Special Section on Progress in Snow Remote Sensing

Spatiotemporal analysis of snow depth inversion based on the FengYun-3B MicroWave Radiation Imager: a case study in Heilongjiang Province, China

[+] Author Affiliations
Xiaofeng Li

Chinese Academy of Sciences, Northeast Institute of Geography and Agroecology, Changchun, China

Chinese Academy of Sciences, Changchun Jingyuetan Remote Sensing Experiment Station, Changchun, China

Kai Zhao

Chinese Academy of Sciences, Northeast Institute of Geography and Agroecology, Changchun, China

Chinese Academy of Sciences, Changchun Jingyuetan Remote Sensing Experiment Station, Changchun, China

Lili Wu

Chinese Academy of Sciences, Northeast Institute of Geography and Agroecology, Changchun, China

University of Chinese Academy of Sciences, Beijing 100049, China

Xingming Zheng

Chinese Academy of Sciences, Northeast Institute of Geography and Agroecology, Changchun, China

Chinese Academy of Sciences, Changchun Jingyuetan Remote Sensing Experiment Station, Changchun, China

Tao Jiang

Chinese Academy of Sciences, Northeast Institute of Geography and Agroecology, Changchun, China

Chinese Academy of Sciences, Changchun Jingyuetan Remote Sensing Experiment Station, Changchun, China

J. Appl. Remote Sens. 8(1), 084692 (May 19, 2014). doi:10.1117/1.JRS.8.084692
History: Received November 25, 2013; Revised April 16, 2014; Accepted April 24, 2014
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Abstract.  To improve snow depth (SD) inversion algorithms using passive microwave data, it is important to objectively assess their accuracy and to analyze their uncertainty. Some previous studies validated the inversion algorithms only using spatial data at a fixed time node, which is not objective or convincing. The spatiotemporal analysis of the SD inversion based on the FengYun-3B MicroWave Radiation Imager is performed in Heilongjiang Province, China. Based on the temporal analysis, the results show that the accuracy of SD inversion algorithms is different at different time phases throughout the winter. In cropland areas, the variation in snow properties, particularly the increase in snow grain and the presence of depth hoar, leads to underestimation and overestimation at the earlier and later phases, respectively. The spatial analysis shows that the SD in the high forest coverage regions is seriously overestimated due to the addition of a forest correction factor using the Chang algorithm. In addition, the complex underlying surfaces and hilly terrain are also influencing factors that result in the low accuracy for several regions. Therefore, the analysis and identification of these uncertainties are benefits not only in understanding the influential factors of SD inversion algorithms but also in developing better algorithms for the next generation of SD retrieval.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Xiaofeng Li ; Kai Zhao ; Lili Wu ; Xingming Zheng and Tao Jiang
"Spatiotemporal analysis of snow depth inversion based on the FengYun-3B MicroWave Radiation Imager: a case study in Heilongjiang Province, China", J. Appl. Remote Sens. 8(1), 084692 (May 19, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.084692


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