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References

  • Allen, R. G. (1986), A Penman for all seasons, J. Irrig. Drain. Eng., 112(4), 348368.
  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith (1998), Crop Evapotranspiration, Guidelines for Computing Crop Water Requirements, FAO, Rome.
  • Anderson, M. C., C. Hain, B. Wardlow, A. Pimstein, J. R. Mecikalski, and W. P. Kustas (2011), Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States, J. Clim., 24, 20252044.
  • Barros, F. V. F., E. S. P. R. Martins, L. S. V. Nascimento, and D. S. Reis Jr (2010), Use of multiobjective evolutionary algorithms in water resources engineering, in Multi-Objective Swarm Intelligent Systems, Theory & Experiences, Studies in Computational Intelligence, vol. 261, edited by N. Nedjah, L. dos Santos Coelho, and L. de Macedo de Mourelle, chap. 3, pp. 4582, Springer, Berlin.
  • Bashir, M. A., H. Tanakamaru, and A. Tada (2009), Spatial and temporal analysis of evapotranspiration using satellite remote sensing data: A case study in the Gezira Scheme, Sudan, J. Environ. Inf., 13(2), 8692.
  • Bastidas, L. A., H. V. Gupta, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang (1999), Sensitivity analysis of a land surface scheme using multicriteria methods, J. Geophys. Res., 104(D16), 19,48119,490.
  • Belmans, C., J. G. Wesseling, and R. A. Feddes (1983), Simulation model of the water balance of a cropped soil: SWATRE, J. Hydrol., 63(3–4), 271286.
  • Bindlish, R., T. J. Jackson, A. J. Gasiewski, M. Klein, and E. G. Njoku (2006), Soil moisture mapping and AMSR-E validation using the PSR in SMEX02, Remote Sens. Environ., 103(2), 127139.
  • Black, T. A., W. R. Gardner, and G. W. Thurtell (1969), The prediction of evaporation, drainage, and soil water storage for a bare soil, Soil Sci. Soc. Am. Proc., 33, 655660.
  • Boufadel, M. C., M. T. Suidan, A. D. Venosa, C. H. Rauch, and P. Biswas (1998), 2d variably saturated flows: Physical scaling and Bayesian estimation, J. Hydrol. Eng., 3(4), 223231.
  • Braden, H. (1985), Ein Energiehaushalts- und Verdun-stungsmodell für Wasser- und Stoffhaushaltsuntersuchungen landwirtschaftlich genutzter Einzugsgebiete, Mitt. Dtsch. Bodenkdl. Ges., 42, 294299.
  • Brutsaert, W. (2005), Hydrology: An Introduction, 605 pp., Cambridge Univ. Press, Cambridge, U. K.
  • Capehart, W. J., and T. N. Carlson (1997), Decoupling of surface and near-surface soil water content: A remote sensing perspective, Water Resour. Res., 33(6), 13831395.
  • Carsel, R. F., and R. S. Parrish (1988), Developing joint probability distributions of soil-water retention characteristics, Water Resour. Res., 24(5), 755769.
  • Choi, M., J. M. Jacobs, and D. D. Bosch (2008), Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S., Water Resour. Res., 44, W08421, doi:10.1029/2006WR005578.
  • Coudert, B., C. Ottle, B. Boudevillain, J. Demarty, and P. Guillevic (2006), Contribution of thermal infrared remote sensing data in multiobjective calibration of a dual-source SVAT model, J. Hydrometeorol., 7(3), 404420.
  • Crispim, J. A., and J. P. de Sousa (2009), Partner selection in virtual enterprises: A multi-criteria decision support approach, Int. J. Prod. Res., 47(17), 47914812.
  • Das, N. N., B. P. Mohanty, M. H. Cosh, and T. J. Jackson (2008), Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04, Remote Sens. Environ., 112(2), 415429.
  • Davenport, I. J., J. Fernandez-Galvez, and R. J. Gurney (2005), A sensitivity analysis of soil moisture retrieval from the Tau-Omega microwave emission model, IEEE Trans. Geosci. Remote Sens. 43(6), 13041316.
