[1] AghaKouchak, A., A. Farahmand, F. S. Melton, et al., 2015: Remote sensing of drought: Progress, challenges and opportunities. Rev. Geophy., 53, 452–480. doi: 10.1002/2014RG000456
[2] Albergel, C., J.-C. Calvet, J.-F. Mahfouf, et al., 2010: Monitoring of water and carbon fluxes using a land data assimilation system: A case study for southwestern France. Hydrol. Earth Syst. Sci., 14, 1109–1124. doi: 10.5194/hess-14-1109-2010
[3] Albergel, C., E. Dutra, S. Munier, et al., 2018: ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? Hydrol. Earth Syst. Sci., 22, 3515–3532. doi: 10.5194/hess-22-3515-2018
[4] Al-Yaari, A., J.-P. Wigneron, A. Ducharne, et al., 2014: Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land). Remote Sen. Environ., 152, 614–626. doi: 10.1016/j.rse.2014.07.013
[5] Bai, W. K., X. L. Gu, S. L. Li, et al., 2018: The performance of multiple model-simulated soil moisture datasets relative to ECV satellite data in China. Water, 10, 1384. doi: 10.3390/w10101384
[6] Baldocchi, D., E.Falge, L. H. Gu, et al., 2001: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Amer. Meteor. Soc., 82, 2415–2434. doi: 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2
[7] Balsamo, G., J.-F. Mahfouf, S. Bélair, et al., 2007: A land data assimilation system for soil moisture and temperature: An information content study. J. Hydrometeor., 8, 1225–1242. doi: 10.1175/2007JHM819.1
[8] 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
[9] Bateni, S. M., and D. Entekhabi, 2012: Relative efficiency of land surface energy balance components. Water Resour. Res., 48, W04510. doi: 10.1029/2011WR011357
[10] Beck, H. E., A. de Roo, and A. I. J. M. van Dijk, 2015: Global maps of streamflow characteristics based on observations from several thousand catchments. J. Hydrometeor., 16, 1478–1501. doi: 10.1175/JHM-D-14-0155.1
[11] Beck, H. E., N. Vergopolan, M. Pan, et al., 2017a: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci., 21, 6201–6217. doi: 10.5194/hess-21-6201-2017
[12] Beck, H. E., A. I. J. M. van Dijk, V. Levizzani, et al., 2017b: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21, 589–615. doi: 10.5194/hess-21-589-2017
[13] Beck, H. E., E. F. Wood, M. Pan, et al., 2018: MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. Bull. Amer. Meteor. Soc. . doi: 10.1175/BAMS-D-17-0138.1
[14] Bélair, S., L.-P. Crevier, J. Mailhot, et al., 2003a: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: Warm season results. J. Hydrometeor., 4, 352–370. doi: 10.1175/1525-7541(2003)4<352:OIOTIL>2.0.CO;2
[15] Bélair, S., R. Brown, J. Mailhot, et al., 2003b: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part II: Cold season results. J. Hydrometeor., 4, 371–386. doi: 10.1175/1525-7541(2003)4<371:OIOTIL>2.0.CO;2
[16] Bell, J. E., M. A. Palecki, C. B. Baker, et al., 2013: U.S. climate reference network soil moisture and temperature observations. J. Hydrometeor., 14, 977–988. doi: 10.1175/JHM-D-12-0146.1
[17] Berg, A. A., J. S. Famiglietti, J. P. Walker, et al., 2003: Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes. J. Geophys. Res. Atmos., 108, 4490. doi: 10.1029/2002JD003334
[18] Best, M. J., M. Pryor, D. B. Clark, et al., 2011: The Joint UK Land Environment Simulator (JULES), model description–Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677–699. doi: 10.5194/gmd-4-677-2011
[19] Best, M. J., G. Abramowitz, H. R. Johnson, et al., 2015: The plumbing of land surface models: Benchmarking model performance. J. Hydrometeor., 16, 1425–1442. doi: 10.1175/JHM-D-14-0158.1
[20] Bowling, L. C., D. P. Lettenmaier, B. Nijssen, et al., 2003: Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e): 1: Experiment description and summary intercomparisons. Glob. Planet. Change, 38, 1–30. doi: 10.1016/S0921-8181(03)00003-1
[21] Brocca, L., S. Hasenauer, T. Lacava, et al., 2011: Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sens. Environ., 115, 3390–3408. doi: 10.1016/j.rse.2011.08.003
[22] Bromwich, D. H., and S. H. Wang, 2005: Evaluation of the NCEP-NCAR and ECMWF 15- and 40-yr reanalyses using rawinsonde data from two independent Arctic field experiments. Mon. Wea. Rev., 133, 3562–3578. doi: 10.1175/MWR3043.1
[23] Broxton, P. D., X. B. Zeng, D. Sulla-Menashe, et al., 2014: A global land cover climatology using MODIS data. J. Appl. Meteor. Climatol., 53, 1593–1605. doi: 10.1175/JAMC-D-13-0270.1
[24] Burnash, R. J. C., R. L. Ferral, and R. A. McGuire, 1973: A Generalized Streamflow Simulation System-Conceptual Modeling for Digital Computer. Technical Report, Joint Fed.–State River Forecast Cent., U. S. Natl. Weather Serv. and California Dep. of Water Resoure, Sacramento, CA, USA, 204 pp.
