Editorial Type:
Article
Article Type:
Research Article

An Integrated Framework for a Joint Assimilation of Brightness Temperature and Soil Moisture Using the Nondominated Sorting Genetic Algorithm II

Gift Dumedah Department of Geography, University of Guelph, Guelph, Canada

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Aaron A. Berg Department of Geography, University of Guelph, Guelph, Canada

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Mark Wineberg Department of Computing and Information Science, University of Guelph, Guelph, Canada

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Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/JHM-D-10-05029.1
Page(s):
1596–1609
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Abstract

This study has applied the Nondominated Sorting Genetic Algorithm II (NSGA-II) in a two-step assimilation procedure to jointly assimilate brightness temperature into a radiative transfer model and soil moisture into a land surface model. The first assimilation procedure generates a time series of soil moisture by assimilating brightness temperature from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) into the Land Parameter Retrieval Model (LPRM). The second procedure generates assimilated soil moisture by assimilating the soil moisture from LPRM into the Canadian Land Surface Scheme (CLASS). Note that the assimilated soil moisture was generated by merging two soil moisture estimates: one from LPRM and the other from the CLASS simulation. The assimilated soil moisture is better than using the soil moisture determined either from the satellite observation or the land surface scheme alone. This method provides improved model state and parameterizations for both LPRM and CLASS with the aim to facilitate real-time forecasts when satellite information becomes available. Application of this framework to the Brightwater Creek watershed in southern Saskatchewan illustrates the utility of the joint assimilation framework to improve a time series of soil moisture estimates. The estimated soil moisture datasets were evaluated over an agricultural site in southern Saskatchewan using in situ monitoring networks. These results demonstrate that soil moisture generated from assimilation of brightness temperature could be improved by incorporating it into a land surface model. A comparison between the assimilated soil moisture and in situ dataset demonstrates an improvement in accuracy and temporal pattern that is accomplished through the assimilation framework.

Corresponding author address: Aaron Berg, Department of Geography, University of Guelph, Guelph ON N1G 2W1, Canada. E-mail: aberg@uoguelph.ca

Abstract

This study has applied the Nondominated Sorting Genetic Algorithm II (NSGA-II) in a two-step assimilation procedure to jointly assimilate brightness temperature into a radiative transfer model and soil moisture into a land surface model. The first assimilation procedure generates a time series of soil moisture by assimilating brightness temperature from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) into the Land Parameter Retrieval Model (LPRM). The second procedure generates assimilated soil moisture by assimilating the soil moisture from LPRM into the Canadian Land Surface Scheme (CLASS). Note that the assimilated soil moisture was generated by merging two soil moisture estimates: one from LPRM and the other from the CLASS simulation. The assimilated soil moisture is better than using the soil moisture determined either from the satellite observation or the land surface scheme alone. This method provides improved model state and parameterizations for both LPRM and CLASS with the aim to facilitate real-time forecasts when satellite information becomes available. Application of this framework to the Brightwater Creek watershed in southern Saskatchewan illustrates the utility of the joint assimilation framework to improve a time series of soil moisture estimates. The estimated soil moisture datasets were evaluated over an agricultural site in southern Saskatchewan using in situ monitoring networks. These results demonstrate that soil moisture generated from assimilation of brightness temperature could be improved by incorporating it into a land surface model. A comparison between the assimilated soil moisture and in situ dataset demonstrates an improvement in accuracy and temporal pattern that is accomplished through the assimilation framework.

Corresponding author address: Aaron Berg, Department of Geography, University of Guelph, Guelph ON N1G 2W1, Canada. E-mail: aberg@uoguelph.ca
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  • Alavi, N., Berg A. , Warland J. , Parkin G. , Verseghy D. , and Bartlett P. , 2010: Assimilating soil moisture variability into the class to improve latent heat flux estimation. Can. Water Resour. J., 35, 1–16.

    • Search Google Scholar
    • Export Citation

  • Aubert, D., Loumagne C. , and Oudin L. , 2003: Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model. J. Hydrol., 280, 145–161.

    • Search Google Scholar
    • Export Citation

  • Berg, A. A., and Mulroy K. , 2006: Streamflow predictability given macro-scale estimates of the initial soil moisture status. Hydrol. Sci. J., 51, 642–654.

    • Search Google Scholar
    • Export Citation

  • Burgers, T., Van Leeuwen P. J. , and Evensen G. , 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719–1724.

  • Caparrini, F., Castelli F. , and Entekhabi D. , 2003: Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data. Bound.-Layer Meteor., 107, 605–633.

    • Search Google Scholar
    • Export Citation

  • Caparrini, F., Castelli F. , and Entekhabi D. , 2004a: Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences. J. Hydrometeor., 5, 145–159.

    • Search Google Scholar
    • Export Citation

  • Caparrini, F., Castelli F. , and Entekhabi D. , 2004b: Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resour. Res., 40, W12515, doi:10.1029/2004WR003358.

    • Search Google Scholar
    • Export Citation

  • Champagne, C., Berg A. , Belanger J. , McNairn H. , and deJeu R. , 2010: Evaluation of soil moisture derived from passive microwave remote sensing over agricultural sites in Canada using ground-based soil moisture monitoring networks. Int. J. Remote Sens., 31, 3669–3690.

