Assessment of SMOS soil moisture retrieval parameters using tau-omega algorithms for soil moisture deficit estimation

Journal of Hydrology

Volumn 519, Issue PA, 2014, Pages 574-587

  Srivastava, Prashant K ^a,b   Han, Dawei ^b   Rico Ramirez, Miguel A ^b   O'Neill, Peggy ^a   Islam, Tanvir ^c   Gupta, Manika ^d  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

^c NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

^d INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

Brightness temperature; Hydrological model; MODIS; Single channel algorithm; Tau omega; WRF NOAH LSM

Indexed keywords

BRIGHTNESS TEMPERATURES; HYDROLOGICAL MODELING; MODIS; SINGLE-CHANNEL ALGORITHMS; TAU-OMEGA; WRF-NOAH LSM;

ALGORITHM; SALINITY; SATELLITE DATA; SMOS; SOIL MOISTURE; VEGETATION;

EID: 84906739327     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.07.056     Document Type: Article

References (74)

1. Aubert D., Loumagne C., Oudin L. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. J. Hydrol. 2003, 280(1):145-161.
2. Bell V., Moore R. A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies. Hydrol. Earth Syst. Sci. 1998, 2(2/3):283-298.
3. Beven K., Binley A. The future of distributed models: model calibration and uncertainty prediction. Hydrol. Process. 2006, 6(3):279-298.
4. Beven K., Wood E.F. Catchment geomorphology and the dynamics of runoff contributing areas. J. Hydrol. 1983, 65(1):139-158.
5. Black T.L. The new NMC mesoscale Eta model: description and forecast examples. Weather Forecast 1994, 9(2):265-278.
6. Blasone R.-S., Madsen H., Rosbjerg D. Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling. J. Hydrol. 2008, 353(1):18-32.
7. Blumer A., Ehrenfeucht A., Haussler D., Warmuth M.K. Occam's razor. Inf. Process. Lett. 1987, 24(6):377-380.
8. Chen F., Mitchell K., Schaake J., Xue Y., Pan H.-L., Koren V., Duan Q., Ek M., Betts A. Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res-Atmos. (1984-2012) 1996, 101(D3):7251-7268.
9. Chen F., Dudhia J. Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Weather Rev. 2001, 129(4):569-585.
10. Cramer W., et al. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Glob. Change Biol. 2001, 7(4):357-373.
11. Croke B.F., Jakeman A.J. A catchment moisture deficit module for the IHACRES rainfall-runoff model. Environ. Model. Softw. 2004, 19(1):1-5.
12. de Jeu, R.A., Holmes, T.R., Parinussa, R.M., Owe, M., 2014. A spatially coherent global soil moisture product with improved temporal resolution. J. Hydrol. doi:10.1016/j.jhydrol.2014.02.015.
13. Dudhia J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci. 1989, 46(20):3077-3107.
14. Engman E.T. Progress in microwave remote sensing of soil moisture. Can. J. Remote Sens. 1990, 16(3):6-14.
15. Entekhabi D., et al. The soil moisture active passive (SMAP) mission. Proc. IEEE 2010, 98(5):704-716.
16. Evans J., Jakeman A. Development of a simple, catchment-scale, rainfall-evapotranspiration-runoff model. Environ. Model. Softw. 1998, 13(3):385-393.
17. Goward S.N., Xue Y., Czajkowski K.P. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: an exploration with the simplified simple biosphere model. Remote Sens. Environ. 2002, 79(2):225-242.
18. Han D., Bray M. Automated Thiessen polygon generation. Water Resour. Res. 2006, 42(11).
19. Han D., Jaafar W.Z.W. Model structure exploration for index flood regionalization. Hydrol. Process. 2013, 27(20):2903-2917.
20. Heikkila¨ U., Sandvik A., Sorteberg A. Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model. Clim. Dyn. 2011, 37(7-8):1551-1564.
21. Hu X.M., Nielsen-Gammon J.W., Zhang F. Evaluation of three planetary boundary layer schemes in the WRF model. J. Appl. Meteorol. Climatol. 2010, 49(9):1831-1844.
22. Islam T., Srivastava P.K., Gupta M., Zhu X., Mukherjee S. Computational Intelligence Techniques in Earth and Environmental Sciences 2014, Springer, Netherlands.
23. Jackson T., Schmugge T. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 1991, 36(3):203-212.
24. Jackson T.J. III. Measuring surface soil moisture using passive microwave remote sensing. Hydrol. Process. 1993, 7(2):139-152.
