Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m³/m³, at low incidence, to ~0.071 m³/m³, at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

This paper describes the specifications of the surface soil volumetric water content ($\Theta$) [m³/m³] product derived from Sentinel-1 (S-1) data and assessed in the study “Sentinel-1 soil moisture at 1 km resolution: a validation study” [1]. The S-1 $\Theta$ product consists of $\Theta$ mean and standard deviation values at 1 km spatial resolution and is expected to support applications in agriculture and hydrology as well as the Numerical Weather Prediction at regional scale [2]. The retrieval algorithm is a time series based short term change detection that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (v2.0). The provided dataset represents an example of the developed S-1 $\Theta$ product and consists of a time series of 183 S-1 $\Theta$ images over Southern Italy from January 2015 to December 2018. The maps were produced for each ascending S-1 acquisition date on the Relative Orbit Number (RON) 146 and the temporal gap between consecutive maps is 6 days (when both S-1A and S-1B data are available) or 12 days.

This paper investigates the use of high resolution (~100 m) surface soil moisture (SSM) maps to detect irrigation occurrences, in time and space. The SSM maps have been derived from time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) observations. The analysis focused on the Riaza irrigation district in the Castilla y León region (Spain), where detailed information on land use, irrigation scheduling, water withdrawal, meteorology and parcel borders is available from 2017 to 2021. The well-documented data basis has supported a solid characterization of the sources of uncertainties affecting the use of SSM to map and monitor irrigation events. The main factors affecting the irrigation detection are meteo-climatic condition, crop type, water supply and spatial and temporal resolution of Earth observation data. Results indicate that approximately three-quarters of the fields irrigated within three days of the S-1 acquisition can be detected. The specific contribution of SSM to irrigation monitoring consists of (i) an early detection, well before vegetation indexes can even detect the presence of a crop, and (ii) the identification of the irrigation event in time, which remains unfeasible for vegetation indexes. Therefore, SSM can integrate vegetation indexes to resolve the irrigation occurrences in time and space.

This study proposes a hybrid incoherent–coherent change detection (CD) approach to retrieve surface soil moisture (SSM) from Sentinel-1 data. It combines time-series observations of synthetic aperture radar (SAR) backscatter and interferometric closure phase to deliver a method that does not require external calibration. A proof-of-concept assessment based on synthetic and experimental data is presented. Sentinel-1 and in situ data over a study site in Southern Italy during the Winter–Spring season 2017 that covered both bare and vegetated soil conditions have been acquired and analyzed. For bare soils, results indicate good performance, that is, Pearson correlation ≈0.8 and root mean square error (RMSE) ≈0.05 m ³ /m ³ . Conversely, over vegetated surfaces, poor results are found.