High resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River Basin as case study

Abstract

Abstract. Satellite sensors to infer rainfall measurements have become widely available in the last years, but their spatial resolution usually exceed 10 kilometres, due to technological limitation. This poses an important constraint on their use for application 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 a high resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and to the 25 km ASCAT soil moisture (12.5 km spacing), resampled to the same grid of S1-RT1, to obtain rainfall products with the same spatial and temporal resolution over the Po River basin. In order to overcome the need of calibration and to allow 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 day, since to the low temporal resolution of this sensor (from 1.5 to 4 days over Europe) prevents its application to infer daily rainfall. On average, the ASCAT derived rainfall product performs better than S1-RT1 one, even if the performances are equally good when 30 days accumulated rainfall is considered, being the mean Pearson’s correlation of the rainfall obtained from ASCAT and S1-RT1 equal to 0.74 and 0.73, respectively, using the parameterized SM2RAIN. 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 parameterized products performances are similar to those obtained with SM2RAIN calibration, 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.

Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop

“…However, the approach holds promise for the future by providing methods to integrate high-resolution observations into LSMs. High-resolution observations of SSM and rainfall have long been identified as gaps in generating more accurate hydrological predictions (e.g., Alfieri et al, 2022), and the EO community is constantly working on improving the space-time granularity of satellite hydrology datasets (e.g., Filippucci et al, 2022;Peng et al, 2021a, Peng et al, 2021b. The development of such high-resolution backscatter based assimilation methods is necessary to ensure the quick uptake of these newly produced datasets.…”

High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study

“…Code and data availability. The code of the SM2RAIN algorithm is available at https://doi.org/10.5281/zenodo.2203559 , while the rainfall data obtained in this work can be downloaded from https://doi.org/10.5281/zenodo.6530709 (Filippucci et al, 2022). The topographic data were downloaded and are available from the NASA Earthdata data repository.…”