Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 3 2009 10.5194/hess-13-343-2009 http://www.hydrol-earth-syst-sci.net/13/343/2009/ http://www.hydrol-earth-syst-sci.net/13/343/2009/hess-13-343-2009.html http://www.hydrol-earth-syst-sci.net/13/343/2009/hess-13-343-2009.pdf 343 356 2009-03-13 Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling F. Mattia F.Mattia@ba.issia.cnr.it G. Satalino V. R. N. Pauwels A. Loew Consiglio Nazionale delle Ricerche, Istituto di Studi sui Sistemi Intelligenti per l'Automazione (ISSIA), Bari, Italy Ghent University, Laboratory of Hydrology and Water Management (LHWM), Ghent, Belgium Max-Planck-Institute for Meteorology, The Land in the Earth System, Hamburg, Germany The objective of the study is to investigate the potential of retrieving superficial soil moisture content (<i>m_v</i>) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km²). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km²) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km²). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior <i>m_v</i> estimates is better than 5%. Alvarez–Perez, J.: An extension of the IEM/IEMM surface scattering model, Waves in Random Media, 11, 307–329, 2001. Bach, H., and Mauser, W.: Methods and examples of for remote sensing data assimilation in land surface process modelling, IEEE T. Geosci. Remote Sens., 41(7), 1629–1637, 2003. Baldocchi, D. D., Hicks, B. B., and Camara P.: A canopy stomatal resistance model for gaseous depositions to vegetated surfaces, Atmos. Environ., 21(1), 91–101, 1987. Bastiaanssen, W. G. M., Noordman, E. J. M., Pelgrum, J., Davids, G., Thoreson, B. P., and Allen, R. G.: SEBAL model with remotely sensed data to improve water–resources management under actual field conditions, J. Irr. Drain. Eng., 131(1), 85–93, 2005. Betts, A. K., Ball, J. H., Beljaars, A. C. M., Miller, M. J., and Viterbo, P. A.: The land surface–atmosphere interaction, a review based on observational and global modelling perspectives, J. Geophys. Res., 101(D3), 7209–7225, 1996. Brooks, R. H., and Corey, A. T.: Hydraulic properties of porous media, Hydrology paper 3, Colorado State University, Fort Collins, Colorado, USA, 1964. Chen, K. S. Wu, T. D., Tsay, M. K., and Fung A. K.: A note on the multiple scattering in IEM model, IEEE T. Geosci. Remote Sens., 38(1), 249–256, 2000. Chen, K. S. Wu, T. D., Tsang, L., Li, Q., Shi, J. C., and Fung A. K.: Emission of Rough Surfaces Calculated by the Integral Equation Method with Comparison to three–dimensional Moment Method simulations, IEEE T. Geosci. Remote Sens., 41(1), 90–101, 2003. Chodhury, B. J., Venkatratnam, L., Rao, P. V. K., and Ramana, K.: Relation between soil moisture and normalized difference vegetation Index of vegetated fields, Int. J. Remote Sens., 14(4), 444–449, 1993. Cookmartin, G., Saich, P., Quegan, S., Cordey, R., Burgess-Allen, R., and Sowter, A.,: Modeling microwave interactions with crops and comparison with ERS-2 SAR observations, IEEE T. Geosci. Remote Sens., 38, 658–670, 2000. Crow, W. T.: A novel method for quantifying value in spaceborne soil moisture, J. Hydrol., 8, 56–57, 2007a. Crow, W. and Zhan X.: Continental scale evaluation of remotely sensed soil moisture products, IEEE Geosci. Remote Sens. Lett., 4(3), 451–455, 2007. Della Vecchia, A., Ferrazzoli, P., Guerriero, L., Blaes, X., Defourny, P., Dente, L., Mattia, F., Satalino, G., Strozzi, T., Wegmuller, U.:Influence