  • De Lannoy, G. J. M., P. R. Houser, V. R. N. Pauwels, and N. E. C. Verhoest (2007), State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency, Water Resour. Res., 43, W0640l, doi:10.1029/2006WR005100.
  • Deardorff, J. W. (1978), Efficient prediction of ground surface-temperature and moisture, with inclusion of a layer of vegetation, J. Geophys. Res., 83(C4), 18891903.
  • Deb, K., and H. Gupta (2005), Searching for robust Pareto-optimal solutions in multi-objective optimization, in Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, vol. 3410, pp. 150164, Springer, Berlin.
  • Demarty, J., C. Ottle, I. Braud, A. Olioso, J. P. Frangi, L. A. Bastidas, and H. V. Gupta (2004), Using a multiobjective approach to retrieve information on surface properties used in a SVAT model, J. Hydrol., 287(1–4), 214236.
  • Demarty, J., C. Ottle, I. Braud, A. Olioso, J. P. Frangi, H. V. Gupta, and L. A. Bastidas (2005), Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach, Water Resour. Res., 41, W01011, doi:10.1029/2004WR003695.
  • Duan, Q. Y., S. Sorooshian, and V. Gupta (1992), Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 10151031.
  • Duan, Q. Y., S. Sorooshian, and V. K. Gupta (1994), Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158(3–4), 265284.
  • Dumedah, G., A. A. Berg, M. Wineberg, and R. Collier (2010), Selecting model parameter sets from a trade-off surface generated from the non-dominated sorting genetic algorithm-II, Water Resour. Manage., 24(15), 44694489.
  • Dumedah, G., A. A. Berg, and M. Wineberg (2012a), Pareto-optimality and a search for robustness: Choosing solutions with desired properties in objective space and parameter space, J. Hydroinf., 14(2), 270285.
  • Dumedah, G., A. Berg, and M. Wineberg (2012b), Evaluating auto-selection methods used for choosing solutions from Pareto-optimal set: Does non-dominance persist from calibration to validation phase?, J. Hydrol. Eng., 17(1), 150159.
  • Eagleson, P. S. (1978), Climate, soil and vegetation: 1. Introduction to water balance dynamics, Water Resour. Res., 14(5), 705712.
  • Efstratiadis, A., and D. Koutsoyiannis (2010), One decade of multiobjective calibration approaches in hydrological modelling: A review, Hydrol. Sci. J., 55(1), 5878.
  • Entekhabi, D., et al. (2010), The soil moisture active passive (SMAP) mission, Proc. IEEE, 98(5), 704716.
  • Feddes, R. A., P. J. Kowalik, and H. Zaradny (1978), Simulation of Field Water Use and Crop Yield, Simulation Monographs, 188 pp., John Wiley, Australia.
  • Feddes, R. A., M. Menenti, P. Kabat, and W. G. M. Bastiaanssen (1993), Is large-scale inverse modelling of unsaturated flow with areal average evaporation and surface soil moisture as estimated from remote sensing feasible?, J. Hydrol., 143(1–2), 125152.
  • Fernández-Gálvez, J. (2008), Errors in soil moisture content estimates induced by uncertainties in the effective soil dielectric constant, Int. J. Remote Sens., 29(11), 33173323.
  • Franks, S. W., K. J. Beven, and J. H. C. Gash (1999), Multi-objective conditioning of a simple SVAT model, Hydrol. Earth Syst. Sci., 3(4), 477489.
  • Gale, M. R., and D. F. Grigal (1987), Vertical root distributions of northern tree species in relation to successional status, Can. J. Forest Res., 17(8), 829834.
  • Gao, Y. C., and D. Long (2008), Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT, Hydrol. Processes, 22(25), 48504869.
  • Goudriaan, J. (1977), Crop Micrometeorology: A Simulation Study, Simulation monographs, Pudoc, Wageningen.
  • Gupta, H. V., S. Sorooshian, and P. O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34(4), 751763.
  • Gupta, H. V., L. A. Bastidas, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang (1999), Parameter estimation of a land surface scheme using multicriteria methods, J. Geophys. Res., 104(D16), 19,49119,503.
  • Gutmann, E. D., and E. E. Small (2010), A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land-surface models, Water Resour. Res., 46, W06520, doi:10.1029/2009WR008203.