25. Jackson T.J., Hsu A.Y., O'Neill P.E. Surface soil moisture retrieval and mapping using high-frequency microwave satellite observations in the Southern Great Plains. J. Hydrometeorol. 2002, 3(6):688-699.
26. Jackson T.J., O'Neill P.E., Swift C.T. Passive microwave observation of diurnal surface soil moisture. Geosci. Remote Sens., IEEE Trans. 1997, 35(5):1210-1222.
27. Jacquemin B., Noilhan J. Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound.-Layer Meteorol. 1990, 52(1):93-134.
28. Jin X., Xu C.-Y., Zhang Q., Singh V. Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J. Hydrol. 2010, 383(3):147-155.
29. Kerr, Y. et al., 2006. SMOS level 2 processor soil moisture algorithm theoretical basis document (ATBD). SM-ESL (CBSA), CESBIO, Toulouse, SO-TN-ESL-SM-GS-0001, V5. a, 15/03.
30. Kerr Y.H., et al. The SMOS soil moisture retrieval algorithm. Geosci. Remote Sens., IEEE Trans. 2012, 50(5):1384-1403.
31. Kerr Y.H., et al. Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission. Geosci. Remote Sens., IEEE Trans. 2001, 39(8):1729-1735.
32. Krause P., Boyle D.P., Ba¨se F. Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci. 2005, 5(5):89-97.
33. Laymon C.A., Manu A., Crosson W.L., Jackson T.J. Defining the range of uncertainty associated with remotely sensed soil moisture estimates with microwave radiometers. Remote Sens. Int. Soc. Optics Photonic. 1999, 504-512.
34. Liu J., Han D. On selection of the optimal data time interval for real-time hydrological forecasting. Hydrol. Earth Syst. Sci. 2013, 17(9):3639-3659.
35. Liu Y., et al. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ. 2012, 123:280-297.
36. Lo J.C.F., Yang Z.L., Pielke R.A. Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res.: Atmos. 2008, (1984-2012), 113(D9).
37. Mahfouf J.-F. Analysis of soil moisture from near-surface parameters: a feasibility study. J. Appl. Meteorol. 1991, 30(11):1534-1547.
38. McCuen R.H., Knight Z., Cutter A.G. Evaluation of the Nash-Sutcliffe efficiency index. J. Hydrol. Eng. 2006, 11(6):597-602.
39. Merz B., Plate E.J. An analysis of the effects of spatial variability of soil and soil moisture on runoff. Water Resour. Res. 1997, 33(12):2909-2922.
40. Mladenova I., et al. Remote monitoring of soil moisture using passive microwave-based techniques-theoretical basis and overview of selected algorithms for AMSR-E. Remote Sens. Environ. 2014, 144:197-213.
41. Mlawer E.J., Taubman S.J., Brown P.D., Iacono M.J., Clough S.A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. 1997, 102(D14):16663-16682.
42. Mo T., Choudhury B., Schmugge T., Wang J., Jackson T. A model for microwave emission from vegetation-covered fields. J. Geophys. Res.: Oceans 1982, 87(C13):11229-11237. (1978-2012).
43. Moore R. The PDM rainfall-runoff model. Hydrol. Earth Syst. Sci. 2007, 11(1):483-499.
44. Nash J.E., Sutcliffe J. River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol. 1970, 10(3):282-290.
45. Owe M., de Jeu R., Walker J. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. Geosci. Remote Sens., IEEE Trans. 2001, 39(8):1643-1654.
46. Pan H.-L., Mahrt L. Interaction between soil hydrology and boundary-layer development. Bound-Lay. Meteorol. 1987, 38(1):185-202.
47. Pinori, S., Crapolicchio, R., Mecklenburg, S., 2008. Preparing the ESA-SMOS (soil moisture and ocean salinity) mission-overview of the user data products and data distribution strategy. Microwave Radiometry and Remote Sensing of the Environment. MICRORAD 2008. IEEE, pp. 1-4.
48. Powers J.G. Numerical prediction of an Antarctic severe wind event with the Weather Research and Forecasting (WRF) model. Mon. Weather Rev. 2007, 135(9):3134-3157.
49. Routray A., Mohanty U., Niyogi D., Rizvi S.R.H., Osuri K.K. Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system. Meteorol. Atmos. Phys. 2010, 106(1):107-125.
50. Sa´nchez-Ruiz, S. et al., 2014. Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. J. Hydrol. doi:10.1016/j.jhydrol.2013.12.047.
51. Sandholt I., Rasmussen K., Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ. 2002, 79(2):213-224.