of geometrical factors on crop backscattering at Cband, IEEE T. Geosci. Remote Sens., 44(4), 778–790, 2006. Davidson, M. W. J., Mattia, F., Satalino, G., Verhoest, N. E. C., Le Toan, T., Borgeaud, M., Louis, J. M. B., and Attema, E.: Joint statistical properties of RMS height and correlation length derived from multisite 1-m roughness measurements, IEEE Transactions on Geosc. and Rem. Sensing, 41(7), 1651–1658, 2003. Descroix, L. and Nouvelot, J. F., and Vauclin, M.: Evaluation of an antecedent precipitation index to model runoff yield in the western Sierra Madre (North-west Mexico), J. Hydrol., 263, 114–130, 2002. Du, Y., Ulaby, F. T., and Dobson, M. C.: Sensitivity to soil moisture by active and passive microwavesensors, IEEE T. Geosci. Remote Sens., 38(1), 105–114, 2002. Eagleson, P.: Climate, soil, and vegetation, simplified model of soil–moisture movement in liquid–phase, WRR, 14(5), 722–730, 1978. Famiglietti, J. S., Wood, E. F.: Multiscale modeling of spatially variable water and energy balance processes, Water Res. Research, 30 (11), 3061–3078, 1994. Fung, A. K., Li, Z., and Chen, K. S.: Backscattering from a Randomly Rough Dielectric Surface, IEEE T. Geosci. Remote Sens., 30(2), 356–369, 1992. Hajnsek, I., Bianchi, R., Davidson, M., D'Urso, G., Gomez-Sanchez, J. A., Hausold, A., Horn, R., Howse, J., Loew, A., Lopez-Sanchez, J. M., Ludwig, R., Martinez-Lozano, J. A., Mattia, F., Miguel, E., Moreno, J., Pauwels, V. R. N., Ruhtz, T., Schmullius, C., Skriver, H., Sobrino, J. A., Timmermans, W., Wloczyk, C., and Wooding, M.: AGRISAR Optical and Radar Campaign, Final Report, Tech. Rep. ESA Contract 19974/06/I/LG, European Space Agency, 2007. Hallikainen, M. T., Ulaby, F. T., Dobson, M. C., El-Rayes, M. A., and Wu, L. K.: Microwave dielectric behavior of wet soil. Part I: Empirical models and experimental observations, IEEE T. Geosci. Remote Sens., 23(1), 25–34, 1985. Hong, S. and Kainay, E.: Role of sea surface temperature and soil–moisture feedback in the 1998 Oklahoma-Texas drought, Nature, 408(6814), 842–844, 2000. Hsieh, C. Y., Fung, A. K., Nesti, G., Siber A. J., and Coppo, P.: A further study of the IEM surface scattering model, IEEE T. Geosci. Remote Sens., 35(4), 901–909, 1997. Jackson, T. J., Le Vine, D. M., Griffis, A. J., Goodrich, D. C., Schmugge, T. J., Swift, C. T., and O'Neill, P. E.: Soil moisture and rainfall estimation over a semiarid environment with the ESTAR microwave radiometer, IEEE T. Geosci. Remote Sens., 31(4), 836–841, 1993. Jackson, T. J., McNairn, H., Weltz, M. A., Brisco, B., and Brown, R.,: First order surface roughness correction of active microwave observations for estimating soil moisture, IEEE T. Geosci. Remote Sens. vol. 35, 1065–1069, 1997. Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Martinuzzi, J., Font, J., Berger, M.: Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote Sens., 39(8), 1729–1735, 2001. Lasdon L. S., Waren, A. D., Jain, A., and Ratner M.: Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming, ACM Trans. on Mathematical Software, 4(1), 34–50, 1978. Liszka, E. G. and McCoy J. J.: Scattering at a rough boundary: Extensions of the Kirchhoff approximation, J. Acoust. Soc. Am., 71(5), 1093–1100, 1982. Loaiza Usuga, J. C. and Pauwels, V. R. N.: Calibration and multiple data set–based validation of a land surface model in a mountainous Mediterranean study area, J. Hydrol., 