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Journal of Applied Remote Sensing

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(s): Jianhui Xu; Feifei Zhang; Yi Zhao; Hong Shu; Kaiwen Zhong
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Paper 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

Paper Details

Date Published: 4 July 2016
PDF: 21 pages
J. Appl. Rem. Sens. 10(3) 036001 doi: 10.1117/1.JRS.10.036001
Published in: Journal of Applied Remote Sensing Volume 10, Issue 3
Show Author Affiliations
Jianhui Xu, Guangzhou Institute of Geography (China)
Feifei Zhang, Guangdong Univ. of Education (China)
Yi Zhao, Guangzhou Institute of Geography (China)
Guangzhou Institute of Geochemistry (China)
Hong Shu, Wuhan Univ. (China)
Kaiwen Zhong, Guangzhou Institute of Geography (China)

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