Using Artificial Neural Networks to Couple Satellite C-Band Synthetic Aperture Radar Interferometry and Alpine3D Numerical Model for the Estimation of Snow Cover Extent, Height, and Density

This work presents a new approach for the estimation of snow extent, height, and density in complex orography regions, which combines differential interferometric synthetic-aperture-radar (DInSAR) data and snowpack numerical model data through artificial neural networks (ANNs). The estimation method, subdivided into classification and estimation, is based on two ANNs trained by a DInSAR response model coupled with Alpine3D snow cover numerical model outputs. Auxiliary satellite training data from satellite visible-infrared MODIS imager as well as digital elevation and land cover models are used to discriminate wet and dry snow areas. For snow cover classification the ANN-based estimation methodology is combined with fuzzy-logic and compared with a consolidated decision threshold approach using C-band SAR backscattering information. For snow height (SH) and density estimation, the proposed methodology is compared with an analytical inverse method and two model-based statistical techniques (linear regression and maximum likelihood). The validation is carried out in Central Apennines, a mountainous area in Italy with an extension of about 104 km
²
and peaks up to 2912 m, using in situ data collected between December 2018 and February 2019. Results show that the ANN-based technique has a snow cover area classification accuracy of more than 80% when compared MODIS maps. Estimation bias and root mean square error are equal to about 0.5 cm and 20 cm for SH and to 5 kg/m
³
and 80 kg/m
³
for snow density. As expected, worse results are associated with low DInSAR coherence between two repeat passes and snow melting periods.