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The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution usually exceeds 10 km, due to technological limitations. This poses an important constraint on its use for applications such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to two soil moisture products over the Po River basin: a high-resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and a 25 (12.5 km spacing) product derived from ASCAT, resampled to the same grid as S1-RT1. In order to overcome the need for calibration and to allow for its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 d, since the low temporal resolution of this sensor (from 1.5 to 4 d over Europe) prevents its application for infer daily rainfall. On average, the ASCAT-derived rainfall product performs better than S1-RT1, even if the performances are equally good when 30 d accumulated rainfall is considered (resulting in a mean Pearson correlation for the parameterized SM2RAIN product of 0.74 and 0.73, respectively). Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high-spatial-resolution information in obtaining spatially detailed rainfall. Finally, the performances of the parameterized products are similar to those obtained with the calibrated SM2RAIN algorithm, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide, even without the availability of a rainfall benchmark product. [ABSTRACT FROM AUTHOR]

Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI. With 25 km Metop ASCAT SSM and 1 km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. We employ a near-real-time-capable SCATSAR-SWI algorithm on a fused 3 year ASCAT-Sentinel-1-SSM data cube over Italy, obtaining a consistent set of model parameters, unperturbed by coverage discontinuities. An evaluation of a therefrom generated SCATSAR-SWI dataset, involving a 1 km Soil Water Balance Model (SWBM) over Umbria, yields comprehensively high agreement with the reference data (median R = 0.61 vs. in situ; 0.71 vs. model; 0.83 vs. ASCAT SSM). While the Sentinel-1 signal is attenuated to some extent, the ASCAT's signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Using the SM2RAIN approach, the SCATSAR-SWI shows excellent capability to reproduce 5 day-accumulated rainfall over Italy, with R = 0.89 against observed rainfall. The SCATSAR-SWI is currently in preparation towards operational product dissemination in the Copernicus Global Land Service (CGLS). [ABSTRACT FROM AUTHOR]

Two new 1km soil moisture (SM) products based on Sentinel-1 CSAR and Metop ASCAT radar observations expand the portfolio of geophysical variables of the Copernicus Global Land Service (CGLS). The CGLS provides freely accessible a large number of harmonised and co-formatted data products on energy, water and vegetation variables in Near-Real-Time (NRT). Complementing the already operational 0.1° Metop-ASCAT Soil Water Index Version3 (SWIV3), the Surface Soil Moisture (SSM) from Sentinel-1 SAR data (named SSM1km) and a SWI product based on fused SSM data from ASCAT and Sentinel-1 observations (named SWI1km) enable continental and global monitoring of SM at a 1km sampling. They feature SM dynamics with much higher detail, allowing the analysis of local hydrological patterns, such as impacts from convectional rains and irrigation activities, and thus meet the requirements of users in many fields.The SSM1km gives in percent the relative soil moisture saturation of the upmost 5 cm, every 1.5-4 days over Europe, and globally every 3-6 days. The employed algorithm processes the CSAR observations at the initial 20m scale, at which a per-pixel signal analysis drives the algorithm parameter retrieval. Subsequent resampling of pre-processed SAR data to 1km suppresses artefacts stemming from the complex radar interactions at the 20m scale, and increases the sensitivity to soil moisture dynamics.The SWI1km gives in percent the relative water content in 8 layers of the soil's upmost metre, with a daily full coverage over operated areas. ASCAT and Sentinel-1 SSM are combined per pixel with the aid of data fusion parameters. These are based on the full combined archive of Sentinel-1 and ASCAT (2015-2018) and account for the signals' differences in scaling, accuracy and spatial representativeness. Through combination of both, the high temporal resolution of ASCAT-SSM and the high spatial resolution of Sentinel-1- SSM, it is the first pre-operational dataset that allows resolving SM dynamics with a daily frequency and a 1km sampling.We present results from the first released dataset from the SSM1km and SWI1km products, covering Europe over the period 2015-2018. We compare them against in-situ measurements, reference data from ASCAT SSM, 1km SM model data, and rainfall observations. Initial experiments on data over Italy found already comprehensively high agreement of the SWI1km with all reference datasets. While the Sentinel-1 signal appears to be attenuated, the ASCAT's signal dynamics are fully transferred to the SWI1km and benefit from the Sentinel-1 parametrisation. Moreover, the SWI1km shows excellent capability to reproduce rainfall observations. The SSM1km yields a consistent set of model parameters and product masks, unperturbed by Sentinel-1 coverage discontinuities. The SSM1km shows high agreement over plains and agricultural areas and low agreement over strong topography and forests, owing to wrongly attributed vegetation dynamics. While positive biases during the growing season are detected, excellent capability to capture small-scale soil moisture changes as such from convectional rainfall or irrigation are evident, which are often missed by remotely sensed coarse-scale soil moisture products.The SSM1km and SWI1km NRT-products are currently covering Europe from 2015 ongoing and are in preparation for dissemination with global coverage. [ABSTRACT FROM AUTHOR]