  • Haverkamp, R., F. J. Leij, C. Fuentes, A. Sciortino, and P. J. Ross (2005), Soil water retention: I. Introduction of a shape index, Soil Sci. Soc. Am. J., 69(6), 18811890.
  • Hong, S. H., J. M. H. Hendrickx, and B. Borchers (2009), Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale, J. Hydrol., 370(1–4), 122138.
  • Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira (2002), Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83(1–2), 195213.
  • Ines, A. V. M., and P. Droogers (2002), Inverse modelling in estimating soil hydraulic functions: A Genetic Algorithm approach, Hydrol. Earth Syst. Sci., 6(1), 4965.
  • Ines, A. V. M., and B. P. Mohanty (2008a), Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties under different hydroclimatic conditions, Vadose Zone J., 7(1), 3952.
  • Ines, A. V. M., and B. P. Mohanty (2008b), Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling, Water Resour. Res., 44, W06422, doi:10.1029/2007WR005990.
  • Ines, A. V. M., and B. P. Mohanty (2008c), Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space, Water Resour. Res., 44, W08441, doi:10.1029/2007WR006125.
  • Ines, A. V. M., and B. P. Mohanty (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.
  • Jackson, T. J., and T. J. Schmugge (1991), Vegetation effects on the microwave emission of soils, Remote Sens. Environ., 36(3), 203212.
  • Jackson, R. B., J. Canadell, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze (1996), A global analysis of root distributions for terrestrial biomes, Oecologia, 108(3), 389411.
  • Jhorar, R. K., W. G. M. Bastiaanssen, R. A. Feddes, and J. C. Van Dam (2002), Inversely estimating soil hydraulic functions using evapotranspiration fluxes, J. Hydrol., 258(1–4), 198213.
  • Jhorar, R. K., J. C. van Dam, W. G. M. Bastiaanssen, and R. A. Feddes (2004), Calibration of effective soil hydraulic parameters of heterogeneous soil profiles, J. Hydrol., 285(1–4), 233247.
  • Kalma, J. D., T. R. McVicar, and M. F. McCabe (2008), Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surv. Geophys., 29(4–5), 421469.
  • Kerr, Y. H., P. Waldteufel, J. P. Wigneron, J. M. Martinuzzi, J. Font, and M. Berger (2001), Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Trans. Geosci. Remote Sens., 39(8), 17291735.
  • Khu, S. T., and H. Madsen (2005), Multiobjective calibration with Pareto preference ordering: An application to rainfall-runoff model calibration, Water Resour. Res., 41, W03004, doi:10.1029/2004WR003041.
  • Kroes, J. G., J. G. Wesseling, and J. C. Van Dam (2000), Integrated modelling of the soil-water-atmosphere-plant system using the model SWAP 2.0 an overview of theory and an application, Hydrol. Processes, 14(11–12), 19932002.
  • Laumanns, M., L. Thiele, K. Deb, and E. Zitzler (2002), Combining convergence and diversity in evolutionary multiobjective optimization, Evol. Comput., 10(3), 263282.
  • Leij, F. J., W. J. Alves, M. T. Van Genuchten, and J. R. Williams (1996), The UNSODA Unsaturated Soil Hydraulic Database, User's Manual, Version 1.0, EPA/600/R-96/095, 103 pp., Natl. Risk Manage. Lab., Off. of Res. and Dev., U.S. Environ. Prot. Agency (EPA), Cincinnati, OH.
  • Li, F., W. P. Kustas, M. C. Anderson, T. J. Jackson, R. Bindlish, and J. H. Prueger (2006), Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape, Remote Sens. Environ., 101(3), 315328.
  • Luckner, L., M. T. Van Genuchten, and D. R. Nielsen (1989), A consistent set of parametric models for the two-phase flow of immiscible fluids in the subsurface, Water Resour. Res., 25(10), 21872193.
  • Madsen, H. (2003), Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives, Adv. Water Resour., 26(2), 205216.
  • Milly, P. C. D. (1986), An event-based simulation model of moisture and energy fluxes at a bare soil surface, Water Resour. Res., 22(12), 16801692.
