Global soil moisture estimation by assimilating AMSR-E brightness temperatures in a coupled CLM4-RTM-DART system

Journal of Hydrometeorology

Volumn 17, Issue 9, 2016, Pages 2431-2454

  Zhao, Long ^a   Yang, Zong Liang ^a   Hoar, Timothy J ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

Data assimilation; Land surface model; Models and modeling; Observational techniques and algorithms; Optimization; Physical meteorology and climatology; Remote sensing; Soil moisture; Surface observations

Indexed keywords

ALGORITHM; AMSR-E; CLIMATOLOGY; DATA ASSIMILATION; LAND SURFACE; METEOROLOGY; NUMERICAL MODEL; OBSERVATIONAL METHOD; OPTIMIZATION; REMOTE SENSING; SOIL MOISTURE;

EID: 84990857226     PISSN: 1525755X     EISSN: 15257541     Source Type: Journal    
DOI: 10.1175/JHM-D-15-0218.1     Document Type: Article

References (94)

1. Al-Yaari, A., and Coauthors, 2014: Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to land data assimilation system estimates. Remote Sens. Environ., 149, 181-195, doi:10.1016/j.rse.2014.04.006.
2. Anderson, J., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.
3. Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283-1296, doi:10.1175/2009BAMS2618.1.
4. Bao, Q., Y. Liu, J. Shi, and G. Wu, 2010: Comparisons of soil moisture datasets over the Tibetan Plateau and application to the simulation of Asia summer monsoon onset. Adv. Atmos. Sci., 27, 303-314, doi:10.1007/s00376-009-8132-5.
5. Blum, A., 2005: Drought resistance, water-use efficiency, and yield potential-Are they compatible, dissonant, or mutually exclusive? Aust. J. Agric. Res., 56, 1159-1168, doi:10.1071/AR05069.
6. Bolten, J. D., W. T. Crow, X. W. Zhan, T. J. Jackson, and C. A. Reynolds, 2010: Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 57-66, doi:10.1109/JSTARS.2009.2037163.
7. Cai, X., Z.-L. Yang, Y. Xia, M. Huang, H. Wei, L. R. Leung, and M. B. Ek, 2014: Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed. J. Geophys. Res. Atmos., 119, 13 751-13 770, doi:10.1002/2014JD022113.
8. Changnon, S. A., 1987: Detecting drought conditions in Illinois. ISWS/CIR-169/87, Illinois State Water Survey, 36 pp. [Available online at http://www.sws.uiuc.edu/pubdoc/C/ISWSC-169.pdf.]
9. Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, and M. Han, 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res. Atmos., 118, 4466-4475, doi:10.1002/jgrd.50301.
10. Clapp, R. B., and G. M. Hornberger, 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601-604, doi:10.1029/WR014i004p00601.
11. Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn, 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20, 682-690, doi:10.1029/WR020i006p00682.
12. Crow, W. T., and E. F. Wood, 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, doi:10.1016/S0309-1708(02)00088-X.
13. Crow, W. T., and M. J. Van den Berg, 2010: An improved approach for estimating observation and model error parameters in soil moisture data assimilation. Water Resour. Res., 46, W12519, doi:10.1029/2010WR009402.
14. Crow, W. T., and Coauthors, 2012: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys., 50, RG2002, doi:10.1029/2011RG000372.
15. Dai, A., K. E. Trenberth, and T. T. Qian, 2004: A global dataset of Palmer drought severity index for 1870-2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeor., 5, 1117-1130, doi:10.1175/JHM-386.1.
16. de Goncalves, L. G. G., W. J. Shuttleworth, S. C. Chou, Y. Xue, P. R. Houser, D. L. Toll, J. Marengo, and M. Rodell, 2006: Impact of different initial soil moisture fields on Eta model weather forecasts for South America. J. Geophys. Res., 111, D17102, doi:10.1029/2005JD006309.
17. De Lannoy, G. J. M., and R. H. Reichle, 2016: Global assimilation of multiangle and multipolarization SMOS brightness temperature observations into the GEOS-5 catchment land surface model for soil moisture estimation. J. Hydrometeor., 17, 669-691, doi:10.1175/JHM-D-15-0037.1.
