can be filled [9; 10; 11]. Providing a cloud- and weather-independent monitoring of the Earth's surface, SAR observations are suitable for regional and local soil moisture estimations, however, with a global extent [12]. While currently available soil moisture products from satellite missions such as Advanced Scatterometer (ASCAT) [13], Advanced Microwave Scanning Radiometer--Earth Observing System/2 (AMSR-E/AMSR2) [14; 15], Soil Moisture and Ocean Salinity (SMOS) [16], and Soil Moisture Active Passive (SMAP) [17] are limited due to their coarse resolution, various studies evaluate the potential of downscaling these products using additional remote sensing or ground reference data [18; 19; 20; 21; 22]. [PERSON] and [PERSON] [18] used Moderate Resolution Imaging Spectroradiometer (MODIS) multi-spectral data to enhance the AMSR-E soil moisture product from 25 km to 1 km spatial resolution. For the successive mission, AMSR2, [PERSON] et al. [23] used as well MODIS data, to produce a 1 km soil moisture product, while [PERSON] et al. [24] and [PERSON] et al. [19] used MODIS data to downscale SMOS 40 km resolution soil moisture product to a finer resolution of 1 km. However, the use of additional optical satellite data limits these methods to cloud-free conditions, which can lead to large gaps within monitoring, especially in tropical and subtropical regions. Using only cloud- and weather-independent radar data for downscaling, [PERSON] et al. [22] used Sentinel-1A and Sentinel-1B SAR data to enhance the SMAP radiometer resolution, increasing the resolution from 36 km to 1 km, even though the temporal resolution is degraded from 3 to 12 days due to sparse spatial overlapping of both missions. By taking advantage of recent machine learning approaches, SAR-based soil moisture estimation can be adapted to local, non-linear relationships between backscattering signals and soil moisture [25]. [PERSON] et al. [26] trained an artificial neural network (ANN) using Sentinel-1 and auxiliary in-situ data (e.g., soil moisture, soil texture, topography, vegetation measurements) to estimate soil moisture within an arid region with a resolution of 20 m. [PERSON] et al. [27] compared different machine learning approaches (Support vector machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF)) to estimate soil moisture over bare soils with a spatial resolution of 20 m. Using only Sentinel-1 backscatter time series, [PERSON] et al. [28] presented a 1-km resolution soil moisture product, using a well-established change detection algorithm operated in the Sentinel-1 Data Cube at TU Wien.