Using Unsupervised Machine Learning Methods and the Nasa Snowex Swesarr Instrument to Study How Snow Water Equivalent is Changing with Climate Change

The Snow Water Equivalent Synthetic Aperture Radar and Radiometer (SWESARR) is a dual microwave instrument meant to fill in information gaps in the remote sensing data of Snow Water Equivalent (SWE). The aim of this work is to improve and validate SWESARR measurements of SWE for areas with tree canopy using unsupervised machine learning methods. This information is critical to NASA’s SnowEx mission for understanding the spatial and temporal variability of snow. SWE is an integral part of the climate system and affects many other climate-related processes, thus an accurate understanding of how SWE is changing with climate change is crucial for future water resource management. We have made use of a suite of parameters to help in identifying features most important in predicting SWE in areas that have missing satellite data due to vegetation. Our aim is to validate and improve SWESARR measurements using unsupervised machine learning clustering algorithms with the goal of being able to better quantify spatial and temporal changes of SWE due to climate change.Other relevant datasets that have been central to identifying parameters for predicting SWE and data validation have been from the ASO (Airborne Snow Observatory), NSIDC (National Snow and Ice Data Center), and ground snow pit observations taken by the SnowEx field work team. This work is a result of NASA’s SnowEx team based out of Goddard Space Flight Center within the Climate Change Research Initiative at NASA GISS.