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A Global Analysis of Soil Moisture Derived from Satellite Observations and a Land Surface Model : Volume 16, Issue 3 (16/03/2012)

By Rebel, K. T.

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Book Id: WPLBN0004010672
Format Type: PDF Article :
File Size: Pages 15
Reproduction Date: 2015

Title: A Global Analysis of Soil Moisture Derived from Satellite Observations and a Land Surface Model : Volume 16, Issue 3 (16/03/2012)  
Author: Rebel, K. T.
Volume: Vol. 16, Issue 3
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Viovy, N., Kiely, G., Ciais, P., Rebel, K. T., Piao, S. L., M. De Je, R. A., & Dolman, A. J. (2012). A Global Analysis of Soil Moisture Derived from Satellite Observations and a Land Surface Model : Volume 16, Issue 3 (16/03/2012). Retrieved from

Description: Department of Environmental Sciences, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands. Soil moisture availability is important in regulating photosynthesis and controlling land surface-climate feedbacks at both the local and global scale. Recently, global remote-sensing datasets for soil moisture have become available. In this paper we assess the possibility of using remotely sensed soil moisture – AMSR-E (LPRM) – to similate soil moisture dynamics of the process-based vegetation model ORCHIDEE by evaluating the correspondence between these two products using both correlation and autocorrelation analyses. We find that the soil moisture product of AMSR-E (LPRM) and the simulated soil moisture in ORCHIDEE correlate well in space and time, in particular when considering the root zone soil moisture of ORCHIDEE. However, the root zone soil moisture in ORCHIDEE has on average a higher temporal autocorrelation relative to AMSR-E (LPRM) and in situ measurements. This may be due to the different vertical depth of the two products – AMSR-E (LPRM) at the 2–5 cm surface depth and ORCHIDEE at the root zone (max. 2 m) depth – to uncertainty in precipitation forcing in ORCHIDEE, and to the fact that the structure of ORCHIDEE consists of a single-layer deep soil, which does not allow simulation of the proper cascade of time scales that characterize soil drying after each rain event. We conclude that assimilating soil moisture, using AMSR-E (LPRM) in a land surface model like ORCHIDEE with an improved hydrological model of more than one soil layer, may significantly improve the soil moisture dynamics, which could lead to improved CO2 and energy flux predictions.

A global analysis of soil moisture derived from satellite observations and a land surface model

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