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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >A First-Order Characterization of Errors From Neglecting Stratigraphy in Forward and Inverse Passive Microwave Modeling of Snow
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A First-Order Characterization of Errors From Neglecting Stratigraphy in Forward and Inverse Passive Microwave Modeling of Snow

机译：雪地正反被动微波建模中忽略地层误差的一阶特征

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摘要

Large-scale snow hydrology has been studied via spaceborne passive microwave (PM) measurements for decades. Forward and inverse radiative transfer (RT) models of snow are utilized in this context but typically neglect snow stratigraphy. Our objective in this paper is to characterize the expected model error in PM brightness temperature $(T_{b})$ predictions due to neglecting stratigraphy over a range of snow cover conditions. For 191 snowpits ranging from prairie to alpine, we performed side-by-side RT model runs including and ignoring stratigraphy via mass-weighted averages across stratigraphic layers; error was estimated by comparing the two RT model runs. Neglecting stratigraphy at 37 GHz led to approximately 10-K root mean square error (RMSE) for moderately deep (alpine) snow cover and to approximately 5-K RMSE for shallower (prairie) snow. RMSE across all types of snow was 1.67 and 26.9 K at 18.7 and 89 GHz, respectively. At 37 GHz, there was a low bias for deep snowpacks and a high bias for moderate-to-shallow snowpacks. Bias magnitude bias was dependent on vertical grain size variability. Based on these results and estimates of sensitivity of $T_{b}$ to snow depth, we estimated that snow depth RMSE due to neglecting stratigraphy approaches 50%.
机译：数十年来，已经通过太空无源微波（PM）测量研究了大型积雪水文学。在这种情况下使用了雪的正向和逆向辐射传输（RT）模型，但通常忽略了雪地层学。本文的目的是表征PM亮度温度中预期的模型误差 $（T_ {b}）$ 预测忽略了一系列积雪条件下的地层学。对于从草原到高山的191个雪场，我们进行了并排的RT模型运行，包括通过地层各层的质量加权平均值来忽略地层；通过比较两个RT模型运行来估计误差。忽略37 GHz的地层会导致中等深度（高山）积雪的约10-K均方根误差（RMSE），而对于较浅（草原）积雪的约5-K RMSE。在18.7 GHz和89 GHz时，所有类型雪的RMSE分别为1.67和26.9K。在37 GHz时，深雪堆有一个低偏差，而中度到浅雪堆有一个高偏差。偏差的大小偏差取决于垂直晶粒尺寸的变异性。根据这些结果和 $ T_ {b} $ 对雪深的敏感性的估计，我们估计由于忽略地层接近50％。

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