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Retrieval of snow water equivalent using passive microwave brightness temperature data

机译:使用被动微波亮度温度数据检索雪水当量

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Existing algorithms for retrieving snow water equivalent (SWE) from the Special Sensor Microwave/Imager (SSM/I) passive microwave brightness temperature data were assessed and new algorithms that include physiographic and atmospheric data were developed for the Red River basin of North Dakota and Minnesota. The frequencies of SSM/I data used are 19 GHz and 37 GHz in both horizontal and vertical polarization. Encouraging calibration results are obtained for the algorithms using multivariate regression technique and dry snow cases of the 1989 and 1988 SSM/I data (from DMSP-F8). Similarly, validation results for data not used in calibration [e.g., 1988 (1989 as calibration data), 1989 (1988 as calibration data), and 1997 (from DMSP-F10 and F13)] are also encouraging. The nonparametric, Projection Pursuit Regression (PPR) technique also gave good results in both stages. However, for the validation stage, adding a shift parameter to all retrieval algorithms was always necessary, possibly because of different scatter-induced darkening (caused by scattering albedo), which could arise even for snowpacks of the same thickness because snowpacks undergo different metamorphism in different winter years. Screening criteria are also proposed to eliminate SSM/I footprints affected by large water bodies and depth-hoar-another key step toward reliable SWE estimation from passive microwave data. (C) 2000 Elsevier Science Inc. [References: 26]
机译:评估了从特殊传感器微波/成像仪(SSM / I)被动微波亮度温度数据中检索雪水当量(SWE)的现有算法,并为北达科他州和明尼苏达州的红河盆地开发了包括地貌和大气数据的新算法。在水平极化和垂直极化中,所使用的SSM / I数据的频率分别为19 GHz和37 GHz。使用多变量回归技术以及1989年和1988年SSM / I数据的干燥积雪情况(来自DMSP-F8),使用算法获得了令人鼓舞的校准结果。类似地,对于未用于校准的数据的验证结果也令人鼓舞,例如,1988年(1989年为校准数据),1989年(1988年为校准数据)和1997年(来自DMSP-F10和F13)。非参数投影寻踪回归(PPR)技术在两个阶段也都给出了良好的结果。但是,对于验证阶段,始终需要向所有检索算法中添加一个移位参数,这可能是由于不同的散射引起的变暗(由散射反照率引起),即使对于相同厚度的积雪,由于积雪经历不同的变质作用,这种情况也可能出现不同的冬季。还提出了筛选标准,以消除受大型水体影响的SSM / I足迹和深度depth声,这是从被动微波数据获得可靠SWE估计的另一个关键步骤。 (C)2000 Elsevier Science Inc. [参考:26]

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