18. De Lannoy, G. J. M., P. R. Houser, V. R. N. Pauwels, and N. E. C. Verhoest, 2006: Assessment of model uncertainty for soil moisture through ensemble verification. J. Geophys. Res., 111, D10101, doi:10.1029/2005JD006367.
19. 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, W06401, doi:10.1029/2006WR005100.
20. De Lannoy, G. J. M., R. H. Reichle, and V. R. N. Pauwels, 2013: Global calibration of the GEOS-5 L-band microwave radiative transfer model over nonfrozen land using SMOS observations. J. Hydrometeor., 14, 765-785, doi:10.1175/JHM-D-12-092.1.
21. De Lannoy, G. J. M., R. H. Reichle, and J. A. Vrugt, 2014: Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations. Remote Sens. Environ., 148, 146-157, doi:10.1016/j.rse.2014.03.030.
22. de Rosnay, P., and Coauthors, 2009: AMMA Land Surface Model Intercomparison Experiment coupled to the Community Microwave Emission Model: ALMIP-MEM. J. Geophys. Res., 114, D05108, doi:10.1029/2008JD010724.
23. Dirmeyer, P. A., X. Gao, M. Zhao, Z. Guo, T. Oki, and N. Hanasaki, 2006: GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87, 1381-1397, doi:10.1175/BAMS-87-10-1381.
24. Dobriyal, P., A. Qureshi, R. Badola, and S. A. Hussain, 2012: A review of the methods available for estimating soil moisture and its implications for water resource management. J. Hydrol., 458-459, 110-117, doi:10.1016/j.jhydrol.2012.06.021.
25. Dobson, M. C., F. T. Ulaby, M. T. Hallikainen, and M. A. El-Rayes, 1985: Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models. IEEE Trans. Geosci. Remote Sens., 23, 35-46, doi:10.1109/TGRS.1985.289498.
26. Dorigo, W. A., and Coauthors, 2011: The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 1675-1698, doi:10.5194/hess-15-1675-2011.
27. Draper, C. S., R. H. Reichle, G. J. M. De Lannoy, and Q. Liu, 2012: Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett., 39, L04401, doi:10.1029/2011GL050655.
28. Drusch, M., 2007: Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set. J. Geophys. Res., 112, D03102, doi:10.1029/2006JD007478.
29. Du, E., A. D. Vittorio, and B. Collins, 2014: Historical evaluation of hydrologic components of CLM4: Surface soil water content and runoff. 2014 CESM Land Model Working Group Meeting, Boulder, CO, NCAR, 20 pp. [Available online at http://www.cesm.ucar.edu/working_groups/Land/Presentations/2014/du.pdf.]
30. Duan, Q. Y., V. K. Gupta, and S. Sorooshian, 1993: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl., 76, 501-521, doi:10.1007/BF00939380.
31. Entin, J. K., A. Robock, K. Y. Vinnikov, V. Zabelin, S. Liu, A. Namkhai, and T. Adyasuren, 1999: Evaluation of global soil wetness project soil moisture simulations. J. Meteor. Soc. Japan, 77, 183-198
32. Fujii, H., 2005: Development of a microwave radiative transfer model for vegetated land surface based on comprehensive in-situ observations. Ph.D. thesis, University of Tokyo, 128 pp.
33. Han, X., H.-J. H. Franssen, C. Montzka, and H. Vereecken, 2014: Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations. Water Resour. Res., 50, 6081-6105, doi:10.1002/2013WR014586.
34. Harding, R. J., G. P. Weedon, H. A. J. van Lanen, and D. B. Clark, 2014: The future for global water assessment. J. Hydrol., 518, 186-193, doi:10.1016/j.jhydrol.2014.05.014.
35. Henderson-Sellers, A., Z. L. Yang, and R. E. Dickinson, 1993: The Project for Intercomparison of Land-Surface Parameterization Schemes. Bull. Amer. Meteor. Soc., 74, 1335-1349, doi:10.1175/1520-0477(1993)074<1335:TPFIOL>2.0.CO;2.
36. Hurrell, J. W., and Coauthors, 2013: The Community Earth System Model: A framework for collaborative research. Bull. Amer. Meteor. Soc., 94, 1339-1360, doi:10.1175/BAMS-D-12-00121.1.
