Development of Processing Algorithms for the Retrieval of Snow/Ice Parameters from SAR Data

The primary objective of this project is to extract wet/dry snow parameters which helps in snow avalanche effect. The most powerful way to evaluate the degree of snow cover extent (SCE) and the method of snow melting. The most significant connection between natural phenomena and human activities is SCE. This research is conducted by using satellite images of Sentinel-1 and Sentinel-2. For snow avalanche and snow melt runoff modelling related studies in this field, the retrieved snow density is highly useful. This research is mainly based on backscattering value of that particular region with snow density. The ESA SNAP is used for basic satellite image filtration, and then the python model is developed. Satellite data will be used in this study for noise reduction, dimensionality reduction, and Geo-code correction. A calibration technique is used to reduce noise by normalizing SAR data to Sigma0 band values. Speckle filtering is used to standardize data from bigger (7 × 7) to smaller (3 × 3) windows. The Range Doppler method is used for geo-coding conversion in terrain rectification. Using the given approach, wet/dry snow may be discriminated by its band value parameter. As a consequence, to understand the snow avalanche effect, snow covers are the most significant environmental research. Earlier approaches operate on SCE or SWE, with certain exceptions. Both parameters are examined in this study. Filtering and prediction are performed for both parameters.