Development of a passive microwave global snow depth retrieval algorithm for Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data
Article first published online: 17 JUL 2003
Copyright 2003 by the American Geophysical Union.
Volume 38, Issue 4, August 2003
How to Cite
2003), Development of a passive microwave global snow depth retrieval algorithm for Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data, Radio Sci., 38, 8076, doi:10.1029/2002RS002648, 4., and (
- Issue published online: 17 JUL 2003
- Article first published online: 17 JUL 2003
- Manuscript Accepted: 20 AUG 2002
- Manuscript Revised: 2 AUG 2002
- Manuscript Received: 5 MAR 2002
- snow depth;
- passive microwave;
 This paper describes research conducted to develop an integrated snow monitoring algorithm at global and regional scales for the Special Sensor Microwave Imager (SSM/I) and the planned Advanced Microwave Scanning Radiometer-EOS. Methods to detect global snow cover are well advanced and have been applied routinely at local, regional, and global scales. Current snow cover retrieval methods tend to underestimate snow volume because important fractional forest cover and snowpack physical properties are not well parameterized in simple snow parameter retrieval schemes. Building on a static approach to snow depth retrieval, a spatially dynamic algorithm is described that incorporates information about fractional forest cover and snowpack physical development. SSM/I brightness temperatures of land surfaces are calibrated with ground measurements of snow depth through empirical statistical and geostatistical models to produce globally distributed snow depth estimates. The calibration is based on a 4-year and a one winter season record of daily snow depth and SSM/I observations. The biasing effect of forest cover on microwave estimates is quantified and short-term fluctuations of microwave-estimated snow depth (caused by physical changes in the pack) are reduced by time smoothing daily estimated snow depth values. For the data sets used the estimated snow depth from the new algorithm gives global seasonal errors of 13.7 and 22.1 cm for the two data sets. Further major improvements in snow depth retrieval methods will only be achieved through enhancements to forest and snow metamorphism parameterizations.