 This paper presents a methodology that utilizes the triangle method based on a feature space developed with NIR reflectance and NDSI for estimating surface snow wetness, permittivity, and density. The triangular feature space is parameterised to yield a linear relationship between snow wetness and NIR reflectance, while snow density and permittivity are derived from least-square solutions of empirical relations based on observed surface snow wetness. The proposed methodology was evaluated using Sentinel-2 data and validated with field measurements, resulting in good agreement for wet snow conditions. The approach can be utilised with unmanned aerial sensors for monitoring physical properties of fresh or wet snow and is expected to contribute significantly to hydrological applications and avalanche studies. This article was authored by Devesh Vaid and Ankar Dixit.