[25] 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
[26] Case, J. L, S. V. Kumar, J. Srikishen, et al., 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high-resolution initializationof the surface state. Wea. Forecasting, 26, 785–807. doi: 10.1175/2011WAF2222455.1
[27] Case, J. L., F. J. Lafontaine, J. R. Bell, et al., 2014: A real-time MODIS vegetation product for land surface and numerical weather prediction models. IEEE Trans. Geosci. Remote Sens., 52, 1772–1786. doi: 10.1109/TGRS.2013.2255059
[28] Chakrabarti, S., T. Bongiovanni, T. Judge, et al., 2017: Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 3867–3879. doi: 10.1109/JSTARS.2014.2315999
[29] Chaudhuri, A. H., R. M. Ponte, and A. T. Nguyen, 2014: A comparison of atmospheric reanalysis products for the Arctic Ocean and implications for uncertainties in air–sea fluxes. J. Climate, 27, 5411–5421. doi: 10.1175/JCLI-D-13-00424.1
[30] Chen, F., Z. Janjic, and K. Mitchell, 1997: Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Bound.-Layer Meteor., 85, 391–421. doi: 10.1023/A:1000531001463
[31] Chen, F., K. W. Manning, M. A. LeMone, et al., 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46, 694–713. doi: 10.1175/JAM2463.1
[32] Chen, Y. Y., K. Yang, J. Qin, et al., 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
[33] Clark, D. B., L. M. Mercado, S. Sitch, et al., 2011: The Joint UK Land Environment Simulator (JULES), model description. Part 2: Carbon fluxes and vegetation dynamics. Geosci. Mo-del Dev., 4, 701–722. doi: 10.5194/gmd-4-701-2011
[34] Clark, M. P., B. Nijssen, J. D. Lundquist, et al., 2015a: A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resour. Res., 51, 2498–2514. doi: 10.1002/2015WR017198
[35] Clark, M. P., B. Nijssen, J. D. Lundquist, et al., 2015b: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies. Water Resour. Res., 51, 2515–2542. doi: 10.1002/2015WR017200
[36] Clewley, D., J. B. Whitcomb, R. Akbar, et al., 2017: A method for upscaling in situ soil moisture measurements to satellite footprint scale using random forests. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 2663–2673. doi: 10.1109/JSTARS.2017.2690220
[37] Cloke, H. L.,and F. Pappenberger, 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613–626. doi: 10.1016/j.jhydrol.2009.06.005
[38] Compo, G. P., J. S. Whitaker, P. D. Sardeshmukh, et al., 2011: The Twentieth Century Reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 1–28. doi: 10.1002/qj.776
[39] Cosgrove, B. A., D. Lohmann, K. E. Mitchell, et al., 2003a: 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
[40] Cosgrove, B. A., D. Lohmann, K. E. Mitchell, et al., 2003b: Land surface model spin-up behavior in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res. Atmos., 108, 8845. doi: 10.1029/2002JD003316
[41] 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
[42] Crow, W. T., A. A. Berg, M. H. Cosh, et al., 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
[43] Cui, C. Y., J. Xu, and J. Y. Zeng, 2018: Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sens., 10, 33. doi: 10.3390/rs10010033
[44] Dai, A. G., 2008: Temperature and pressure dependence of the rain–snow phase transition over land and ocean. Geophys. Res. Lett., 35, L12802. doi: 10.1029/2008GL033295
[45] Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1023. doi: 10.1175/BAMS-84-8-1013
[46] de Goncalves, L. G. G., W. J. Shuttleworth, E. J. Burke, et al., 2006: Toward a South America land data assimilation system: Aspects of land surface model spin-up using the simplified simple biosphere. J. Geophys. Res. Atmos., 111, D17110. doi: 10.1029/2005JD006297
[47] de Rosnay, P., 2017: Land Surface Data for Land Surface Analy-sis. ECMWF Data Assimilation Training Course, ECMWF, Reading, UK, 45 pp. Available at https://software.ecmwf.int/wiki/display/LDAS/LDAS+Home?preview=/27398058/76382811/Land_satellite_NWP_SAF_TC_2017.pdf.