37. Jackson, T. J., and T. J. Schmugge, 1991: Vegetation effects on the microwave emission of soils. Remote Sens. Environ., 36, 203-212, doi:10.1016/0034-4257(91)90057-D.
38. Jackson, T. J., and Coauthors, 2010: Validation of Advanced Microwave Scanning Radiometer soil moisture products. IEEE Trans. Geosci. Remote Sens., 48, 4256-4272, doi:10.1109/TGRS.2010.2051035.
39. Kerr, Y., 2007: Soil moisture from space: Where are we? Hydrogeol. J., 15, 117-120, doi:10.1007/s10040-006-0095-3.
40. Kluzek, E., 2013: CESM Research Tools: CLM4 in CESM1.1.1 user's guide documentation. NCAR, 147 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.1/clm/models/lnd/clm/doc/UsersGuide/clm_ug.pdf.]
41. Knowles, K. W., M. H. Savoie, R. L. Armstrong, and M. J. Brodzik, 2011: AMSR-E/Aqua daily global quarter-degree gridded brightness temperatures. Subset used: 1 June 2010 to 1 October 2010. National Snow and Ice Data Center, accessed 10 September 2013, doi:10.5067/RRR4WWORG070
42. Kumar, S. V., R. H. Reichle, R. D. Koster, W. T. Crow, and C. D. Peters-Lidard, 2009: Role of subsurface physics in the assimilation of surface soil moisture observations. J. Hydrometeor., 10, 1534-1547, doi:10.1175/2009JHM1134.1.
43. Kumar, S. V., R. H. Reichle, K. W. Harrison, C. D. Peters-Lidard, S. Yatheendradas, and J. A. Santanello, 2012: A comparison of methods for a priori bias correction in soil moisture data assimilation. Water Resour. Res., 48, W03515, doi:10.1029/2010WR010261.
44. Kwon, Y., A. M. Toure, Z. L. Yang, M. Rodell, and G. Picard, 2015: Error characterization of coupled land surface-radiative transfer models for snow microwave radiance assimilation. IEEE Trans. Geosci. Remote Sens., 53, 5247-5268, doi:10.1109/TGRS.2015.2419977.
45. Lakshmi, V., E. F. Wood, and B. J. Choudhury, 1997: Investigation of effect of heterogeneities in vegetation and rainfall on simulated SSM/I brightness temperatures. Int. J. Remote Sens., 18, 2763-2784, doi:10.1080/014311697217323.
46. Lawrence, D. M., and A. G. Slater, 2008: Incorporating organic soil into a global climate model. Climate Dyn., 30, 145-160, doi:10.1007/s00382-007-0278-1.
47. Lawrence, D. M., and Coauthors, 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J Adv. Model. Earth Syst., 3, M03001, doi:10.1029/2011MS000045.
48. Lawrence, P. J., and T. N. Chase, 2007: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). J. Geophys. Res., 112, G01023, doi:10.1029/2006JG000168.
49. Liang, S., and J. Qin, 2008: Data assimilation methods for land surface variable estimation. Advances in Land Remote Sensing, S. Liang, Ed., Springer, 313-339, doi:10.1007/978-1-4020-6450-0_12
50. Liang, S., and Z. Xiao, 2012: Global land surface products: Leaf area index product data collection (1985-2010). Beijing Normal University, accessed 28 July 2014, doi:10.6050/glass863.3004.db.
51. Loew, A., M. Schwank, and F. Schlenz, 2009: Assimilation of an L-band microwave soil moisture proxy to compensate for uncertainties in precipitation data. IEEE Trans. Geosci. Remote Sens., 47, 2606-2616, doi:10.1109/TGRS.2009.2014846.
52. Loew, A., T. Stacke, W. Dorigo, R. de Jeu, and S. Hagemann, 2013: Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies. Hydrol. Earth Syst. Sci., 17, 3523-3542, doi:10.5194/hess-17-3523-2013.
53. Long, D., B. R. Scanlon, L. Longuevergne, A. Y. Sun, D. N. Fernando, and H. Save, 2013: GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophys. Res. Lett., 40, 3395-3401, doi:10.1002/grl.50655.
54. Margulis, S. A., D. McLaughlin, D. Entekhabi, and S. Dunne, 2002: Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment. Water Resour. Res., 38, 1299, doi:10.1029/2001WR001114.
55. Montzka, C., H. Moradkhani, L. Weihermuller, H. J. H. Franssen, M. Canty, and H. Vereecken, 2011: Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter. J. Hydrol., 399, 410-421, doi:10.1016/j.jhydrol.2011.01.020.
