[1] |
Balsamo, G., C. Albergel, A. Beljaars, et al., 2015: ERA-Interim/Land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci., 19, 389–407. doi: 10.5194/hess-19-389-2015 |
[2] |
Bartalis, Z., W. Wagner, V. Naeimi, et al., 2007: Initial soil moisture retrievals from the METOP-A advanced scatterometer (ASCAT). Geophys. Res. Lett., 34, L20401. doi: 10.1029/2007GL031088 |
[3] |
Blyverket, J., P. D. Hamer, L. Bertino, et al., 2019: An evaluation of the EnKF vs. EnOI and the assimilation of SMAP, SMOS and ESA CCI soil moisture data over the contiguous US. Remote Sens., 11, 478. doi: 10.3390/rs11050478 |
[4] |
Carrera, M. L., S. Bélair, and B. Bilodeau, 2015: The Canadian land data assimilation system (CaLDAS): Description and synthetic evaluation study. J. Hydrometeor., 16, 1293–1314. doi: 10.1175/JHM-D-14-0089.1 |
[5] |
Cosgrove, B. A., D. Lohmann, K. E. Mitchell, et al., 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res. Atmos., 108, 8842. doi: 10.1029/2002JD003118 |
[6] |
De Rosnay, P., M. Drusch, D. Vasiljevic, et al., 2013: A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF. Quart. J. Roy. Meteor. Soc., 139, 1199–1213. doi: 10.1002/qj.2023 |
[7] |
De Lannoy, G. J. M., and R. H. Reichle, 2016: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model. Hydrol. Earth Syst. Sci., 20, 4895–4911. doi: 10.5194/hess-20-4895-2016 |
[8] |
Draper, C. S., R. H. Reichle, G. J. M. De Lannoy, et al., 2012: Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett., 39, L04401. doi: 10.1029/2011GL050655 |
[9] |
Engman, E. T., 1991: Applications of microwave remote sensing of soil moisture for water resources and agriculture. Remote Sens. Environ., 35, 213–226. doi: 10.1016/0034-4257(91)90013-V |
[10] |
Entekhabi, D., E. Njoku, and P. O'Neill., 2009: The Soil Moisture Active and Passive mission (SMAP): Science and applications. Proc. 2009 IEEE Radar Conference, IEEE, Pasadena, CA, USA, 1–3, doi: 10.1109/RADAR.2009.4977030. |
[11] |
Evensen, G., 2003: The Ensemble Kalman Filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–367. doi: 10.1007/s10236-003-0036-9 |
[12] |
Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723–757. doi: 10.1002/qj.49712555417 |
[13] |
Gruber, A., W. Crow, W. Dorigo, et al., 2015: The potential of 2D Kalman filtering for soil moisture data assimilation. Remote Sens. Environ., 171, 137–148. doi: 10.1016/j.rse.2015.10.019 |
[14] |
Gruber, A., W. T. Crow, and W. A. Dorigo, 2018: Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain. Water Resour. Res., 54, 1353–1367. doi: 10.1002/2017WR021277 |
[15] |
Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776–2790. doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2 |
[16] |
Jia, B. H., Z. H. Xie, X. J. Tian, et al., 2009: A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature. Sci. China Ser. D: Earth Sci., 52, 1835. doi: 10.1007/s11430-009-0122-z |
[17] |
Jordan R., 1991: A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM. 89 (No. CRREL-SR-91-16). Cold Regions Research and Engineering Lab Hanover NH, 61 pp |
[18] |
Kawanishi, T., T. Sezai, Y. Ito, et al., 2003: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosci. Remote Sens., 41, 184–194. doi: 10.1109/TGRS.2002.808331 |
[19] |
Kerr, Y. H., P. Waldteufel, J. P. Wigneron, et al., 2001: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens., 39, 1729–1735. doi: 10.1109/36.942551 |
[20] |
Kim, S., Y. Y. Liu, F. M. Johnson, et al., 2015: A global comparison of alternate AMSR2 soil moisture products: Why do they differ? Remote Sens. Environ., 161, 43–62. doi: 10.1016/j.rse.2015.02.002 |
[21] |
Kolassa, J., R. H. Reichle, Q. Liu, et al., 2017: Data assimilation to extract soil moisture information from SMAP observations. Remote Sens., 9, 1179. doi: 10.3390/rs9111179 |
[22] |
Lievens, H., S. K. Tomer, A. Al Bitar, et al., 2015: SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia. Remote Sens. Environ., 168, 146–162. doi: 10.1016/j.rse.2015.06.025 |
[23] |
Ma, Z. G., G. Huang, W. Q. Gan, et al., 2005: Multi-scale temporal characteristics of the dryness/wetness over northern China during the last century. Chinese J. Atmos. Sci., 29, 671–681. (in Chinese) |
[24] |