[48] de Rosnay, P., G. Balsamo, C. Albergel, et al., 2014: Initialization of land surface variables for numerical weather prediction. Surv. Geophys., 35, 607–621. doi: 10.1007/s10712-012-9207-x
[49] de Wit, A. J. W., and C. A. van Diepen, 2007: Crop model data assimilation with the ensemble Kalman filter for improving regional crop yield forecasts. Agric. Forest Meteor., 146, 38–56. doi: 10.1016/j.agrformet.2007.05.004
[50] Decker, M., M. A. Brunke, Z. Wang, et al., 2012: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Climate, 25, 1916–1944. doi: 10.1175/JCLI-D-11-00004.1
[51] Dee, D. P., M. Balmaseda, G. Balsamo, et al., 2014: Toward a consistent reanalysis of the climate system. Bull. Amer. Meteor. Soc., 95, 1235–1248. doi: 10.1175/BAMS-D-13-00043.1
[52] Dente, L., G. Satalino, F. Mattia, et al., 2008: Assimilation of leaf area index derived from ASAR and MERIS data into CERES-wheat model to map wheat yield. Remote Sens. Environ., 112, 1395–1407. doi: 10.1016/j.rse.2007.05.023
[53] Derin, Y., and K. K. Yilmaz, 2014: Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeor., 15, 1498–1516. doi: 10.1175/JHM-D-13-0191.1
[54] Dharssi, I., K. J. Bovis, B. Macpherson, et al., 2011: Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrol. Earth Syst. Sci., 15, 2729–2746. doi: 10.5194/hess-15-2729-2011
[55] Dickinson, R. E., A. Henderson-Sellers, and P. J. Kennedy, 1993: Biosphere–Atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model. NCAR Technical Note NCAR/TN-387+STR, NCAR, Boulder, 72 pp, doi: 10.5065/D67W6959.
[56] Dietz, A. J., C. Kuenzer, U. Gessner, et al., 2012: Remote sensing of snow–a review of available methods. Int. J. Remote Sens., 33, 4094–4134. doi: 10.1080/01431161.2011.640964
[57] Ding, B. H., K. Yang, J. Qin, et al., 2014: The dependence of precipitation types on surface elevation and meteorological conditions and its parameterization. J. Hydrol., 513, 154–163. doi: 10.1016/j.jhydrol.2014.03.038
[58] Dirmeyer, P. A., A. J. Dolman, and N. Sato, 1999: The global soil wetness project. Bull. Amer. Meteor. Soc., 80, 851–878. doi: 10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO;2
[59] Dirmeyer, P. A., X. Gao, M. Zhao, et al., 2006: GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87, 1381–1398. doi: 10.1175/BAMS-87-10-1381
[60] Dorigo, W. A., W. Wagner, R. Hohensinn, et al., 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
[61] Dorigo, W. A., A. Xaver, M. Vreugdenhil, et al., 2013: Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone Jour-nal, 12, 1–21. doi: 10.2136/vzj2012.0097
[62] Doycheva, K., G. Horn, C. Koch, et al., 2017: Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning. Adv. Eng. Inform., 33, 427–439. doi: 10.1016/j.aei.2016.11.001
[63] Draper, C. S., R. H. Reichle, and R. D. Koster, 2018: Assessment of MERRA-2 land surface energy flux estimates. J. Climate, 31, 671–691. doi: 10.1175/JCLI-D-17-0121.1
[64] Ek, M. B., K. E. Mitchell, Y. Lin, et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res. Atmos., 108, 8851. doi: 10.1029/2002JD003296
[65] Entin, J. K., A. Robock, K. Y. Vinnikov, et al., 2000: Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res. Atmos., 105, 11865–11877. doi: 10.1029/2000JD900051
[66] Fan, Y. R., G. H. Huang, B. W. Baetz, et al., 2017: Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods. J. Hydrol., 550, 412–426. doi: 10.1016/j.jhydrol.2017.05.010
[67] Fang, L., X. W. Zhan, C. R. Hain, et al., 2018: Impact of using near real-time green vegetation fraction in Noah land surface model of NOAA NCEP on numerical weather predictions. Adv. Meteor., . doi: 10.1155/2018/9256396
[68] Feng, L., J. Li, W. S. Gong, et al., 2016: Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8 OLI images: A solution for large view angle associated problems. Remote Sens. Environ., 174, 56–68. doi: 10.1016/j.rse.2015.11.031
[69] Ferguson, C. R., and D. M. Mocko, 2017: Diagnosing an artificial trend in NLDAS-2 afternoon precipitation. J. Hydrometeor., 18, 1051–1070. doi: 10.1175/JHM-D-16-0251.1
[70] Fischer, G., F. Nachtergaele, S. Prieler, et al., 2008: Global Agro-Ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy. Available at www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/. Accessed on 31 March 2019.