52. Schaake J.C., Koren V.I., Duan Q.Y., Mitchell K., Chen F. Simple water balance model for estimating runoff at different spatial and temporal scales. J. Geophys. Res. D: Atmos. 1996, 101:7461-7475.
53. Schmugge T., O'Neill P.E., Wang J.R. Passive microwave soil moisture research. Geosci. Remote Sens. IEEE Trans. 1986, 1:12-22.
54. Schwartz C.S., et al. Next-day convection-allowing WRF model guidance: a second look at 2-km versus 4-km grid spacing. Mon. Weather Rev. 2009, 137(10):3351-3372.
55. Scott C.A., Bastiaanssen W.G.M. Mapping root zone soil moisture using remotely sensed optical imagery. J. Irrig. Drain. Eng. 2003, 129:326.
56. Srivastava, P.K., 2013. Soil Moisture Estimation from SMOS Satellite and Mesoscale Model for Hydrological Applications. PhD Thesis, University of Bristol, Bristol, United Kingdom.
57. Srivastava P.K., Han D., Ramirez M.R., Islam T. Machine learning techniques for downscaling SMOS Satellite Soil Moisture Using MODIS land surface temperature for hydrological application. Water Resour. Manage 2013, 27(8):3127-3144.
58. Srivastava P.K., Han D., Rico-Ramirez M., Al-Shrafany D., Islam T. Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH Land Surface Model. Water Resour. Manage 2013, 27(15):5069-5087.
59. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., 2012a. Assessment of SMOS satellite derived soil moisture for soil moisture deficit stimation. In: Prediction in Ungauged Basin (PUBS) Symposium. Delft University of Technology, Delft, Netherlands and IAHS, 22-25 October 2012.
60. Srivastava P.K., Han D., Rico-Ramirez M.A., Bray M., Islam T. Selection of classification techniques for land use/land cover change investigation. Adv. Space Res. 2012, 50(9):1250-1265.
61. Srivastava P.K., Han D., Rico Ramirez M.A., Islam T. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. J. Hydrol. 2013, 10.1016/j.jhydrol.2013.06.021.
62. Srivastava P.K., Han D., Rico Ramirez M.A., Islam T. Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model. Atmos. Sci. Lett. 2013, 14(2):118-125.
63. Srivastava P.K., Han D., Rico Ramirez M.A., Islam T. Sensitivity and uncertainty analysis of mesoscale model downscaled hydro-meteorological variables for discharge prediction. Hydrol. Process. 2013, 10.1002/hyp.9946.
64. Su Z., et al. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrol. Earth Syst. Sci. 2011, 15(7):2303-2316.
65. Taylor K.E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res 2001, 106(D7):7183-7192.
66. Thakur J., Srivastava P.K., Singh S.K. Ecological monitoring of wetlands in semi-arid region of Konya closed Basin, Turkey. Reg. Environ. Change 2012, 12(1):133-144.
67. Wang L., Qu J.J. Satellite remote sensing applications for surface soil moisture monitoring: a review. Front Earth Sci. China 2009, 3(2):237-247.
68. Wang L., et al. Surface soil moisture estimates from AMSR-E observations over an arid area, Northwest China. Hydrol. Earth Syst. Sci. Discuss. 2009, 6(1).
69. Wigneron, J.-P., Calvet, J.-C., De Rosnay, P., Kerr, Y.H., 2006. L-MEB: A simple model at L-band for the continental areas-Application to the simulation of a half-degree resolution and global scale data set. http://hal.archives-ouvertes.fr/ird-00423064/.
70. Wigneron J.-P., Chanzy A., Calvet J.-C., Olioso A., Kerr Y. Modeling approaches to assimilating L band passive microwave observations over land surfaces. J. Geophys. Res. 2002, 107(D14):4219.
71. Wigneron J.-P., et al. L-band Microwave Emission of the Biosphere (L-MEB) Model: description and calibration against experimental data sets over crop fields. Remote Sens. Environ. 2007, 107(4):639-655.
72. Yang J., Reichert P., Abbaspour K., Xia J., Yang H. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J. Hydrol. 2008, 358(1):1-23.
73. Yatheendradas S., et al. Understanding uncertainty in distributed flash flood forecasting for semiarid regions. Water Resour. Res. 2008, 44(5). W05S19.
74. Zheng Y., Keller A.A. Uncertainty assessment in watershed-scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach. Water Resour. Res. 2007, 43(8):W08407.