University of Twente Student Theses


Snow Depth and SWE estimation using Multi-sensor Microwave and Optical Remote Sensing Time series Data for Indian Himalayas

Pandey, Shivang (2022) Snow Depth and SWE estimation using Multi-sensor Microwave and Optical Remote Sensing Time series Data for Indian Himalayas.

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Abstract:Seasonal Snow in Indian Himalayas plays important role for water resources in South Asia which give water supply over the hundreds of millions of people living in most of the south Asian countries and hydro-electricity city to the several states of northern India. The study of SD and SWE can play a major role in forecasting yearly water supplies, flood prediction, and general climate research. Seasonal snow is an important part of the Himalayan hydrological system which gives water to billions of people and is a major source of generation of hydroelectricity in south Asian region. Depending on the availability and variety, mathematical model selection, and the hydro-meteorological conditions of the area of interest of the data, it is extremely difficult to estimate these two factors accurately. Estimation of SD and SWE using multi-sensor microwave and optical remote sensing time series data for Indian Himalayas is feasible but challenging. This thesis is intended to develop a model which can estimate SD and SWE values at optimal temporal and spatial resolution using multi-sensor data like active and passive microwave sensors, optical sensors and other climatological and topographical factors which affects snow and its physical properties. In this research work, two study areas were the focus which are Beas and Sutlej basin in North-western Himalayas. Both basins are large water resources supply as well as snowmelt indulge in crucial role in terms to provide water supply. Water year October 2016 to September 2017 has been selected for this research work as per the optimum availability of the input dataset that has been used in this thesis work. Machine Leaning based model has been trained to obtain SD and SWE estimations from extracted input features from different dataset and prediction later has been done. Also, downscaling has been done for coarser resolution datasets. The quality of developed model (SVR) has been analysed by creating another machine learning model (RFR) to check how accuracy metrices are varying in similar circumstances in two different developed models. As SD and SWE values varies a lot with elevation and aspect in the mountainous regions like Himalayas, an analysis has been done to observe how aggregated SD and SWE varying with these parameters. Analysis has been performed for month of February, which is approximately most snowy month of the years 2017, 2018 and 2019. Other ideas for improving the model quality, issued that arrived in research phase and drawbacks of the research work has been discussed in this report.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Subject:02 science and culture in general, 38 earth sciences, 43 environmental science, 54 computer science
Programme:Geoinformation Science and Earth Observation MSc (75014)
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