56. Moradkhani, H., S. Sorooshian, H. V. Gupta, and P. R. Houser, 2005: Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour., 28, 135-147, doi:10.1016/j.advwatres.2004.09.002.
57. Nie, S., J. Zhu, and Y. Luo, 2011: Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: Identical twin experiments. Hydrol. Earth Syst. Sci., 15, 2437-2457, doi:10.5194/hess-15-2437-2011.
58. Njoku, E. G., P. Ashcroft, T. K. Chan, and L. Li, 2005: Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans. Geosci. Remote Sens., 43, 938-947, doi:10.1109/TGRS.2004.837507.
59. Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-461+STR, 173 pp., doi:10.5065/D6N877R0
60. Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, 257 pp., doi:10.5065/D6FB50WZ.
61. Paloscia, S., and P. Pampaloni, 1988: Microwave polarization index for monitoring vegetation growth. IEEE Trans. Geosci. Remote Sens., 26, 617-621, doi:10.1109/36.7687.
62. Pan, M., and E. F. Wood, 2006: Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J. Hydrometeor., 7, 534-547, doi:10.1175/JHM495.1.
63. Prigent, C., F. Aires, W. B. Rossow, and A. Robock, 2005: Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements. J. Geophys. Res., 110, D07110, doi:10.1029/2004JD005087.
64. Qin, J., S. L. Liang, K. Yang, I. Kaihotsu, R. G. Liu, and T. Koike, 2009: Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal. J. Geophys. Res., 114, D15103, doi:10.1029/2008JD011358.
65. Raeder, K., J. L. Anderson, N. Collins, T. J. Hoar, J. E. Kay, P. H. Lauritzen, and R. Pincus, 2012: DART/CAM: An ensemble data assimilation system for CESM atmospheric models. J. Climate, 25, 6304-6317, doi:10.1175/JCLI-D-11-00395.1.
66. Reichle, R. H., 2008: Data assimilation methods in the earth sciences. Adv. Water Resour., 31, 1411-1418, doi:10.1016/j.advwatres.2008.01.001.
67. Reichle, R. H., and R. D. Koster, 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501, doi:10.1029/2004GL020938.
68. Reichle, R. H., D. B. McLaughlin, and D. Entekhabi, 2002: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130, 103-114, doi:10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2.
69. Reichle, R. H., R. D. Koster, P. Liu, S. P. P. Mahanama, E. G. Njoku, and M. Owe, 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.
70. Reichle, R. H., W. T. Crow, and C. L. Keppenne, 2008: An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resour. Res., 44, W03423, doi:10.1029/2007WR006357.
71. Reichle, R. H., M. G. Bosilovich, W. T. Crow, R. D. Koster, S. V. Kumar, S. P. Mahanama, and B. F. Zaitchik, 2009: Recent advances in land data assimilation at the NASA Global Modeling and Assimilation Office. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, S. K. Park and L. Xu, Eds., Springer, 407-428, doi:10.1007/978-3-540-71056-1
72. Reichle, R. H., G. J. M. De Lannoy, B. A. Forman, C. S. Draper, and Q. Liu, 2014: Connecting satellite observations with water cycle variables through land data assimilation: Examples using the NASA GEOS-5 LDAS. Surv. Geophys., 35, 577-606, doi:10.1007/s10712-013-9220-8.
73. Robinson, D. A., and Coauthors, 2008: Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vadose Zone J., 7, 358-389, doi:10.2136/vzj2007.0143.
74. Romano, N., 2014: Soil moisture at local scale: Measurements and simulations. J. Hydrol., 516, 6-20, doi:10.1016/j.jhydrol.2014.01.026.
75. Sellers, P. J., and Coauthors, 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Climate, 9, 676-705, doi:10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2.
76. Shi, X., J. Wen, L. Wang, T. Zhang, H. Tian, X. Wang, R. Liu, and J. Zhang, 2010: Regional soil moisture retrievals and simulations from assimilation of satellite microwave brightness temperature observations. Environ. Earth Sci., 61, 1289-1299, doi:10.1007/s12665-010-0504-8.
