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

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. That is the model predicted on average cooler LST's (∼ 1.7 K) and wetter SMC values (∼ 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, g C , and latent heat flux, λE , from the MODIS ET product were in good agreement with RMSEs for g C  = 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.

[1]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

[2]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[3]  R.A.M. de Jeu,et al.  Retrieval of Land Surface Parameters using Passive Microwave Remote Sensing , 2003 .

[4]  Atlas of Australian resources , 1962 .

[5]  J. J. Mott,et al.  Ecology and management of the world's savannas , 1985 .

[6]  Mark A. Friedl,et al.  Forward and inverse modeling of land surface energy balance using surface temperature measurements , 2002 .

[7]  P. Jarvis.  The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field , 1976 .

[8]  Mark A. Friedl,et al.  Modeling land surface fluxes using a sparse canopy model and radiometric surface temperature measurements , 1995 .

[9]  S. Running,et al.  Regional evaporation estimates from flux tower and MODIS satellite data , 2007 .

[10]  Shaun Quegan,et al.  Model–data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications , 2005 .

[11]  Jeffrey P. Walker,et al.  A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index , 2001, IEEE Trans. Geosci. Remote. Sens..

[12]  R. Monson,et al.  Model‐data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado , 2006 .

[13]  B. Choudhury,et al.  Effect of surface roughness on the microwave emission from soils , 1979 .

[14]  James Llinas,et al.  An introduction to multi-sensor data fusion , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[15]  S. Running,et al.  Application of multiple constraints model-data assimilation techniques to coupling satellite passive microwave and thermal imagery for estimation of land surface variables and energy fluxes , 2006 .

[16]  Wade T. Crow,et al.  An observation system simulation experiment for the impact of land surface heterogeneity on AMSR-E soil moisture retrieval , 2001, IEEE Trans. Geosci. Remote. Sens..

[17]  Derek Eamus,et al.  LEAF PHENOLOGY OF WOODY SPECIES IN A NORTH AUSTRALIAN TROPICAL SAVANNA , 1997 .

[18]  F. Aires,et al.  Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements , 2005 .

[19]  Ernst Linder,et al.  Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .

[20]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[21]  E. Njoku,et al.  Vegetation and surface roughness effects on AMSR-E land observations , 2006 .

[22]  W. Crow,et al.  The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97 , 2003 .

[23]  Damian Barrett,et al.  Steady state turnover time of carbon in the Australian terrestrial biosphere , 2002 .

[24]  Thomas J. Jackson,et al.  Assimilation of surface soil moisture to estimate profile soil water content , 2003 .

[25]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[26]  D. Barrett,et al.  Prospects for improving savanna biophysical models by using multiple-constraints model-data assimilation methods , 2005 .

[27]  R. Giering,et al.  Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS) , 2005 .

[28]  Wade T. Crow,et al.  A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Randal D. Koster,et al.  Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model , 2005 .

[30]  Eleanor J. Burke,et al.  Using a modeling approach to predict soil hydraulic properties from passive microwave measurements , 1998, IEEE Trans. Geosci. Remote. Sens..

[31]  Ramakrishna R. Nemani,et al.  Multi-objective parameter estimation for simulating canopy transpiration in forested watersheds , 2002 .

[32]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[33]  Jeremy Russell-Smith,et al.  Viewpoint: Assessing the carbon sequestration potential of mesic savannas in the Northern Territory, Australia: approaches, uncertainties and potential impacts of fire. , 2004, Functional plant biology : FPB.

[34]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[35]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[36]  L. Hutley,et al.  Savanna fires and their impact on net ecosystem productivity in North Australia , 2004 .

[37]  A. Hollingsworth,et al.  Some aspects of the improvement in skill of numerical weather prediction , 2002 .

[38]  T. Mo,et al.  A model for microwave emission from vegetation‐covered fields , 1982 .

[39]  William L. Barnes,et al.  Next-Generation MODIS for Polar Operational Environmental Satellites , 1998 .

[40]  Gwangseob Kim,et al.  Space-time characterization of soil moisture from passive microwave remotely sensed imagery and ancillary data , 2002 .

[41]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[42]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[43]  Lindsay B. Hutley,et al.  Evapotranspiration from eucalypt open-forest savanna of northern australia , 2000 .

[44]  Rolf Reichle,et al.  Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications , 2001, IEEE Trans. Geosci. Remote. Sens..

[45]  Charles A. Laymon,et al.  Assimilating remote sensing data in a surface flux–soil moisture model , 2002 .

[46]  Wade T. Crow,et al.  Relevance of time‐varying and time‐invariant retrieval error sources on the utility of spaceborne soil moisture products , 2005 .

[47]  J. Norman,et al.  Remote sensing of surface energy fluxes at 101‐m pixel resolutions , 2003 .

[48]  Robert J. Abrahart,et al.  Multi-model data fusion for hydrological forecasting , 2001 .

[49]  Richard Harding,et al.  A canopy conductance and photosynthesis model for use in a GCM land surface scheme , 1998 .