    • Search Google Scholar
    • Export Citation

  • Chemin, Y., and Honda K. , 2006: Spatiotemporal fusion of rice actual evapotranspiration with genetic algorithms and an agrohydrological model. IEEE Trans. Geosci. Remote Sens., 44, 3462–3469.

    • Search Google Scholar
    • Export Citation

  • Confesor, R. B., and Whittaker G. W. , 2007: Automatic calibration of hydrologic models with multi-objective evolutionary algorithm and Pareto optimization. J. Amer. Water Resour. Assoc., 43, 981–989.

    • Search Google Scholar
    • Export Citation

  • Crosson, W. L., Laymon C. A. , Inguva R. , and Schamschula M. P. , 2002: Assimilating remote sensing data in a surface flux-soil moisture model. Hydrol. Processes, 16, 1645–1662.

    • Search Google Scholar
    • Export Citation

  • Crow, W. T., and Wood E. F. , 2003: 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. Adv. Water Resour., 26, 137–149.

    • Search Google Scholar
    • Export Citation

  • Deb, K., and Goel T. , 2001: Controlled elitist non-dominated sorting genetic algorithms for better convergence. Evolutionary Multi-Criterion Optimization, E. Zitzler et al., Eds., Lecture Notes in Computer Science, Vol. 1993, Springer, 67–81.

    • Search Google Scholar
    • Export Citation

  • Deb, K., Agrawal S. , Pratap A. , and Meyarivan T. , 2000: A fast elitist non-dominated sorting genetic algorithms for multi-objective optimization: NSGA-II. Parallel Problem Solving from Nature—PPSN VI, M. Schoenauer et al., Eds., Lecture Notes in Computer Science, Vol. 1917, Springer, 849–858.

    • Search Google Scholar
    • Export Citation

  • Deb, K., Pratap A. , Agrawal S. , and Meyarivan T. , 2002: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 6, 182–197.

    • Search Google Scholar
    • Export Citation

  • Dumedah, G., Berg A. A. , Wineberg M. , and Collier R. , 2010: Selecting model parameter sets from a trade-off surface generated from the non-dominated sorting genetic algorithm-II. Water Resour. Manage., 24, 4469–4489, doi: 10.1007/s11269-010-9668-y.

    • Search Google Scholar
    • Export Citation

  • Dumedah, G., Berg A. A. , and Wineberg M. , 2011: Evaluating auto-selection methods used for choosing solutions from Pareto-optimal set: Does non-dominance persist from calibration to validation phase? J. Hydrol. Eng., in press.

    • Search Google Scholar
    • Export Citation

  • Dunne, S., and Entekhabi D. , 2005: An ensemble-based reanalysis approach to land data assimilation. Water Resour. Res., 41, W02013, doi:10.1029/2004WR003449.

    • Search Google Scholar
    • Export Citation

  • Eiben, A. E., and Smith J. E. , 2003: Introduction to Evolutionary Computing. Springer, 299 pp.

  • Galantowicz, J. F., Entekhabi D. , and Njoku E. , 1999: Tests of sequential data assimilation for retrieving profile soil moisture and temperature from observed l-band radiobrightness. IEEE Trans. Geosci. Remote Sens., 37, 1860–1870.

    • Search Google Scholar
    • Export Citation

  • Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 1414–1430.

    • Search Google Scholar
    • Export Citation

  • Holmes, T. R. H., De Jeu R. A. M. , Owe M. , and Dolman A. J. , 2009: Land surface temperature from Ka band (37 GHz) passive microwave observations. J. Geophys. Res., 114, D04113, doi:10.1029/2008JD010257.

    • Search Google Scholar
    • Export Citation

  • Houser, P. R., Shuttleworth W. J. , Famiglietti J. S. , Gupta H. V. , Syed K. H. , and Goodrich D. C. , 1998: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour. Res., 34, 3405–3420.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., and Honda K. , 2005: On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment. Adv. Water Resour., 28, 856–870.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., and Mohanty B. P. , 2008: Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties under different hydroclimatic conditions. Vadose Zone J., 7, 39–52.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., and Mohanty B. P. , 2009: Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02. Water Resour. Res., 45, W01408, doi:10.1029/2008WR007022.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., Honda K. , Das Gupta A. , Droogers P. , and Clemente R. S. , 2006: Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture. Agric. Water Manage., 83, 221–232.

    • Search Google Scholar
    • Export Citation

  • Khu, S. T., and Madsen H. , 2005: Multiobjective calibration with Pareto preference ordering: An application to rainfall-runoff model calibration. Water Resour. Res., 41, W03004, doi:10.1029/2004WR003041.

    • Search Google Scholar
    • Export Citation

  • Liu, Y., and Gupta H. V. , 2007: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resour. Res., 43, W07401, doi:10.1029/2006WR005756.

    • Search Google Scholar
    • Export Citation

  • Madsen, H., 2003: Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv. Water Resour., 26, 205–216.

