Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

Lingjia Gu; Ruizhi Ren; Kai Zhao; Xiaofeng Li

doi:10.1117/1.JRS.8.084682

3 November 2014 Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

Lingjia Gu, Ruizhi Ren, Kai Zhao, Xiaofeng Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 084682 (November 2014). https://doi.org/10.1117/1.JRS.8.084682

Abstract

The precision of snow parameter retrieval is unsatisfactory for current practical demands. The primary reason is because of the problem of mixed pixels that are caused by low spatial resolution of satellite passive microwave data. A snow passive microwave unmixing method is proposed in this paper, based on land cover type data and the antenna gain function of passive microwaves. The land cover type of Northeast China is partitioned into grass, farmland, bare soil, forest, and water body types. The component brightness temperatures (CBT), namely unmixed data, with 1 km data resolution are obtained using the proposed unmixing method. The snow depth determined by the CBT and three snow depth retrieval algorithms are validated through field measurements taken in forest and farmland areas of Northeast China in January 2012 and 2013. The results show that the overall of the retrieval precision of the snow depth is improved by 17% in farmland areas and 10% in forest areas when using the CBT in comparison with the mixed pixels. The snow cover results based on the CBT are compared with existing MODIS snow cover products. The results demonstrate that more snow cover information can be obtained with up to 86% accuracy.

Citation Download Citation

Lingjia Gu, Ruizhi Ren, Kai Zhao, and Xiaofeng Li "Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China," Journal of Applied Remote Sensing 8(1), 084682 (3 November 2014). https://doi.org/10.1117/1.JRS.8.084682

Published: 3 November 2014

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