[71] Foken, T., 2008: The energy balance closure problem: An overview. Ecol. Appl., 18, 1351–1367. doi: 10.1890/06-0922.1
[72] Frei, A., M. Tedesco, S. Lee, et al., 2012: A review of global satellite-derived snow products. Adv. Space Res., 50, 1007–1029. doi: 10.1016/j.asr.2011.12.021
[73] Friedl, M. A., D. Sulla-Menashe, B. Tan, et al., 2010: MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168–182. doi: 10.1016/j.rse.2009.08.016
[74] Gao, S. G., Z. L. Zhu, H. T. Weng, et al., 2017: Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing. Int. J. Remote Sens., 38, 4782–4803. doi: 10.1080/01431161.2017.1320444
[75] Gruber, A., C.–H. Su, W. T. Crow, et al., 2016: Estimating error cross-correlations in soil moisture data sets using extended collocation analysis. J. Geophys. Res. Atmos., 121, 1208–1219. doi: 10.1002/2015JD024027
[76] Gupta, H. V., L. A. Bastidas, S. Sorooshian, et al., 1999: Parame-ter estimation of a land surface scheme using multicriteria methods. J. Geophys. Res. Atmos., 104, 19491–19503. doi: 10.1029/1999JD900154
[77] Hamilton, A. S., and R. D. Moore, 2012: Quantifying uncertainty in streamflow records. Can. Water Resour. J., 37, 3–21. doi: 10.4296/cwrj370186
[78] Hansen, M. C., R. S. DeFries, J. R. G. Townshend, et al., 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sen., 21, 1331–1364. doi: 10.1080/014311600210209
[79] Hao, Z. C., F. H. Hao, Y. L. Xia, et al., 2016a: A statistical me-thod for categorical drought prediction based on NLDAS-2. J. Appl. Meteor. Climatol., 55, 1049–1061. doi: 10.1175/JAMC-D-15-0200.1
[80] Hao, Z. C., Y. Hong, Y. L. Xia, et al., 2016b: Probabilistic drought characterization in the categorical form using ordinal regression. J. Hydrol., 535, 331–339. doi: 10.1016/j.jhydrol.2016.01.074
[81] Hao, Z. C., X. Yuan, Y. L. Xia, et al., 2017: An overview of drought monitoring and prediction systems at regional and global scales. Bull. Amer. Meteor. Soc., 98, 1879–1896. doi: 10.1175/BAMS-D-15-00149.1
[82] Hao, Z. C., V. P. Singh, and Y. L. Xia, 2018: Seasonal drought prediction: Advances, challenges, and future prospects. Rev. Geophys., 56, 108–141. doi: 10.1002/2016RG000549
[83] Harmel, R. D., R. J. Cooper, R. M. Slade, et al., 2006: Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Transactions of the ASABE, 49, 689–701. doi: 10.13031/2013.20488
[84] Heim, Jr. R. R., 2002: A review of twentieth-century drought indices used in the United States. Bull. Amer. Meteor. Soc., 83, 1149–1166. doi: 10.1175/1520-0477-83.8.1149
[85] Henderson-Sellers, A., A. J. Pitman, P. K. Love, et al., 1995: The project for intercomparison of land surface parameterization schemes (PILPS): Phases 2 and 3. Bull. Amer. Meteor. Soc., 76, 489–504. doi: 10.1175/1520-0477(1995)076<0489:TPFIOL>2.0.CO;2
[86] Henry, F., D. E. Herwindiati, S. Mulyono, et al., 2017: Sugarcane land classification with satellite imagery using logistic regression model. IOP Conference Series: Materials Science and Engineering, 185, 012024. doi: 10.1088/1757-899X/185/1/012024
[87] Hersbach, H., and D. Dee, 2016: ERA5 reanalysis is in production. ECMWF Newsletter, 147, 1–7.