  • Mo, X. G., S. X. Liu, Z. Lin, X. Sun, and Z. Zhu (2006), Multi-objective conditioning of a SVAT model for heat and CO2 fluxes prediction, in Prediction in Ungauged Basins: Promises and Progress (Proceedings of symposium S7 held during the Seventh IAHS Scientific), edited by M. Sivapalan, et al., pp. 164176, IAHS Publ., Assembly at Foz do Iguaçu, Brazil.
  • Mohanty, B. P., and J. Zhu (2007), Effective hydraulic parameters in horizontally and vertically heterogeneous soils for steady-state land-atmosphere interaction, J. Hydrometeorol., 8(4), 715729.
  • Mroczkowski, M., G. P. Raper, and G. Kuczera (1997), The quest for more powerful validation of conceptual catchment models, Water Resour. Res., 33(10), 23252335.
  • Mualem, Y. (1976), A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12(3), 513522.
  • Naeimi, V., K. Scipal, Z. Bartalis, S. Hasenauer, and W. Wagner (2009), An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations, IEEE Trans. Geosci. Remote Sens., 47(7), 19992013.
  • Nagler, P. L., J. Cleverly, E. Glenn, D. Lampkin, A. Huete, and Z. M. Wan (2005), Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data, Remote Sens. Environ., 94(1), 1730.
  • Njoku, E. G., and S. K. Chan (2006), Vegetation and surface roughness effects on AMSR-E land observations, Remote Sens. Environ., 100(2), 190199.
  • Noilhan, J., and P. Lacarrere (1995), GCM grid-scale evaporation from mesoscale modeling, J. Clim., 8(2), 206223.
  • Opoku-Duah, S., D. N. M. Donoghue, and T. P. Burt (2008), Intercomparison of evapotranspiration over the Savannah Volta Basin in West Africa using remote sensing data, Sensors, 8(4), 27362761.
  • Pollacco, J. A. P. (2005), Inverse methods to determine parameters in a physically-based model of soil water balance, 190 pp., Univ. of Newcastle upon Tyne, Newcastle upon Tyne, U. K.
  • Pollacco, J. A. P., and B. P. Mohanty (2012), Uncertainties of water fluxes in soil-vegetation-atmosphere transfer models: Inverting surface soil moisture and evapotranspiration retrieved from remote sensing, Vadose Zone J., 11(3), doi:10.2136/vzj2011.0167.
  • Pollacco, J. A. P., I. Braud, R. Angulo-Jaramillo, and B. Saugier (2008a), A Linking Test that establishes if groundwater recharge can be determined by optimising vegetation parameters against soil moisture, Ann. Forest Sci., 65(7), 702, doi:10.1051/forest:2008046.
  • Pollacco, J. A. P., J. M. S. Ugalde, R. Angulo-Jaramillo, I. Braud, and B. Saugier (2008b), A linking test to reduce the number of hydraulic parameters necessary to simulate groundwater recharge in unsaturated soils, Adv. Water Resour., 31(2), 355369.
  • Ramos, J. G., C. R. Cratchley, J. A. Kay, M. A. Casterad, A. Martinez-Cob, and R. Dominguez (2009), Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain, Agric. Water Manage., 96(4), 638652.
  • Reed, P., B. S. Minsker, and D. E. Goldberg (2003), Simplifying multiobjective optimization: An automated design methodology for the Nondominated Sorted Genetic Algorithm-II, Water Resour. Res., 39(7), 1196, doi:10.1029/2002WR001483.
  • Refsgaard, J. C., and B. Storm (1996), Construction, calibration and validation of hydrological models, in Distributed Hydrological Modelling, edited by M. B. Abbott and J. C. Refsgaard, pp. 4154, Kluwer Academic Press: The Netherlands.
  • Ritchie, J. T. (1972), Model for predicting evaporation from a row crop with incomplete cover, Water Resour. Res., 8(5), 12041213.
  • Romano, E., and M. Giudici (2007), Experimental and modeling study of the soil-atmosphere interaction and unsaturated water flow to estimate the recharge of a phreatic aquifer, J. Hydrol. Eng., 12(6), 573584.