Meng, J., R. Q. Yang, H. L. Wei, et al., 2012: The land surface analysis in the NCEP Climate Forecast System Reanalysis. J. Hydrometeor., 13, 1621–1630. doi: 10.1175/JHM-D-11-090.1 |
[25] |
Moradkhani, H., 2008: Hydrologic remote sensing and land surface data assimilation. Sensors, 8, 2986–3004. doi: 10.3390/s8052986 |
[26] |
Naeimi, V., K. Scipal, Z. Bartalis, et al., 2009: An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations. IEEE Trans. Geosci. Remote Sens., 47, 1999–2013. doi: 10.1109/TGRS.2008.2011617 |
[27] |
Niu, G. Y., and Z. L. Yang, 2006: Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale. J. Hydrometeor., 7, 937–952. doi: doi.org/10.1175/JHM538.1 |
[28] |
Niu, G. Y., Z. L. Yang, R. E. Dickinson, et al., 2007: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J. Geophys. Res. Atmos., 112 . doi: doi:10.1029/2006JD007522 |
[29] |
Niu, G. Y., Z. L. Yang, K. E. Mitchell, et al., 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. Atmos., 116, D12109. doi: 10.1029/2010JD015139 |
[30] |
Oke, P. R., P. Sakov, and S. P. Corney, 2007: Impacts of localisation in the EnKF and EnOI: Experiments with a small model. Ocean Dyn., 57, 32–45. doi: 10.1007/s10236-006-0088-8 |
[31] |
Paloscia, S., G. Macelloni, E. Santi, et al., 2001: A multifrequency algorithm for the retrieval of soil moisture on a large scale using microwave data from SMMR and SSM/I satellites. IEEE Trans. Geosci. Remote Sens., 39, 1655–1661. doi: 10.1109/36.942543 |
[32] |
Pan, M., and E. F. Wood, 2009: A multiscale ensemble filtering system for hydrologic data assimilation. Part II: Application to land surface modeling with satellite rainfall forcing. J. Hydrometeor., 10, 1493–1506. doi: 10.1175/2009JHM1155.1 |
[33] |
Pan, M., and E. F. Wood, 2010: Impact of accuracy, spatial availability, and revisit time of satellite-derived surface soil moisture in a multiscale ensemble data assimilation system. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens., 3, 49–56. doi: 10.1109/JSTARS.2010.2040585 |
[34] |
Pan, M., E. F. Wood, D. B. McLaughlin, et al., 2009: A multiscale ensemble filtering system for hydrologic data assimilation. Part I: Implementation and synthetic experiment. J. Hydrometeor., 10, 794–806. doi: 10.1175/2009JHM1088.1 |
[35] |
Reichle, R. H., R. D. Koster, G. J. M. De Lannoy, et al., 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 6322–6338. doi: 10.1175/JCLI-D-10-05033.1 |
[36] |
Rodell, M., P. R. Houser, U. Jambor, et al., 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381–394. doi: 10.1175/BAMS-85-3-381 |
[37] |
Scipal, K., M. Drusch, and W. Wagner, 2008: Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system. Adv. Water Res., 31, 1101–1112. doi: 10.1016/j.advwatres.2008.04.013 |
[38] |
Shi, C. X., Z. H. Xie, H. Qian, et al., 2011: China land soil moisture EnKF data assimilation based on satellite remote sensing data. Sci. China Earth Sci., 54, 1430–1440. doi: 10.1007/s11430-010-4160-3 |
[39] |
Shi, C. X., Y. Pan, J. X. Gu, et al., 2019: A review of multi-source meteorological data fusion products. Acta Meteor. Sinica, 77, 774–783. (in Chinese) |
[40] |
Sun, R. J., Y. P. Zhang, S. L. Wu, et al., 2014: The FY-3B/MWRI soil moisture product and its application in drought monitoring. Proc. 2014 IEEE Geoscience and Remote Sensing Symposium, IEEE, Quebec, Canada, doi: 10.1109/IGARSS.2014.6947184. |
[41] |
Tian, X. J., Z. H. Xie, A. G. Dai, et al., 2009: A dual-pass variational data assimilation framework for estimating soil moisture profiles from AMSR-E microwave brightness temperature. J. Geophys. Res. Atmos., 114, D16102. doi: 10.1029/2008JD011600 |
[42] |
Wang, L. Y., and Y. B. He, 2015: Research on outlier threshold of automatic soil moisture observation data. Meteor. Mon., 41, 1017–1022. (in Chinese) doi: 10.7519/j.issn.1000-0526.2015.08.011 |
[43] |
Xia, Y. L., J. Sheffield, M. B. Ek, et al., 2014: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107–125. doi: 10.1016/j.jhydrol.2014.02.027 |
[44] |
Yang, K., T. Watanabe, T. Koike, et al., 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 |
[45] |
Yeh, T. C., R. T. Wetherald, and S. Manabe, 1984: The effect of soil moisture on the short-term climate and hydrology change—A numerical experiment. Mon. Wea. Rev., 112, 474–490. doi: 10.1175/1520-0493(1984)112<0474:TEOSMO>2.0.CO;2 |