Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 14 8 2010 10.5194/hess-14-1509-2010 http://www.hydrol-earth-syst-sci.net/14/1509/2010/ http://www.hydrol-earth-syst-sci.net/14/1509/2010/hess-14-1509-2010.html http://www.hydrol-earth-syst-sci.net/14/1509/2010/hess-14-1509-2010.pdf 1509 1525 2010-08-10 Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station S. Juglea silvia.juglea@cesbio.cnes.fr Y. Kerr A. Mialon E. Lopez-Baeza D. Braithwaite K. Hsu Centre d'Études Spatiales de la BIOsphère, UMR5126 (CNRS, CNES, IRD, UPS), Toulouse, France Universitat de Valencia, Departament de Termodinamica i Fisica de la Terra, Valencia, Spain Center for Hydrometeorology and Remote Sensing (CHRS), Civil and Environmental Engineering, UC IRvine, Irvine CA 92697-2175, USA In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS1database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km2). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km2) requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66%) but the precipitation occurrences are in general retrieved (about 67%). To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT) model – ISBA (Interactions between Soil Biosphere Atmosphere) is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E). We show that the PERSIANN database provides useful information at temporal and spatial scales in the context of soil moisture retrieval. 1Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System – <a href="http://chrs.web.uci.edu/persiann"target="_blank">http://chrs.web.uci.edu/persiann</a> Boone, A.: Modelisation des processus hydrologiques dans le schema de surface ISBA: Inclusion d'un reservoir hydrologique, du gel et modelisation de la neige., PhD thesis, University Paul Sabatier, Toulouse, France, 252~pp., 2000. Boone, A., Masson, V., Meyers, T., and Noilhan, J.: The influence of the inclusion of soil freezing on simulations by a soil-vegetation-atmosphere transfer scheme., J. Appl. Meteor., 39, 1544–1569, 2000. Brown, J. E M.: An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions, Rem. Sens. Environ., 101, 63–81, 2006. Brutsaert, W H.: On a derivable formula for long-wave radiation from clear skies, Water Resour. Res., 11(2), 742–744, 1975. Cano, A., Millan-Scheiding, C., Wigneron, J.-P., Antolin, C., Balling, J., Grant, J., Kruszewski, A., Saleh, K., Sobjaerg, S., Skou, N., and Lopez-Baeza, E.: The Mediterranean Ecosystem L-Band EXperiment over vineyards (Melbex-2)., 10th Specialist Meeting on Microwave Radiometry and Remote Sensing for the Environment, Italy, 2008. Collischonn, B., Collischonn, W., and Morelli Tucci, C.E.: Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates, J. Hydrol., 360(1–4), 207–216, 2008. Delwart, S., Bouzinac, C., and Wursteisen, P.: Overall SMOS Cal/Val Plan and Requirements, SMOS 7th Workshop, Esrin, 2007. Grimes, D. and Diop, M.: Satellite-based rainfall estimation for river flow forecasting in Africa. Part 1. Rainfall estimates and hydrological forecasts., Hydrol. Sci. J., 48(4), 567–584, 2003. Gruhier, C., de Rosnay, P., Hasenauer, S., Holmes, T., de Jeu, R., Kerr, Y., Mougin, E., Njoku, E., Timouk, F., Wagner, W., and Zribi, M.: Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site, Hydrol. Earth Syst. Sci., 14, 141–156, doi:10.5194/hess-14-141-2010, 2010. Gottschalck, J., Meng, J., Rodell, M., and Houser, P.: Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System Land Surface States, J. Hydrometeorol., 6, 573–598, doi:10.1175/JHM437.1, 2005. Grayson, R. B., Western, A. W., Walker, J. P., Kandel, D. D., Costelloe, J. F. and