  • Romano, E., and M. Giudici (2009), On the use of meteorological data to assess the evaporation from a bare soil, J. Hydrol., 372(1–4), 3040.
  • Russo, D. (1988), Determining soil hydraulic properties by parameter estimation: On the selection of a model for the hydraulic properties, Water Resour. Res., 24(3), 453459.
  • Rutter, A. J., K. A. Kershaw, P. C. Robins, and A. J. Morton (1971), A predictive model of rainfall interception in forests, 1. Derivation of the model from observations in a plantation of Corsican pine, Agric. Meteorol., 9(C), 367384.
  • Sahoo, A. K., P. R. Houser, C. Ferguson, E. F. Wood, P. A. Dirmeyer, and M. Kafatos (2008), Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia, Remote Sens. Environ., 112(6), 31423152.
  • Schaap, M. G., and F. J. Leij (1998), Using neural networks to predict soil water retention and soil hydraulic conductivity, Soil Tillage Res., 47(1–2), 3742.
  • Schenk, H. J., and R. B. Jackson (2002), The global biogeography of roots, Ecol. Monogr., 72(3), 311328.
  • Shin, Y., B. P. Mohanty, and A. V. M. Ines (2012), Soil hydraulic properties in one-dimensional layered soil profile using layer-specific soil moisture assimilation scheme, Water Resour. Res., 48, W06529, doi:10.1029/2010WR009581.
  • Simic, A., R. Fernandes, R. Brown, P. Romanov, and W. Park (2004), Validation of VEGETATION, MODIS, and GOES + SSM/I snow-cover products over Canada based on surface snow depth observations, Hydrol. Processes, 18(6), 10891104.
  • Simmons, C. S., and P. D. Meyer (2000), A simplified model for the transient water budget of a shallow unsaturated zone, Water Resour. Res., 36(10), 28352844.
  • Simmonds, L. P., et al. (2004), Soil moisture retrieval by a future space-borne Earth observation mission, Eur. Space Agency Contract Rep., Univ. of Reading, Reading, U. K.
  • Singh, R., J. G. Kroes, J. C. van Dam, and R. A. Feddes (2006), Distributed ecohydrological modelling to evaluate the performance of irrigation system in Sirsa district, India: I. Current water management and its productivity, J. Hydrol., 329(3–4), 692713.
  • Sun, R., J. Shi, and L. Jiang (2007), A method to retrieve soil moisture using ERS Scatterometer data, Geoscience and Remote Sensing Symposium (IGARSS 2007), IEEE Int., 18571860.
  • Taboada, H., and D. Coit (2006), Data mining techniques to facilitate the analysis of the Pareto-optimal set for multiple objective problems, paper presented at Industrial Engineering Research Conference (IERC), Orlando, Fla.
  • Teixeira, A. H. d. C., W. G. M. Bastiaanssen, M. D. Ahmad, and M. G. Bos (2009a), Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Sao Francisco River basin, Brazil. Part B: Application to the regional scale, Agric. Forest Meteorol., 149(3–4), 477490.
  • Teixeira, A. H. d. C., W. G. M. Bastiaanssen, M. D. Ahmad, and M. G. Bos (2009b), Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Säo Francisco River basin, Brazil. Part A: Calibration and validation, Agric. Forest Meteorol., 149(3–4), 462476.
  • Tietje, O., and M. Tapkenhinrichs (1993), Evaluation of pedo-transfer functions, Soil Sci. Soc. Am. J., 57(4), 10881095.
  • Twarakavi, N. K. C., H. Saito, J. Simunek, and M. T. van Genuchten (2008), A new approach to estimate soil hydraulic parameters using only soil water retention data, Soil Sci. Soc. Am. J., 72(2), 471479.
  • Valante, F., J. S. David, and J. H. C. Gash (1997), Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models, J. Hydrol., 190(1–2), 141162.
  • Van Dam, J. C. (2000), Field-scale water flow and solute transport, SWAP model concepts, parameter estimation, and case studies. PhD thesis, 167 pp., Wageningen University, Wageningen, The Netherlands.
