Mapping Seasonal Snow Cover Variation in the Mago River Basin of Arunachal Pradesh using Ensemble Machine Learning Algorithm

In this study, a comprehensive analysis is performed to interpret the change in Snow Cover Area (SCA) using traditional and ensemble machine learning algorithm in Mago Basin, Arunachal Pradesh in the North-Eastern Himalayan Region (NEHR). The changes further aim to analyse the impacts on the glacio-hydrology of the region. Precise geographical data on snow cover is valuable for comprehending the effects of global warming, and it has great importance for predicting hydrological disasters, managing water resources, and conducting climate change research. The NDSI-based technique has been widely employed globally to map snow and has demonstrated exceptional accuracy in open regions. This study presents a new machine learning (ML) approach that uses remotely sensed surface reflectance to increase the accuracy of snow cover mapping. The model achieved a median F1 score of 0.97 exhibiting an excellent performance in accuracy mapping of snow cover.