Wilson, D. J.: Controls on patterns of soil moisture in arid and semi-arid systems, Chapter 7, Ecohydrology of Arid and Semi-Arid Ecosystems. Eds. P. D�Ordorico and A. Porporato, Springer, The Netherlands, 341~p., 2006. Hong, Y., Hsu, K., Gao, X., and Sorooshian, S.: Precipitation Estimation from Remotely Sensed Information using An Artificial Neural~~Network-Cloud~~Classification~~System.,~~J ~Appl ~Met., \noindent 43, 1834–1852, 2004. Hsu, K., Gao, X., Sorooshian, S., and Gupta, H V.: Precipitation estimation from remotely sensed information using artificial neural networks., J. Appl. Meteor., 36, 1176–1190, 1997. Hsu, K., Gupta, H., Gao, X., and Sorooshian, S.: Estimation of physical variables from multi-channel remotely sensed imagery using a neural network: application to rainfall estimation., Water Resour. Res., 35(5), 1605–1618, 1999. Hughes, D., Andersson, L.,~Wilk, J., and Savenije, H.: Regional calibration of the Pitman model for the Okavango River, J. Hydrol., 331(1–2), 30–42, 2006. Hughes, D A.: Comparison of satellite rainfall data with observations from gauging station networks., J. Hydrol., 327, 399–410, 2006. Jackson, T J., LeVine, D., Swift, C., Schmugge, T J., and Schiebe, F.: \protectLarge area mapping of soil moisture using the ESTAR passive microwave radiometer., Rem. Sens. Environ., 54(1), 27–37, 1995. Jackson, T J., Le~Vine, D M., Hsu, A., Oldack, A., Starks, P., Swift, C., Isham, J., and Haken, M.: Soil moisture Mapping at regional scales using microwave radiometry: The southern great plains hydrology experiment, IEEE Trans. Geosc. Remote Sens., 37(5), 2136–2151, 1999. Juglea, S., Kerr, Y., Mialon, A., Wigneron, J.-P., Lopez-Baeza, E., Cano, A., Albitar, A., Millan-Scheiding, C., Carmen Antolin, M., and Delwart, S.: Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station, Hydrol. Earth Syst. Sci., 14, 831–846, doi:10.5194/hess-14-831-2010, 2010. Kerr, Y H. and Njoku, E G.: A semi empirical model for interpreting microwave emission from semiarid surfaces as seeen from space, IEEE Trans. Geosc. Remote Sens., 28, 384–393, 1990. Kerr, Y H., Waldteufel, P., Wigneron, J.-P., Martinuzzi, J.-M., Font, J., and Berger, M.: Soil moisture retrieval from Space: The soil moisture and ocean salinity (SMOS) mission, IEEE Trans. Geosc. Remote Sens., 39(8), 1729–1735, 2001. Le~Moigne, P., Boone, A., Calvet, J.-C., Decharme, B., Faroux, S., Gibelin, A.-L., Lebeaupin, C., Mahfouf, J.-F., Martin, E., Masson, V., Mironov, D., Noilhan, J., Tulet, P., and Van Den~Hurk, B.: SURFEX scientific documentation, 2009. Lopez-Baeza, E., Antolin~Tomas, C., Bodas~Salcedo, A., Gimeno~Ferrer, J F., Saleh~Contell, K., Ferrer, F., Castell~Balaguer, N., Domenech~Garcia, C., Sanchez~Alandi, M A., and Velazquez~Blazquez, A.: The Valencia Anchor Station: A Reference Cal/Val Area for Low-Resolution Remote Sensing Data and Products., Recent Advances in Quantitative Remote Sensing, Torrente (Valencia), poster, 2002. Lopez-Baeza, E., Alonso, S., Comeron, A., Diaz-Pabon, R., Domenech, C., Gimeno-Ferrer, J., Jorge, J., Labajo, A., Pineda, N., Pino, D., Rius, A., Rocadenbosch, F., Saleh, K., Sicard, M., Tarruella, R., Torrobella, J., and Velazquez, A.: A High-Quality Dataset of Land-Surface and Atmospheric Measurements for the Comparison/Crosscalibration of Data From Large Scale Optical Earth Observation Sensors in Space. The Valencia Anchor Station., Proceedings of the Workshop on Inter-Comparison of Large Scale Optical and Infrared Sensors, ESA/ESTEC 12–14 October 