  • Van Dam, J. C., J. Huygen, J. G. Wesseling, R. A. Feddes, P. Kabat, P. E. V. Van Walsum, P. Groenendijk, and C. A. Van Diepen (1997), Theory of SWAP Version 2.0. Simulation of Water Flow, Solute Transport and Plant Growth in the Soil-Water-Atmosphere-Plant Environment, Tech. Doc. 45, DLO Winand Staring Cent., Wageningen, Netherlands.
  • Van Dam, J. C., P. Groenendijk, R. F. A. Hendriks, and J. G. Kroes (2008), Advances of modeling water flow in variably saturated soils with SWAP, Vadose Zone J., 7(2), 640653.
  • van Genuchten, M. T. (1980), Closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44(5), 892898.
  • van Griensven, A., and T. Meixner (2006), Methods to quantify and identify the sources of uncertainty for river basin water quality models, Water Sci. Technol., 53(1), 5159.
  • Varado, N., I. Braud, and P. J. Ross (2006), Development and assessment of an efficient vadose zone module solving the 1D Richards' equation and including root extraction by plants, J. Hydrol., 323(1–4), 258275.
  • Vegas Galdos, F., C. Álvarez, A. García, and J. A. Revilla (2012), Estimated distributed rainfall interception using a simple conceptual model and Moderate Resolution Imaging Spectroradiometer (MODIS), J. Hydrol., 468–469, 213228.
  • Verstraeten, W. W., F. Veroustraete, and J. Feyen (2008), Assessment of evapotranspiration and soil moisture content across different scales of observation, Sensors, 8(1), 70117.
  • Vischel, T., G. G. S. Pegram, S. Sinclair, W. Wagner, and A. Bartsch (2008), Comparison of soil moisture fields estimated by catchment modelling and remote sensing: A case study in South Africa, Hydrol. Earth Syst. Sci., 12(3), 751767.
  • Von Hoyningen-Huene, J. (1981), Die Interzeption des Niederschlags in Landwirtschaftlichen Pflanzenbeständen, Arbeitsbericht Deutscher verband fur Wasserwirtschaft und Kulturbau, Braunschwig, Germany.
  • Vrugt, J. A., W. Bouten, H. V. Gupta, and S. Sorooshian (2002), Toward improved identifiability of hydrologic model parameters: The information content of experimental data, Water Resour. Res., 38(12), 48-1-48-13.
  • Walker, J. P., G. R. Willgoose, and J. D. Kalma (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(12), 118.
  • Wang, C., P. Wang, X. Zhu, W. Zheng, and H. Yang (2008), Estimations of evapotranspiration and surface soil moisture based on remote sensing data and influence factors, Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng., 24(10), 127133.
  • Wang, T., V. A. Zlotnik, J. Simunek, and M. G. Schaap (2009), Using pedotransfer functions in vadose zone models for estimating groundwater recharge in semiarid regions, Water Resour. Res., 45(4), W04412, doi:10.1029/2008WR006903.
  • Wilson, D. J., A. W. Western, R. B. Grayson, A. A. Berg, M. S. Lear, M. Rodell, J. S. Famiglietti, R. A. Woods, and T. A. McMahon (2003), Spatial distribution of soil moisture over 6 and 30 cm depth, Mahurangi river catchment, New Zealand, J. Hydrol., 276(1–4), 254274.
  • Wu, B. F., J. Xiong, N. N. Yan, L. D. Yang, and X. Du (2008), ETWatch for monitoring regional evapotranspiration with remote sensing, Shuikexue Jinzhan/Adv. Water Sci., 19(5), 671678.
  • Yapo, P. O., H. V. Gupta, and S. Sorooshian (1998), Multi-objective global optimization for hydrologic models, J. Hydrol., 204(1–4), 8397.
  • Zhan, X. W., W. T. Crow, T. J. Jackson, and P. E. O'Neill (2008), Improving spaceborne radiometer soil moisture retrievals with alternative aggregation rules for ancillary parameters in highly heterogeneous vegetated areas, IEEE Geosci. Remote Sens. Lett., 5(2), 261265.
  • Zhang, X., S. Kang, P. Wang, and L. Tong (2006), Comparative analysis of regional evapotranspiration estimation models using remotely sensed data, Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng., 22(7), 613.