In this study, glaciers of the Patsio of Himachal Pradesh, India, are selected. The field spectra were collected for pure snow, contaminated snow, and adjacency factors using a Spectro-radiometer. Contaminants, like ash, coal, salt, sand, soil, wood, and metals are mixed with snow in the field in varying amounts (2.5g and 5g). Similarly, the spectra of green vegetation, vegetation covered with snow, wet grass, etc., were acquired. The authors also demonstrated the utility of AVRIS-NG hyperspectral data in linear spectral analysis to classify spectrally similar objects. The preliminary results from the linear unmixing technique for pure snow and its respective mixed components, like snow with debris, and frozen lakes are synchronized with the Google Earth imagery and are acclaimed for their validity. Contaminated snow analysis was done using the normalized difference snow index and S3-based snow indexes. Snow under vegetation and areas with snow cover could be mapped using both snow indexes.
TopIntroduction
In the form of snow, the Himalayan Mountains contain significant natural resources of frozen fresh water. These snow-covered regions are common in the tropics, high-altitude areas, primarily valley type, and many are littered with debris. Seasonal snow cover plays a major role in the generation of hydro-power, water storage, weather research, avalanche, and numerous more activities that contribute to the nation's economy including flood forecasting (Negi et al. 2006). Snow study has recently gained an enormous momentum in context to climate changing scenario and in accordance to IPCC 2018, that the world would be with the risk of 1.5oC warmer above the pre-industrial level and global greenhouse gas emission pathways. Himalaya, the abode of major rivers of India and consequently the livelihood of the nation, is suffering from steady shrinkage of glaciers and availability of fresh water. The snow-bearing mountains are subject to the severe wrath of nature, including frequent forest fires, storms, and blizzards, as well as air aerosols such as haze, dust, and industrial pollutants, which are contaminating snow with ash, coal, wood, dirt, and other impurities. These contaminants will be more effective at reducing the snow's albedo if they are mixed with the ice grains. A little quantity of contaminant in the snow cover spread linearly through larger particle size has a significant impact on snow reflectivity. It has been demonstrated that the increasing concentration of contaminants in snowmelt has a negative effect on water quality. For the study and prediction of contamination in snow cover, it is essential to understand how the snow is handled and which contaminants are present. Snow cover is one of the most commonly identifiable measurements of water resources from satellite photos or aerial photography when employing remote sensing (Saha et al, 2019). Some of the existing current remote sensing satellite systems are restricted to measuring snow depth, snow cover, snow quality, and snow-to-water equivalence, and do not directly monitor snow physical properties. So, hyperspectral remote sensing will be utilised for the majority of snow studies using spectral signature, since the spectral signature of snow is distinct from that of other features (Saha et al, 2019).
Hyperspectral remote sensing plays an important role in monitoring the snow cover in the vast Himalayan region, this is due to snow which produces unique diagnostics characteristics features between the spectral ranges (350–2500nm) of the electromagnetic spectrum. These diagnostic characteristics exhibit a narrow spectrum appearance. Fresh snow approximately reflects 80-90% of the incoming sunlight in the electromagnetic spectrum's visible region, and reflection decreases at a longer wavelength (Singh et. al., 2010). As compared to the reflectance characteristics of fresh snow, the snow contaminated with materials, like ash, coal, metal, wood, carbon, etc., shows that reflectance decreases maximum in the electromagnetic spectrum's visible region, and affects less in the longer wavelength region (Negi et al. 2006). Due to contamination and adjacency features, like vegetation, water, shadow, etc., estimation of snow cover in the Himalayas is usually challenging. Then, the satellite sensor receives the reflectance of contaminated snow and the effect of snow due to adjacency features (Negi et al. 2006). Thus, variations in snow reflectance between 350-2500nm are discussed in this paper under various contaminations and their effects on the reflectance of snow. This study broadly entails fresh and contaminated snow studies using ASD (Analytical spectral devices) imaging spectroscopy. The majority of snow research focuses on snow cover mapping since it readily identifies the snow cover area and facilitates the measurement of snowmelt flow. Using NDSI, snow is mapped by remote sensing (Saha, 2017).
For the most part, snow cover is evaluated using the Normalized Difference Snow Index (NDSI) (Hall et al. 1995, 2002, Gupta et al.2005, Kulkarni et al. 2002, 2006). In order to distinguish and map snow in the shadows of a mountain., NDSI makes use of the snow's low and high reflectance in the shortwave infrared (SWIR) and visible (green) region (Mourya et al., 2002).