Remote Sensing Applications and Decision Support

Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations

[+] Author Affiliations
Jianhui Xu, Kaiwen Zhong

Guangzhou Institute of Geography, 100 Xianlie Zhong Road, Guangzhou 510070, China

Guangdong Open Laboratory of Geospatial Information Technology and Application, 100 Xianlie Zhong Road, Guangzhou 510070, China

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, 100 Xianlie Zhong Road, Guangzhou 510070, China

Feifei Zhang

Guangdong University of Education, Department of Computer Science, 351 Xingang Zhong Road, Guangzhou 510310, China

Yi Zhao

Guangzhou Institute of Geography, 100 Xianlie Zhong Road, Guangzhou 510070, China

Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, 511 Kehua Street, Guangzhou 510640, China

Hong Shu

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, 129 Luoyu Road, Wuhan 430079, China

Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China

J. Appl. Remote Sens. 10(3), 036001 (Jul 04, 2016). doi:10.1117/1.JRS.10.036001
History: Received February 17, 2016; Accepted June 14, 2016
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Abstract.  For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with in situ SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Jianhui Xu ; Feifei Zhang ; Yi Zhao ; Hong Shu and Kaiwen Zhong
"Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations", J. Appl. Remote Sens. 10(3), 036001 (Jul 04, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036001


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