Extended Abstract (812K)4.7
A time series analysis to assess the effect of snowpack dynamics on SSM/I brightness temperatures for various land covers in Great Lakes area
Amir E Azar, NOAA-CREST, New York, NY; and R. Khanbilvardi, P. Romanov, H. Ghedira, D. Astanehasl, and P. G. Zikalala
Using satellite technology to estimate snow characteristics has been employed for many years. In particular, passive microwave images have been successfully used to estimate snow characteristics such as Snow Water Equivalent (SWE) and snow depth. Despite considerable progress, challenges still exist with respect to accuracy and reliability. It is the aim of this study to improve the estimation of snow depth using Special Sensor Microwave Imager (SSM/I) channels which are available in Equal-Area Scalable Earth Grid (EASE-GRID) format. The study area is located in the Great Lakes Region of the United States. There are 980 pixels, each 25 by 25 kilometer, covering the study area. To have a comprehensive data set of brightness temperatures (Tb) of SSM/I channels, an assortment of pixels were selected based on latitude and landcover. A time series analysis was conducted for three winter seasons to assess the SSM/I capability to estimates snow depth and SWE for various landcovers.
The actual values for snow depth or ‘ground truth data' were obtained from the National Climate Data Center (NCDC) and the National Operational Hydrological Remote Sensing Center (NOHRSC). The NCDC provided daily snow depth measurements reported from various stations located in the study area. Measurements were recorded and projected to match EASE-GRID formatting. The NOHRSC SNODAS data set was produced using airborne Gamma radiation and gauge measurements combined with a physical model. The data set consisted of different snow characteristics such as SWE and snow depth, both available in gridded format through the National Snow and Ice Data Center (NSIDC). Actual values were then compared to brightness temperature and anomalies from the SSM/I channels.
The preliminary time series results showed a various degrees of correlations between snow depths and SWE with combinations of different channels. However a consistent relationship is observed for many channels but the highest sensitivity is observed for GTV (37v-19v) for a three year period, showing correlation of 70 percent for some pixels. This is in agreement with the literature though it indicates necessity of further research to establish an algorithm to estimate SWE/Snow depth for our study area.
Session 4, Hydrologic Data Assimilation, Parameter Estimation, And Uncertainty
Thursday, 2 February 2006, 1:30 PM-5:15 PM, A403
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