Improvement in Estimating Snowpack Properties with SSM/I Data and Land Cover Using Artificial Neural Networks

The goal of this study is to develop an algorithm to estimate snow water equivalent (SWE) in Great Lakes area based on a three year time series of SSM/I data along with corresponding ground truth data. An assortment of SSM/I EASE-GRID pixels was selected for time series analysis. The pixels were selected based on the amount of snow, latitude, and land cover. Two types of ground truth data were used: 1 - point-based snow depth observations from NCDC; 2 - grid based SNODAS-SWE dataset, produced by NOHRSC. To account for land cover variation in a quantitative way a NDVI was used. To do the time series analysis, three scattering signatures of GTVN (19V-37V), GTH (19H-37H), and SSI (22V-85V) were derived. The analysis shows at lower latitudes of the study area there is no correlation between GTH and GTVN vs snow depth. On the other hand SSI shows an average correlation of 75 percent with snow depth in lower latitudes makes it suitable for shallow snow. In model development a multi-linear algorithm was defined to estimate SWE using NDVI values along with the location of the pixels as classification criteria. The results show up to 60 percent correlation between estimated and ground truth SWE