Spatio‐temporal combination of MODIS images – potential for snow cover mapping
Spatio‐temporal combination of MODIS images – potential for snow cover mapping
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DOI：
10.1029/2007wr006204
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发表时间：
2008-03
影响因子：
5.4
通讯作者：
J. Parajka;Günter Blöschl
中科院分区：
文献类型：
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作者：
J. Parajka;Günter Blöschl
MODIS积雪产品因其良好的准确性和每日可用性而受到水文应用的青睐。然而，它们的主要限制是云的遮挡。在本研究中，我们评估了一种简单的制图方法，称为时空滤波器，通过使用来自相邻非云覆盖像素的时间或空间信息，并结合Terra和Aqua卫星的MODIS数据来减少云覆盖。利用2003-2005年期间754个气候站的每日雪深观测和MODIS的每日图像，对奥地利的过滤方法的精度进行了评估。结果表明，滤波技术在降云方面非常有效，得到的雪图与地面积雪观测结果仍然吻合较好。对于各种过滤方法，在准确性和云覆盖率之间存在明显的、季节性的权衡。Aqua图像的平均云覆盖率从63%降低到组合Aqua - Terra图像的52%、空间滤波器的46%、1天时间滤波器的34%和7天时间滤波器的4%，相应的总体精度分别为95.5%、94.9%、94.2%、94.4%和92.1%。
MODIS snow cover products are appealing for hydrological applications because of their good accuracy and daily availability. Their main limitation, however, is cloud obscuration. In this study we evaluate simple mapping methods, termed temporal and spatial filters, that reduce cloud coverage by using information from neighboring non‐cloud covered pixels in time or space, and by combining MODIS data from the Terra and Aqua satellites. The accuracy of the filter methods is evaluated over Austria, using daily snow depth observations at 754 climate stations and daily MODIS images in the period 2003–2005. The results indicate that the filtering techniques are remarkably efficient in cloud reduction, and the resulting snow maps are still in good agreement with the ground snow observations. There exists a clear, seasonally dependent, trade off between accuracy and cloud coverage for the various filtering methods. An average of 63% cloud coverage of the Aqua images is reduced to 52% for combined Aqua‐Terra images, 46% for the spatial filter, 34% for the 1‐day temporal filter and 4% for the 7‐day temporal filter, and the corresponding overall accuracies are 95.5%, 94.9%, 94.2%, 94.4% and 92.1%, respectively.