Abstract. 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.