2004, ESA-WPP-244, 2005a. Lopez-Baeza, E., Domenech, C., Gimeno-Ferrer, J., and Velazquez, A.: Proposal of a Water Cycle Observatory: The Reference Valencia and Alacant Anchor Stations for Remote Sensing Data and Products., XI Spanish Remote Sensing Congress, Puerto de la Cruz, Tenerife, 2005b. Lopez-Baeza, E., SVRC, and team: Validation of SMOS Products over Mediterranean Ecosystem Vegetation at the Valencia Anchor Station Reference Area, Experimental Plan SMOS Validation Rehearsal Campaign, SMOS Cal/Val AO I.D, 2008. Masson, V., Champeaux, J.-L., Chauvin, F., Meriguet, C., and Lacaze, R.: A global database of land surface parameters at 1-km resolution in meteorological and climate models, J. Climate, 16(9), 1261–1282, 2003. Millan-Scheiding, C., Marco, J., Soriano, M., Torre, E., Torregrosa, G., Abalos, B., Requena, F., Cano, A., Antolin, C., and Lopez-Baeza, E.: VAS Soil and Vegetation Characterization, SMOS meeting in Bordeaux, INRA, 30–31 October 2008. Ming, P., Li, H., and Wood, E. F.: Assessing the Skill of Satellite-based Precipitation Estimates in Hydrologic Applications, Water Resour. Res., in press, 2010. Njoku, E G.: AMSR-E/AQUA daily L3 surface soil moisture, interpretive parms, & QC EASE-Grids, Boulder, CO, USA: National Snow and Ice Data Center, Digital Media, 2004. Njoku, E G., Jackson, T., Lakshmi, V., Chan, T., and Nghiem, S.: Soil moisture retrieval from AMSR-E, IEEE Geosc. Remote Sens. Lett., 41(2), 215–229, 2003. Noilhan, J. and Mahfouf, J.-F.: The ISBA land surface parameterization scheme, Global Planet. Change, 13, 145–159, 1996. Noilhan, J. and Planton, S.: A simple parameterization of land surface fluxes processes for meteorological models, Mon Weather~Rev., 117, 536–549, 1989. Owe, M., De~Jeu, R. A M., and Walker, J P.: A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval using the Microwave Polarization Difference index, IEEE Trans. Geosc. Remote Sens., 39(8), 1643–1654, 2001. Rüdiger, C., Calvet, J.-C., Gruhier, C., Holmes, T. R H., de~Jeu, R. A M., and Wagner, W.: An Intercomparison of ERS-SCAT and AMSR-E Soil Moisture Observations with Model Simulations over France, J. Hydrometeorol., 10(2), 431–447, doi:10.1175/2008JHM997.1, 2009. Schmugge, T., Jackson, T., Kustas, T J., and Wang, J. R.: Passive microwave remote sensing of soil moisture: results from HAPEX, FIFE and MONSOON'90, ISPRS J. Photogramm., 47, 127–143, 1992. Seed, A. and Austin, G.: Variability of summer Florida rainfall and its significance for the estimation of rainfall by gages, radar and satellite., J Geophys Res., 95, 2207–2216, 1990. Sorooshian, S., Hsu, K., Gao, X., Gupta, H., Imam, B., and Braithwaite, D.: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall., B. Am. Meteorol. Soc., 81, 2035–2046, 2000. Thorne, V., Coakley, P., Grimes, D., and Dugdale, G.: Comparsion of TAMSAT and CPC rainfall estimates with rainfall, for Southern Africa., Int. J. Remote Sens., 22(10), 1951–1974, 2001. Wang, J R., Shiue, J., Schmugge, T., and Engman, E. T.: The L-band PBMR measurements of surface soil moisture in FIFE, IEEE Trans. Geosc. Remote Sens., 28, 906–914, 1990a. Wagner, W., Naeimi, V., Scipal, K., de Jeu, R., and Mart\'inez-Fernández, J.: Soil moisture from operational meteorological satellites, Hydrogeol. J., 15(1), 121-131, doi:10.1007/s10040-006-0104-6, 2007. Wilk, J., Kniveton, D., Andersson, L., Layberry, R., Todd, M., Hughes, D., Ringrose, S., and Vanderpost, C.: Estimating rainfall and water balance over the Okavango River Basin for hydrological applications., J Hydrol., 331(1–2), 18–29, 2006.