A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

REMOTE SENSING OF ENVIRONMENT

卷:112

Article

Biancamaria, Sylvain¹  Mognard, Nelly M.¹  Boone, Aaron²  Grippa, Manuela³  Josberger, Edward G.⁴ 

[1] CNES CNRS IRD UPS, LEGOS, F-31400 Toulouse, France

[2] CNRS, GAME CNRM, Meteo France, F-31057 Toulouse, France

[3] CNES CNRS IRD UPS, Ctr Etud Spatiales BIOsphere, F-31400 Toulouse, France

[4] USGS, Tacoma, WA 98042 USA

关键词: SSM/I;    GSWP2;    snow depth;    high latitude regions;    tundra;    taiga;    lakes;   

DOI  :  10.1016/j.rse.2007.12.002

来源: Elsevier

【 摘 要 】

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/ Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia. The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987-1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is -0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient -0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29). (C) 2007 Elsevier Inc. All rights reserved.

【 授权许可】

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