77. Su, Z., J. Wen, L. Dente, R. van der Velde, L. Wang, Y. Ma, K. Yang, and Z. Hu, 2011: 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., 15, 2303-2316, doi:10.5194/hess-15-2303-2011.
78. Tian, X., Z. Xie, and A. Dai, 2008: A land surface soil moisture data assimilation system based on the dual-UKF method and the Community Land Model. J. Geophys. Res., 113, D14127, doi:10.1029/2007JD009650.
79. Tian, X., Z. Xie, A. Dai, B. H. Jia, and C. X. Shi, 2010: A microwave land data assimilation system: Scheme and preliminary evaluation over China. J. Geophys. Res., 115, D21113, doi:10.1029/2010JD014370.
80. Tian, X., Z. Xie, and Q. Sun, 2011: A POD-based ensemble four-dimensional variational assimilation method. Tellus, 63A, 805-816, doi:10.1111/j.1600-0870.2011.00529.x.
81. Ulaby, F. T., R. K. Moore, and A. K. Fung, 1986: Microwave Remote Sensing Active and Passive-Volume III: From Theory to Applications. Artech House, 2162 pp.
82. Vereecken, H., J. A. Huisman, H. Bogena, J. Vanderborght, J. A. Vrugt, and J. W. Hopmans, 2008: On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resour. Res., 44, W00D06, doi:10.1029/2008WR006829.
83. Vinnikov, K. Y., A. Robock, S. Qiu, J. K. Entin, M. Owe, B. J. Choudhury, S. E. Hollinger, and E. G. Njoku, 1999: Satellite remote sensing of soil moisture in Illinois, United States. J. Geophys. Res., 104, 4145-4168, doi:10.1029/1998JD200054.
84. Wang, J. R., and B. J. Choudhury, 1981: Remote sensing of soil moisture content over bare field at 1.4 GHz frequency. J. Geophys. Res., 86, 5277-5282, doi:10.1029/JC086iC06p05277.
85. Wegmuller, U., and C. Matzler, 1999: Rough bare soil reflectivity model. IEEE Trans. Gesoci. Remote Sens., 37, 1391-1395, doi:10.1109/36.763303.
86. Xia, Y., J. Sheffield, M. B. Ek, J. Dong, N. Chaney, H. Wei, J. Meng, and E. F. Wood, 2014: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107-125, doi:10.1016/j.jhydrol.2014.02.027.
87. Xiao, Z., S. Liang, J. Wang, P. Chen, X. Yin, L. Zhang, and J. Song, 2014: Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Gesoci. Remote Sens., 52, 209-223, doi:10.1109/TGRS.2013.2237780.
88. Yang, K., and Coauthors, 2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget. J. Meteor. Soc. Japan, 85A, 229-242, doi:10.2151/jmsj.85A.229.
89. Yang, K., T. Koike, I. Kaihotsu, and J. Qin, 2009: Validation of a dual-pass microwave land data assimilation system for estimating surface soil moisture in semiarid regions. J. Hydrometeor., 10, 780-793, doi:10.1175/2008JHM1065.1.
90. Zeng, X., and M. Decker, 2009: Improving the numerical solution of soil moisture-based Richards equation for land models with a deep or shallow water table. J. Hydrometeor., 10, 308-319, doi:10.1175/2008JHM1011.1.
91. Zhang, S. L., J. C. Shi, and Y. J. Dou, 2012: A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model. Int. J. Remote Sens., 33, 2770-2797, doi:10.1080/01431161.2011.620032.
92. Zhang, Y.-F., 2015: Multivariate land snow data assimilation in the Northern Hemisphere: Development, evaluation and uncertainty quantification of the extensible data assimilation system. Ph.D. dissertation, The University of Texas at Austin, 138 pp. [Available online at https://repositories.lib.utexas.edu/handle/2152/32613.]
93. Zhang, Y.-F., T. J. Hoar, Z.-L. Yang, J. L. Anderson, A. M. Toure, and M. Rodell, 2014: Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4. J. Geophys. Res. Atmos., 119, 7091-7103, doi:10.1002/2013JD021329.
94. Zhao, L., K. Yang, J. Qin, and Y. Chen, 2013: Optimal exploitation of AMSR-E signals for improving soil moisture estimation through land data assimilation. IEEE Trans. Geosci. Remote Sens., 51, 399-410, doi:10.1109/TGRS.2012.2198483.