    • Search Google Scholar
    • Export Citation

  • Moradkhani, H., Hsu K.-L. , Gupta H. , and Sorooshian S. , 2005: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resour. Res., 41, W05012, doi:10.1029/2004WR003604.

    • Search Google Scholar
    • Export Citation

  • Njoku, E. G., and Li L. , 1999: Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Trans. Geosci. Remote Sens., 37, 79–93.

    • Search Google Scholar
    • Export Citation

  • Njoku, E. G., Jackson T. J. , Lakshmi V. , Chan T. K. , and Nghiem S. V. , 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 215–229.

    • Search Google Scholar
    • Export Citation

  • NSIDC, 2008: AMSR-E/Aqua daily L3 surface soil moisture, interpretive parameters, and QC EASE-grids v002. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/docs/daac/ae_land3_l3_soil_moisture.gd.html.]

    • Search Google Scholar
    • Export Citation

  • Owe, M., de Jeu R. , and Walker J. , 2001: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans. Geosci. Remote Sens., 39, 1643–1654.

    • Search Google Scholar
    • Export Citation

  • Owe, M., de Jeu R. , and Holmes T. , 2008: Multisensor historical climatology of satellite-derived global land surface moisture. J. Geophys. Res., 113, F01002, doi:10.1029/2007JF000769.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., 2008: Data assimilation methods in the Earth sciences. Adv. Water Resour., 31, 1411–1418.

  • Reichle, R. H., and Koster R. D. , 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys. Res. Lett., 32, L02404, doi:10.1029/2004GL021700.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Entekhabi D. , and McLaughlin D. B. , 2001: Downscaling of radio brightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach. Water Resour. Res., 37, 2353–2364.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., McLaughlin D. B. , and Entekhabi D. , 2002a: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130, 103–114.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Walker J. P. , Koster R. D. , and Houser P. R. , 2002b: Extended versus ensemble Kalman filtering for land data assimilation. J. Hydrometeor., 3, 728–740.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Koster R. D. , Dong J. , and Berg A. A. , 2004: Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation. J. Hydrometeor., 5, 430–442.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Koster R. D. , Liu P. , Mahanama S. P. P. , Njoku E. G. , and Owe M. , 2007: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Crow W. T. , Koster R. D. , Sharif H. O. , and Mahanama S. P. P. , 2008: Contribution of soil moisture retrievals to land data assimilation products. Geophys. Res. Lett., 35, L01404, doi:10.1029/2007GL031986.

    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., Rayner N. A. , Smith T. M. , Stokes D. C. , and Wang W. , 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625.

    • Search Google Scholar
    • Export Citation

  • Schubert, S. D., Rood R. B. , and Pfaendtner J. , 1993: An assimilated dataset for earth science applications. Bull. Amer. Meteor. Soc., 74, 2331–2342.

    • Search Google Scholar
    • Export Citation

  • Schuurmans, J. M., Troch P. A. , Veldhuizen A. A. , Bastiaanssen W. G. M. , and Bierkens M. F. P. , 2003: Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv. Water Resour., 26, 151–159.

    • Search Google Scholar
    • Export Citation

  • Tang, Y., Reed P. , and Wagener T. , 2006: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol. Earth Syst. Sci., 10, 289–307.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., and Guillemot C. J. , 1998: Evaluation of the atmospheric moisture and hydrological cycle in the NCEP/NCAR reanalyses. Climate Dyn., 14, 213–231.

    • Search Google Scholar
    • Export Citation

  • Troch, P., Paniconi C. , and McLaughlin D. , 2003: Catchment-scale hydrological modeling and data assimilation. Adv. Water Resour., 26, 131–135.

    • Search Google Scholar
    • Export Citation

  • Verseghy, D. L., 1991: CLASS—A Canadian land surface scheme for GCMs: I. Soil model. Int. J. Climatol., 11, 111–133.

  • Verseghy, D. L., 2000: The Canadian Land Surface Scheme (CLASS): Its history and future. Atmos.–Ocean, 38, 1–13.

  • Verseghy, D. L., McFarlane N. A. , and Lazare M. , 1993: CLASS—A Canadian land surface scheme for GCMs: II. Vegetation model and coupled runs. Int. J. Climatol., 13, 347–370.

    • Search Google Scholar
    • Export Citation

  • Vrugt, J. A., Gupta H. V. , Bastidas L. A. , Bouten W. , and Sorooshian S. , 2003: Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res., 39, 1214, doi:10.1029/2002WR001746.

    • Search Google Scholar
    • Export Citation

  • Walker, J. P., and Houser P. R. , 2004: Requirements of a global near-surface soil moisture satellite mission: Accuracy, repeat time, and spatial resolution. Adv. Water Resour., 27, 785–801.

    • Search Google Scholar
    • Export Citation

  • Walker, J. P., Willgoose G. R. , and Kalma J. D. , 2002: Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application. Water Resour. Res., 38, 1301, doi:10.1029/2002WR001545.

    • Search Google Scholar
    • Export Citation

  • Wöhling, T., Vrugt J. A. , and Barkle G. F. , 2008: Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties. Soil Sci. Soc. Amer. J., 72, 305–319.

    • Search Google Scholar
    • Export Citation
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