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Spatial and Temporal Varying Thresholds for Cloud Detection in GOES Imagery

机译:GOES影像中云检测的时空变化阈值

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A new cloud detection technique has been developed and applied to GOES-12 Imager data. The bispectral composite threshold (BCT) technique uses only the 11- and 3.9-$muhbox{m}$ channels, and composite imagery generated from these channels, in a four-step cloud detection procedure to produce a binary cloud mask at single-pixel resolution. An innovative aspect of this algorithm is the use of 20-day composites of the 11- and the 11–3.9-$muhbox{m}$ channel difference imagery to represent spatially and temporally varying clear-sky thresholds for the bispectral cloud tests. The BCT cloud detection technique has been validated against a “truth” data set generated by the manual determination of the sky conditions from available satellite imagery for four seasons during 2003–2004. The day-and-night algorithm has been shown to determine the correct sky conditions 87.6% of the time (on average) over the eastern two-thirds of the U.S. and surroundings oceans. The incorrectly determined conditions arose from missing clouds 8.9% of the time or from overdetermining clouds 3.5% of the time. Nearly 82% of the misses came in the presence of low clouds. Only small variations in algorithm performance occurred between day–night, land–ocean, and between seasons. The algorithm performed best in the warmer seasons (90.9% correct during the summer versus 81.8% correct in the winter season) and during the day, when the solar illumination provides enhanced surface atmospheric cloud contrast in the infrared channels, and least well during the winter season. The algorithm was found to slightly underdetermine clouds at night and during times of low sun angle and tends to be cloud conservative during the day, particularly in the summertime.
机译:已经开发了一种新的云检测技术,并将其应用于GOES-12成像仪数据。双光谱复合阈值(BCT)技术仅使用11-和3.9-muhbox {m} $通道,以及从这些通道生成的合成图像,通过四步云检测过程在单个像素上生成二进制云掩模解析度。该算法的一个创新方面是使用11天和11–3.9-muhbox {m} $通道差异图像的20天合成来表示双光谱云测试的时空变化的晴空阈值。 BCT云探测技术已经针对“真相”数据集进行了验证,该“真相”数据集是通过手动确定可用卫星图像中2003年至2004年四个季节的天空状况而生成的。事实证明,通过昼夜算法可以确定美国东部三分之二和周围海洋平均87.6%的正确天空条件(平均)。错误确定条件是由于8.9%的时间缺少云或3.5%的时间过高确定了云。近82%的未命中来自低云。在昼夜,陆地海洋和季节之间,算法性能只有很小的变化。该算法在较温暖的季节(夏季正确率90.9%,冬季正确率81.8%)和白天(太阳光在红外通道中提供增强的表面大气云对比度)方面表现最佳,而在冬季效果最差季节。该算法被发现在夜间和低太阳角时略微确定云量,并且在白天(尤其是在夏季)倾向于保守云。

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