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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Snow wetness and density retrieved from L-band satellite radiometer observations over a site in the West Greenland ablation zone
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Snow wetness and density retrieved from L-band satellite radiometer observations over a site in the West Greenland ablation zone

机译:从西格陵兰消融区的网站上的L波段卫星辐射计观察中检索雪湿度和密度

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摘要

We demonstrate a novel method to retrieve snow liquid water content and density over a site in the ablation zone of the Western Greenland Ice Sheet from L-band radiometer data measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. Previous demonstrations using ground-based close-range radiometry separately retrieved snow density and snow wetness over frozen and thawed ground. We apply similar techniques over the ice sheet to simultaneously retrieve snow density and wetness at the location of "Swiss Camp" from June 2010 through August 2018 at nearly daily temporal resolution. Achieved results are compared to in-situ air temperature data and to a well-known 19 GHz and 37 GHz passive-microwave melt characterization technique known as the cross-polarized gradient ratio (XPGR). The L-band based snow wetness retrievals often detect the onset of seasonal melt earlier than the XPGR algorithm without the need for empirically tuned thresholds. We also demonstrate the performance of the SMOS based snow wetness retrievals based on error statistics compared with an air temperature melt proxy. By applying temporal averaging to the SMOS based snow density retrievals, we achieve reasonable agreement with in-situ observations from May 2014 and May 2018. The demonstrated retrieval algorithm shows potential as a future SMOS data product for ice-covered regions of the cryosphere.
机译:我们展示了一种新的方法,以从土壤水分和海洋盐度(SMOS)卫星测量的L波段辐射计数据中检索雪地液体含水量和密度。以前使用地面近距离辐射测定的演示分别检索过冷冻和解冻地的雪密度和雪湿度。我们在冰盖上应用类似的技术,同时在2010年6月至2018年8月的“瑞士阵营”的位置以几乎日常的时间分辨率来检索雪密度和湿度。将达到的结果与原位空气温度数据进行比较,并以众所周知的19GHz和37GHz被动微波熔融表征技术称为交叉极化梯度比(XPGR)。基于L波段的雪湿度检索经常检测比XPGR算法早于季节熔体的开始,而无需经验上调的阈值。我们还展示了基于误差统计的SMOS基础的雪湿度检索的性能与空气温度熔体代理相比。通过将时间平均应用于基于SMOS的雪密度检索,我们从2014年5月和2018年5月实现了合理的协议。所示的检索算法显示了冰冻层的冰盖区域未来的SMOS数据产品。

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