356(1–2), 223–233, 2008. Loew, A.: Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: the Upper Danube case, Remote Sens. Environ., 112, 231–248, 2008. Loew, A., Bach, H., and Mauser, W.: 5 years of ENVISAT ASAR soil moisture observations in Southern German, Proc. ENVISAT Symposium 23–27.04.2007, Montreux, Switzerland. ESA-SP636, 2007. Loew, A., Ludwig, R., and Mauser, W.: Derivation of surface soil moisture from ENVISAT ASAR WideSwath and Image mode data in agricultural areas, IEEE T. Geosci. Remote Sens., 44(4), 889–899, 2006. Ludwig, R., and Mauser, W.: Modelling catchment hydrology within a GIS–based SVAT model framework, Hydrol. Earth Syst. Sci., 4(2), 239–249, 2000. Marliani, F., Paloscia, S., Pampaloni, P., and Kong, J. A.,: Simulating coherent backscattering from crops during the growing season, IEEE T. Geosci. Remote Sens. 40, 162–177, 2002. Mattia, F., Le Toan, T., Souyris, J.-C., De Carolis, C., Floury, N., Posa, F., and Pasquariello, N. G.,: The effect of surface roughness on multifrequency polarimetric SAR data, IEEE T. Geosci. Remote Sens., 35, 954–966, 1997 Mattia, F., Le Toan, T., Picard, G., Posa, F., D'Alessio, A., Notarnicola, C., Gatti, A. M., Rinaldi, M., Satalino, G., and Pasquariello, G.: Multitemporal C-band radar measurements on wheat fields, IEEE T. Geosci. Remote Sens., 41(7), 1551–1560, 2003a. Mattia, F., Davidson, M. W. J., Le Toan, T., D'Haese, C. M. F., Verhoest, N. E. C., Gatti, A. M., and Borgeaud, M.: A Comparison Between Soil Roughness Statistics Used in Surface Scattering Models Derived From Mechanical and Laser Profilers, IEEE T. Geosci. Remote Sens., 41(7), 1659–1671, 2003b. Mattia, F., Satalino, G., Dente, L., and Pasquariello G.: Using \emphA Priori Information to Improve Soil Moisture Retrieval From ENVISAT ASAR AP Data in Semiarid Regions, IEEE T. Geosci. Remote Sens., 44(4), 900–912, 2006. Mattia, F., Satalino, G., Balenzano, A., Pauwels, V. R. N., Verhoest, N. E. C., Skriver, H., and Davidson M.: Exploiting L-band SAR data for the improvement of surface process modelling, Proceedings of the 5th Int. Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications, Bari, Italy, 25–28 September, 2007. Mauser, W. and Schädlich, S.: Modelling the spatial distribution of evapotranspiration using remote sensing data and PROMET, J. Hydrol., 213, 250–267, 1998. Mauser, W. and Bach, H.: PROMET <96> a Physical Hydrological Model to Study the Impact of Climate Change on the Water Flows of Medium Sized Mountain Watersheds, J. Hydrol., submitted paper, 2008. Monteith, J. L.: Evaporation and the environment, Proc. Symposium of the society of exploratory biology, 19, 205–234, 1965. Moran, M. S., Peters–Lidard, C. D., Watts, J. M., and McElroy, S.: Estimating soil moisture at the watershed scale with satellite–based radar and land surface models, Canad. J. Remote Sens., 2004. Muerth, M.: A Multiscale Energy Balance and Soil Temperature Model to Assess Water Related Climate Change Impacts, University of Munich, PhD thesis, unpublished, 2008. Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.: Soil moisture retrieval from AMSR–E, IEEE T. Geosci. Remote Sens., 41(2), 215–229, 2003. Pauwels, V. R. N., Hoeben, R., Verhoest, N. E. C., De Troch, F. P., and Troch, P. A.: Improvement of TOPLATS–based discharge predictions through assimilation of ERS–based remotely sensed soil moisture values, Hydrol. Proc., 16(5), 995–1013, 2002. Pauwels, V. R. N., Balenzano, A., Satalino, G., Skriver, H., Verhoest, N. E. C., and Mattia F.: Optimization of soil hydraulic model parameters using Synthetic Aperture Radar data: an integrated multidisciplinary approach, accepted for publication on IEEE T. Geosci. Remote Sens., 47(2), 455–467, 2009. Pauwels, V. R. N., Timmermans, W., and Loew, A.: Comparison of the estimated water and energy budgets of a large winter wheat field during AgriSAR 2006 by multiple sensors and models, J. Hydrol., 349(3–4), 425-440, 2008. Pauwels, V. R. N. and Wood, E. F.: A soil–vegetation–atmosphere transfer scheme for the modeling of water and energy balance processes in high latitudes. 1. Model improvements, J. Geophys. Res., 104 (D22), 27, 811–822, 1999. Peters-Lidard, C. D., Zion, M. S., and Wood, E. F.: A soil-vegetation-atmosphere transfer scheme for modeling spatially variable water and energy balance processes, J. Geophys. Res., 102(D4), 4303–4324, 1997. Philip, J. R.: The theory of infiltration : 1. The infiltration equation and its solution, Soil Sci., 83, 345–357, 1957. Picard, G., Le Toan, T., and Mattia, F.: Understanding C-band radar backscatter from wheat canopy using a multiple-scattering coherent model, IEEE T. Geosci. Remote Sens., 41(7), 1583–1591, 2003. Rose, S.: A statistical method for evaluating the effects of antecedent rainfall upon runoff: applications to the coastal plain of Georgia, J. Hydrol., 211, 168–177, 1998. Satalino G., Mattia, F., Davidson, M., Le Toan, T., Pasquariello, G., and Borgeaud, M.: On Current limits of Soil Moisture Retrieval from ERS–SAR data, IEEE T. Geosci. Remote Sens., 40(11), 2438–2447, 2002. Schneider, K.: Assimilating remote sensing data into a land–surface process model, Int. J. Remote Sens., 23(14), 2959–2980, 2003. Scheiber, R., Keller, M., Fischer, J., Horn, R., and Hajnsek, I.: Radar Data Processing Quality 5 Analysis and Level–1b Product Generation for AGRISAR and EAGLE campaigns, Proceedings AGRISAR and EAGLE Campaigns Final Workshop, Noordwijk, The Netherlands, CD, 15–16 October, 2007. Shi, J., Jiang, L., Zhang, L., Chen, K. S., Wigneron, J. P., Chanzy, A., and Jackson, T. J.: Physically Based Estimation of Bare–Surface Soil Moisture With the Passive Radiometers, IEEE T. Geosci. Remote Sens., 44(11), 3145–3153, 2006. Sittner, W. T., Schauss, C. E., and Monro, J. C.: Continuous hydrograph synthesis with an API–type hydrologic model, Water Resour. Res., 5(5), 1007–1022, 1969. Sivapalan, M., Beven, K., and Wood, E. F.: On hydrologic similarity. 2. A scaled model for runoff prediction, Water Resour. Res., 23 (12), 2266–2278, 1987. Strasser, U. and Mauser, W., Modelling the spatial and temporal variations of the water balance for the Weser catchment 1965–1994, J. Hydrol., 254(1–4), 199–214, 2001. Touré, A., Thompson, K. P. B., Edwards, G., Brown, R. J., and Brisco, B. G.: Adaptation of MIMICS backscattering model to the agricultural context – wheat and canola at L and C bands, IEEE T. Geosci. Remote Sens., 32, 47–61, 1994. Ulaby, F. T., Aslam, A., and Dobson M. C.: Effects of vegetation cover on the radar sensitivity to soil moisture, IEEE T. Geosci. Remote Sens., 20, 476–481, 1982. Vries De, J. J. and Hromadka, T. V.: Handbook of Hydrology, McGraw–Hill, 1993. Wagner, W., Scipal, K., Pathe, C., Gerten, D., Lucht, W., and Rudolf, B.: Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data, J. Geophys. Res., 108, 4611, 2003.