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Global identification of snowcover using SSM/I measurements

机译:使用SSM / I测量值对积雪进行全局识别

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Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product.
机译:可见的卫星传感器已经监测了整个北半球的积雪已近三十年了。这些传感器可以在白天,无云的情况下检测积雪。由NOAA / NESDIS开发的操作程序要求分析人员手动查看图像,以便主观地区分云层和积雪。因为此过程是手动密集型的,所以仅每周执行一次。由于微波传感器可以穿透不降水的云层,因此无需分析师的干预即可客观地确定积雪。此外,微波传感器可以实时提供积雪的每日分析,这对于运行预测模型和区域水文监测至关重要。积雪测量值是从DMSP卫星上的特殊传感器微波成像仪(SSM / I)获得的。包含各种过滤器的决策树用于将积雪的散射特征与其他散射特征分开。讨论了问题区域,并在可能的情况下开发了消除偏差的滤波器。最终的决策树是一种目标算法,用于监视积雪的全局分布。在SSM / I积雪产品和NOAA / NESDIS主观分析的每周产品之间进行了比较。

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