Differential SAR interferometry for estimating snow water equivalent in central Apennines complex orography from Sentinel-1 satellite within SMIVIA project

Snow-mantle extent (or area), its local thickness (or height) and mass (often expressed by the snow water equivalent, SWE) are the main parameters characterizing snow deposits. Such parameters result of particular importance in meteorology, hydrology, and climate monitoring applications. The considerable geographical extension of snow layers and their typical spatial heterogeneity makes it impractical to monitor snow by means of direct or indirect in situ measurements, suggesting the exploitation of satellite technologies. Space-borne C-band synthetic aperture radar (SAR) sensors (such as those operating in Sentinel-1 A and B missions) are particularly suitable for the analysis of snow deposits, providing data with resolutions up to some meters with global coverage and 6-day revisit time. Most of the satellite remote sensing applications have been focused on major mountain systems, such as the Andes, the Alps, or the Himalayan region. Other important mountain systems, like the Italian Apennines, have not been extensively considered, probably due to their complex orography and the high variability of their snow cover. Nevertheless, the central Apennine has a central role for the meteorological dynamics in the Mediterranean area, and it hosts the southernmost European glacier – namely, the Calderone glacier whose evolution represents a relevant indicator, at least for the medium latitudes, of climatic changes.The implementation of the objectives of the SMIVIA (Snow-mantle Modeling, Inversion and Validation using multi-frequency multi-mission InSAR in central Apennines) project is based on the development of innovative simulation techniques and snow parameter estimators from SAR and differential interferometric SAR (DInSAR) measurements, based on the synergy with spatial measurements from optical remote sensing sensors, data from ground weather radar and simulations from dynamic snow cover models and on an inverse problem approach with a robust physical-statistical rationale. Furthermore, the scientific validity of the achievable results is supported by an enormous systematic validation effort in the Apennine area with in-situ measurements, identifying 3 pilot sites manned with meteorological and snow measurements, dielectric and georadar measurements, trenches and micro-macrophysical sampling, 6 sites of semi-automatic verification, 31 remote auxiliary sites and 1 site of glaciological interest (Calderone) with ad hoc campaigns. SAR data processing can be performed in different ways to retrieve snow parameters.In this work we exploit SAR backscattering coefficient to study the effects of backscattering at the air-snow interface, at the snow-ground interface, together with the volumetric effects of the snow layer. The distinction between wet and dry snow is obtained exploiting the copolar and cross-polar SAR returns. DInSAR is exploited to analyze the effects of air-snow refraction and the snow-ground reflection, together with the coherence and phase-shifts between two sequential images. In this work we will present the Sentinel-1 DInSAR processing chain to estimate snowpack height (SPH) combined with SAR-backscattered data for wet snow discrimination. The potential of using physically based analytical and statistical inversion algorithms, trained by forward electromagnetic and snowpack models, is introduced, and discussed. The processing chain is tested in central Apennines, using validation sites with snow-pit in-situ measurements, discussing potential developments and critical issues.