Observations and statistical analysis of combined active–passive microwave space-borne data and snow depth at large spatial scales
Observations and statistical analysis of combined active–passive microwave space-borne data and snow depth at large spatial scales
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DOI：
10.1016/j.rse.2007.04.019
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发表时间：
2007-11
影响因子：
13.5
通讯作者：
M. Tedesco;J. Miller
中科院分区：
文献类型：
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作者：
M. Tedesco;J. Miller
基于主动和被动微波数据的遥感是在高时间分辨率和大空间尺度上研究冰冻圈状态的有用工具。特别是，从星载数据中检索雪参数可以有利于水文、气象和气候应用。本文分析了 1999-2004 年期间北半球不同地点雪深值与 NASA 快速散射仪 (QuikSCAT) 测量的 Ku 波段后向散射系数以及特殊传感器微波成像仪 (SSM/I) 测量的 K 和 Ka 波段亮温的变化趋势。我们还首次在非常大的空间尺度上量化了积雪覆盖区域的星载 Ku 波段散射仪数据的动态范围，并与被动微波亮度温度的范围进行了比较。我们还首次量化了与主动和被动数据结合使用相关的雪深检索的改进，并将结果与仅使用主动或被动数据获得的结果进行比较。最后，我们报告了由 EOS 先进微波扫描辐射计 (AMSR-E) 收集的 X 波段亮度温度分析的初步结果，旨在了解使用主动和被动数据组合所带来的改进是否与低频的使用或所使用的不同技术（例如主动或被动）有关。
Remote sensing based on active and passive microwave data represents a useful tool for studying the state of the cryosphere at high temporal resolution and large spatial scale. In particular, retrieving snow parameters from space-borne data can benefit hydrological, meteorological, and climatological applications. In this paper, we analyze the trend of Ku band backscatter coefficients measured by the NASA's Quick Scatterometer (QuikSCAT) and K and Ka band brightness temperatures measured by the Special Sensor Microwave Imager (SSM/I) with respect to snow depth values at different locations in the Northern hemisphere during the period 1999–2004. We also quantify, for the first time, the dynamic range of space-borne Ku band scatterometer data over snow covered areas at very large spatial scale in comparison to the range in passive microwave brightness temperatures. Also for the first time, we quantify the improvement on the snow depth retrieval related to the combined use of active and passive data and compare the results with those obtained using either only active or passive data. Finally, we report first results regarding an analysis involving X-band brightness temperatures, collected by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), aiming at understanding whether the improvements derived from using a combination of active and passive data are related to the use of a low frequency or to the different techniques used (e.g., active or passive).