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 m³/m³.

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 paper investigates the use of dense time series of C-band SAR data (i.e. acquired with revisit time within 1-2 weeks) for the retrieval of volumetric soil moisture content (m _v ) underneath agricultural crops. Its final aim is to contribute at assessing retrieval strategies for monitoring agricultural areas using near future frequent-revisit SAR missions, such as the forthcoming European Sentinel-1. A recently developed mγ retrieval algorithm is firstly presented and then applied to a time series of ASAR HH and HV data collected in 2006 over the agricultural DEMMIN site (Germany). The assessment of the algorithm is provided by comparing the SAR-derived m _v maps over the DEMMIN site with hydrologically modelled m _v maps. Results indicate that m _v can be retrieved with accuracy ranging between 5% and 6%.

Monitoring soil water content (SWC) is vital for various applications, particularly in agriculture. This study compares SWC estimated by means of SCATSAR-SWI remote sensing (RS) at different depths (T-values) with Cosmic Ray Neutron Sensing (CRNS) across four agricultural sites in northern Italy. Additionally, it examines the spatial mismatch and representativeness of SWC products’ footprints based on different factors within the following areas: the Normalized Difference Vegetation Index (NDVI), soil properties (sand, silt, clay, Soil Organic Carbon (SOC)), and irrigation information. The results reveal that RS-derived SWC, particularly at T = 2 depth, exhibits moderate positive linear correlation (mean Pearson correlation coefficient, R = 0.6) and a mean unbiased Root–Mean–Square Difference (ubRMSD) of 14.90%SR. However, lower agreement is observed during summer and autumn, attributed to factors such as high biomass growth. Sites with less variation in vegetation and soil properties within RS pixels rank better in comparing SWC products. Although a weak correlation (mean R = 0.35) exists between median NDVI differences of footprints and disparities in SWC product performance metrics, the influence of vegetation greenness on the results is clearly identified. Additionally, RS pixels with a lower percentage of sand and SOC and silt loam soil type correlate to decreased agreement between SWC products. Finally, localized irrigation practices also partially explain some differences in the SWC products. Overall, the results highlight how RS pixel variability of the different factors can explain differences between SWC products and how this information should be considered when selecting optimal ground-based measurement locations for remote sensing comparison.

Cosmic-ray neutron sensing (CRNS) has gained popularity for estimating soil moisture due to its innovative capability to measure at an intermediate scale – a notable advantage over point-scale sensors, which are often sparsely installed and lead to inaccurate absolute values due to small-scale heterogeneity. CRNS serves as a crucial link between small and large scales and has been emerging as a reference measurement for validating remote sensing algorithms. However, the sparse availability of long-term datasets limits use of this possibility. Within the DFG-research unit Cosmic Sense and the European 21GRD08 SoMMet project, multiscale soil moisture monitoring was implemented to integrate CRNS with complementary in-situ observations. In this paper, we present harmonized soil moisture data from different sensor types, including a CRNS cluster, shallow soil moisture measurements, and soil moisture profile data, creating a ready-to-use dataset as a reference observation for remote sensing products, covering a highly-instrumented agricultural site in the northeast of Germany. The newly established Potsdam Soil Moisture Observatory (PoSMO) comprises 16 stationary CRNS sensors with point-scale soil moisture sensors installed at the same locations in different depths and data from intensive manual sampling campaigns (covering soil moisture, bulk density, organic matter, etc.). This dataset goes beyond other studies by covering a larger area of approx. 1 km², while nevertheless achieving a high sensor density and mostly overlapping CRNS footprints allowing for nearly complete coverage. Complementary measurements of soil properties, vegetation, groundwater, meteorology, and remote sensing imagery provide the context required to interpret the observed soil moisture dynamics across spatial and temporal scales. The data are available at https://doi.org/10.23728/b2share.bxamy-4zh85 (Grosse et al., 2025) and provide a new reference dataset for remote sensing products, hydrological or land-surface models, and other applications related to soil water balance.