Threshold functions for automated cloud analyses of global meteorological satellite imagery
Threshold functions for automated cloud analyses of global meteorological satellite imagery
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DOI：
10.1080/01431169508954653
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发表时间：
1995-12-01
影响因子：
3.4
通讯作者：
Hardy, KR
中科院分区：
文献类型：
--
作者：
Hutchison, KD;Hardy, KR
极地轨道气象卫星收集各种太阳光照和大气条件下的图像。用于从这些数据中创建自动云分析的算法必须补偿卫星绕地球运行时大气衰减和太阳散射几何形状变化引起的云特征变化。在本文中，提出了一种方法，描述了云光谱特征的变化，卫星观测条件的变化。关系的发展，描述太阳光照和散射几何光学厚水云的光谱特征在白天的高级甚高分辨率辐射计（AVHRR）可见光和近红外图像的影响。另外的关系，描述总集成的水蒸气光学薄卷云和层云的光谱特征的夜间AVHRR红外线的影响。阈值函数，然后来自这些关系，并证明在高分辨率AVHRR图像的自动分析。每个自动分析的准确性都是根据从多光谱图像创建的地面实况（手动）云-无云分析来测量的。它的结论是，高度准确的自动化云分析是可以实现的双光谱云检测技术，采用阈值函数的方法来补偿全球云光谱特征的变化。
Polar-orbiting meteorological satellites collect imagery across a wide range of solar illumination and atmospheric conditions. Algorithms used to create automated cloud analyses from these data must compensate for variations in cloud signatures caused by changes in atmospheric attenuation and solar scattering geometry that occur as the satellite orbits the Earth. In this paper, a methodology is presented that describes the variations in cloud spectral signatures that result from changes in satellite observational conditions. Relationships are developed that describe the impact of solar illumination and scattering geometry on the spectral signature of optically-thick water clouds in the daytime Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared imagery. Additional relationships are presented that describe the impact of total integrated water vapour on the spectral signatures of optically-thin cirrus and stratus clouds in night-time AVHRR infrared. Threshold functions are then derived from these relationships and demonstrated in the automated analysis of high resolution AVHRR imagery. The accuracy of each automated analysis is measured against a ground truth (manual) cloud-no-cloud analysis created from the multi-spectral imagery. It is concluded that highly accurate automated cloud analyses are achievable using bi-spectral cloud detection techniques that employ the threshold function methodology to compensate for global variations in cloud spectral signatures.