Utilizing Machine Learning and Anomaly Detection to Detect Skewed SWESARR Data and Understand How Snow Water Equivalent Changes Spatially

The SWESARR instrument produces unreliable results when flying over terrain populated by foliage. As a result, our team used a modified version of the LOF algorithm to detect subtle and extreme disturbances in the SWESARR data. We were only able to work with the VV polarization of the X band and the VH polarization of the Ku band as those were the sets of data where we found a significant correlation between the LOF data and the original data. Currently, we can only use the LOF data to make predictions as to where the foliage might be. But in the future, we aim to overlay the two sets of data to find precise locations and remove the need for ground truth measurements. NASA’s SWESARR airborne instrument collected the relevant data used in this research experiment.