With the availability of space imaging spectroscopy data over snow and ice-covered areas of the planet, new opportunities for snow parameter retrievals were opened. For example, PRISMA mission does not acquire images as continuously as other optical satellite missions (i.e., Sentinel-2, Landsat-8 etc.) but it allows detailed studies of the cryosphere both at mid-latitudes and at the poles in unprecedented way.In this study, we show the retrieval of snow parameters from remote sensing observations, with particular attention to snow density and snow liquid water content.

This work presents the results of an algorithm for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The algorithm is a hybrid approach based on spectral indices and radiative transfer theory and it was tested and intercompared with ground data from 6 field stations and other sensors in the European Alps for the period 2017-2023. The results indicate that for albedo the algorithm agrees well with ground measurements showing an unbiased Root Mean Square Error (ubRMSE) between 0.05 and 0.13 and a correlation coefficient ranging from 0.63 to 0.78 depending on the locations. For grain size, even though a general underestimation is found, the estimates reflect well the typical grain size metamorphosis from winter to spring. To further test the algorithm, these results from Sentinel-3 data were also confronted with ECOSTRESS thermal data specifically useful to show the metamorphosis of the grain size. For albedo, the algorithm was further applied to PRISMA hyperspectral images, showing consistent results with the values obtained by multispectral Sentinel-3 imagery.

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X- band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as “model-driven”. The second one was defined as “data-driven”. The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" RANN= 0.60, RRF= 0.68; “data-driven” RANN= 0.50, RRF= 0.88 at X-band). Moreover, the RF implementation trained with the “data-driven” approach outperformed the “model-driven” approach in terms of correlation coefficient R (RRF= 0.88 at X-band).

The main objective of the project SCIA (Sviluppo di algoritmi per lo studio della Criosfera mediante Immagini PrismA) is the development and optimization of methods for generating products related to the cryosphere. The project foresees the development of a robust processing chain of PRISMA hyperspectral data for the estimation of snow and glacier parameters in Alpine areas, through a combined use of satellite images, field data and radiative transfer models (RTMs). The image spectroscopy measurements provided by PRISMA will make possible to investigate radiometrically complex surfaces and obtain geophysical parameters currently only achievable through airborne hyperspectral sensors.

This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

In this work, we tested two algorithms for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The first algorithm is semi-empirical based on spectral indices and radiative transfer theory adapted from Painter et al. (2009, 2012) and the second is based on an approximation of the radiative transfer theory proposed by Kokhanovsky et al. (2019). Being interested mainly in mountain areas, we introduced adaptations to account for topography and heterogeneity of the area of interest. The algorithms were tested and intercompared in the European Alps for the period 2018–2021. The results indicate that for albedo both algorithms can follow the snow dynamics from winter to springtime. Both algorithms agree well with ground measurements showing a Root Mean Square Error (RMSE) between 0.05 and 0.15 and a correlation coefficient ranging from 0.71 to 0.81. As for grain size, a general underestimation is found for both methods, even though the semiempirical method follows better the typical grain size metamorphosis from winter to spring.

Alpine catchments represent a fundamental reservoir of fresh water at midlatitude. Remote sensing offers the opportunity to estimate snow properties in the optical, thermal and microwave domains. In particular, the possibility to estimate snow density from remote sensing is relevant and still represents a great challenge for the remote sensing scientific community. Since changes of snow density and liquid water content occur continuously in the snowpack, spatial and temporal patterns of optical and thermal data can give information about snowmelt processes. The main goal of this study is to evaluate if snow thermal inertia can be an indicator of snowmelt processes and to evaluate its relationship with snow variables, with particular attention to snow density. This study is a first attempt in exploiting thermal inertia for monitoring snow dynamics, and it may open new perspectives for early detection of snowmelt processes and snow parameters from remote sensing observations.