The main objective of this work is to estimate Snow Water Equivalent (SWE) by jointly exploiting the information derived from X-band Synthetic Aperture Radar (SAR) imagery acquired by the Italian Space Agency COSMO-SkyMed satellite constellation in StripMap HIMAGE mode and manual SWE ground measurements. The idea is to verify the sensitivity of the backscattering coefficient at X-band to the SWE and, by means of a Support Vector Regression (SVR) algorithm, to estimate the SWE for the South Tyrol region, north-eastern Italy. The regressor is trained by exploiting about 1,000 simulated backscattering coefficients corresponding to different snowpack conditions, obtained with a theoretical model based on the Dense Media Radiative Transfer theory - Quasi-crystalline approximation Mie scattering of Sticky spheres (DMRT-QMS). Then, the performance is evaluated on the backscattering values derived from COSMO-SkyMed satellite images and using the corresponding ground measurements of SWE as references. The results show a correlation coefficient equal to 0.6, a bias of 10.5 mm and a RMSE of 51.8 mm between estimated SWE values and ground measurements. The limited performance could be related to the DMRT-QMS theoretical model used for the simulations that results to be very sensitive to snow grain size and may have generated a training dataset only partially representative of satellite derived backscattering coefficients used for testing the algorithm.
The accurate monitoring and understanding of glacier dynamics are of high relevance for climate science and water-resources management. The glacier parameters are typically estimated by data assimilation methods which inject field measurements into the numerical simulations with the aim of improving the physical model estimates. However, these methods often are not able to capture and model the complexity of the estimation problem. To solve this problem, this paper proposes a method that integrates remote sensing (RS) data, in-situ observations and a physical-based model to accurately estimate the Glacier Mass Balance (GMB). The RS data are used to represent the physical properties of the glaciers by characterizing their topography and spectral properties. Instead of assimilating the observations into the model, the in-situ measurements are used to perform a data-driven correction of the GMB estimates derived from the physically-based simulations in the informative RS feature space. The method is applied to the Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) hydro-climatological model. In the experimental analysis, the multispectral images used to define the feature space are high-resolution Sentinel-2 images. The method is validated on three glaciers in Tyrol (Hintereis, Kasselwand and Varnagt glaciers), in 2015 and 2016. The obtained results show the effectiveness of the method in improving the GMB estimates.