Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data.

This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data.

Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: Applications to the Campidano plain of Sardinia, Italy.

The aim of this study is to investigate the potential of radar (ENVISAT ASAR and RADARSAT-2) and LANDSAT data to generate reliable soil moisture maps to support water management and agricultural practice in Mediterranean regions, particularly during dry seasons. The study is based on extensive field surveys conducted from 2005 to 2009 in the Campidano plain of Sardinia, Italy. A total of 12 small bare soil fields were sampled for moisture, surface roughness, and texture values. From field scale analysis with ENVISAT ASAR (C-band, VV polarized, descending mode, incidence angle from 15.0° to 31.4°), an empirical model for estimating bare soil moisture was established, with a coefficient of determination (R(2)) of 0.85. LANDSAT TM5 images were also used for soil moisture estimation using the TVX slope (temperature/vegetation index), and in this case the best linear relationship had an R(2) of 0.81. A cross-validation on the two empirical models demonstrated the potential of C-band SAR data for estimation of surface moisture, with and R(2) of 0.76 (bias +0.3% and RMSE 7%) for ENVISAT ASAR and 0.54 (bias +1.3% and RMSE 5%) for LANDSAT TM5. The two models developed at plot level were then applied over the Campidano plain and assessed via multitemporal and spatial analyses, in the latter case against soil permeability data from a pedological map of Sardinia. Encouraging estimated soil moisture (ESM) maps were obtained for the SAR-based model, whereas the LANDSAT-based model would require a better field data set for validation, including ground data collected on vegetated fields. ESM maps showed sensitivity to soil drainage qualities or drainage potential, which could be useful in irrigation management and other agricultural applications.