Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters

Renzullo, Luijg J., Barrett, Damian J., Marks, lan S., Hill, Michael J., Guerschman, Juan P., Mu, Qiaozhen and Running, Steve W. (2008) Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters. Remote Sensing of Environment, 112 4: 1306-1319. doi:10.1016/j.rse.2007.06.022

Author Renzullo, Luijg J.
Barrett, Damian J.
Marks, lan S.
Hill, Michael J.
Guerschman, Juan P.
Mu, Qiaozhen
Running, Steve W.
Title Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters
Journal name Remote Sensing of Environment   Check publisher's open access policy
ISSN 0034-4257
Publication date 2008-04-15
Year available 2007
Sub-type Article (original research)
DOI 10.1016/j.rse.2007.06.022
Volume 112
Issue 4
Start page 1306
End page 1319
Total pages 14
Place of publication New York, NY, United States
Publisher Elsevier
Language eng
Subject 090905 Photogrammetry and Remote Sensing
Formatted abstract
Model-data fusion offers considerable promise in remote sensing for improved state and parameter estimation particularly when applied to multi-sensor image products. This paper demonstrates the application of a 'multiple constraints' model-data fusion (MCMDF) scheme to integrating AMSR-E soil moisture content (SMC) and MODIS land surface temperature (LST) data products with a coupled biophysical model of surface moisture and energy budgets for savannas of northern Australia. The focus in this paper is on the methods, difficulties and error sources encountered in developing an MCMDF scheme and enhancements for future schemes.

An important aspect of the MCMDF approach emphasized here is the identification of inconsistencies between model and data, and among data sets. The MCMDF scheme was able to identify that an inconsistency existed between AMSR-E SMC and LST data when combined with the coupled SEB-MRT model. For the example presented, an optimal fit to both remote sensing data sets together resulted in an 84% increase in predicted SMC and 0.06% increase for LST relative to the fit to each data set separately. Thai: is the model predicted on average cooler LST's (similar to 1.7 K) and wetter SMC values (similar to 0.04 g cm-3) than the satellite image products. In this instance we found that the AMSR-E SMC data on their own were poor constraints on the model. Incorporating LST data via the MCMDF scheme ameliorated deficiencies in the SMC data and resulted in enhanced characterization of the land surface soil moisture and energy balance based on comparison with the MODIS evapotranspiration (ET) product of Mu et al. [Mu, Q., Heinsch, F.A, Zhao, M. and Running, S.W. (in press), Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment.]. Canopy conductance, gc, and latent heat flux, λE, from the MODI S ET product were in good agreement with RMSEs for gC=0.5 mm s-1 and for λE=18 W m-2, respectively. Differences were attributable to a greater canopy-to-air vapor pressure gradient in the MCMDF approach obtained from a more realistic partitioning of soil surface and canopy temperatures.
Keyword Multiple constraints model-data fusion
Biophysical modeling
Soil moisture content
Land surface temperature
Canopy conductance
Energy flux
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Sustainable Minerals Institute Publications
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Created: Thu, 12 Nov 2009, 19:43:25 EST by Thelma Whitbourne on behalf of Sustainable Minerals Institute