[88] Hu, Q., and S. Feng, 2003: A daily soil temperature dataset and soil temperature climatology of the contiguous United States. J. Appl. Meteor., 42, 1139–1156. doi: 10.1175/1520-0450(2003)042<1139:ADSTDA>2.0.CO;2
[89] Hu, Q., S. Feng, and G. Schaefer, 2002: Quality control for USDA NRCS SM-ST network soil temperatures: A method and a dataset. J. Appl. Meteor., 41, 607–619. doi: 10.1175/1520-0450(2002)041<0607:QCFUNS>2.0.CO;2
[90] Jacobs, C. M. J., E. J. Moors, H. W. Ter Maat, et al., 2008: Evaluation of European Land Data Assimilation System (ELDAS) products using in situ observations. Tellus A, 60, 1023–1037. doi: 10.1111/j.1600-0870.2008.00351.x
[91] Jiménez, C., C. Prigent, B. Mueller, et al., 2011: Global intercomparison of 12 land surface heat flux estimates. J. Geophys. Res. Atmos., 116, D02102. doi: 10.1029/2010JD014545
[92] Jiménez-Muñoz, J. C., and J. A. Sobrino, 2006: Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data. Int. J. Remote Sens., 27, 999–1014. doi: 10.1080/01431160500075907
[93] Jin, X. L., Z. H. Li, G. J. Yang, et al., 2017: Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS J. Photogram. Remote Sens., 126, 24–37. doi: 10.1016/j.isprsjprs.2017.02.001
[94] Jones, J. W., G. Hoogenboom, C. H. Porter, et al., 2003: The DSSAT cropping system model. Eur. J. Agron., 18, 235–265. doi: 10.1016/S1161-0301(02)00107-7
[95] Jung, M., M. Reichstein, and A. Bondeau, 2009: Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 2001–2013. doi: 10.5194/bg-6-2001-2009
[96] Jung, M., M. Reichstein, P. Ciais, et al., 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951–954. doi: 10.1038/nature09396
[97] Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472. doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
[98] Kanamitsu, M., W. Ebisuzaki, J. Woollen, et al., 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1644. doi: 10.1175/BAMS-83-11-1631
[99] Kang, J., R. Jin, X. Li, et al., 2018: Spatial upscaling of sparse soil moisture observations based on ridge regression. Remote Sens., 10, 192. doi: 10.3390/rs10020192
[100] Kato, S., F. G. Rose, D. A. Rutan, et al., 2018: Surface irradiances of edition 4.0 clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) data product. J. Climate, 31, 4501–4527. doi: 10.1175/JCLI-D-17-0523.1
[101] Kerr, Y. H., 2007: Soil moisture from space: Where are we? Hydrogeol. J., 15, 117–120. doi: 10.1007/s10040-006-0095-3
[102] Khaki, M., F. Hamilton, E. Forootan, et al., 2018: Nonparametric data assimilation scheme for land hydrological applications. Water Resour. Res., 54, 4946–4964. doi: 10.1029/2018WR022854
[103] Kitanidis, P. K., and R. L. Bras, 1980: Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resour. Res., 16, 1034–1044. doi: 10.1029/WR016i006p01034
[104] Kobayashi, S., Y. Ota, Y. Harada, et al., 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan Ser. II, 93, 5–48. doi: 10.2151/jmsj.2015-001
[105] Komma, J., G. Blöschl, and C. Reszler, 2008: Soil moisture updating by ensemble Kalman filtering in real-time flood forecasting. J. Hydrol., 357, 228–242. doi: 10.1016/j.jhydrol.2008.05.020
[106] Konzelmann, T., D. R. Cahoon, and C. H. Whitlock, 1996: Impact of biomass burning in equatorial Africa on the downward surface shortwave irradiance: Observations versus calculations. J. Geophys. Res. Atmos., 101, 22833–22844. doi: 10.1029/96JD01556
[107] Koster, R. D., and M. J. Suarez, 1994: The components of a ‘SVAT’ scheme and their effects on a GCM’s hydrological cycle. Adv. Water Resour., 17, 61–78. doi: 10.1016/0309-1708(94)90024-8
[108] Koster, R. D., M. J. Suarez, A. Ducharne, et al., 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res. Atmos., 105, 24809–24822. doi: 10.1029/2000JD900327
[109] Kumar, S. V., C. D. Peters-Lidard, Y. Tian, et al., 2006: Land Information System—An interoperable framework for high resolution land surface modeling. Environ. Model. Soft., 21, 1402–1415. doi: 10.1016/j.envsoft.2005.07.004
[110] Kumar, S. V., R. H. Reichle, R. D. Koster, et al., 2009: Role of subsurface physics in the assimilation of surface soil moisture observations. J. Hydrometeor., 10, 1534–1547. doi: 10.1175/2009JHM1134.1
[111] Kumar, S. V., C. D. Peters-Lidard, D. Mocko, et al., 2014: Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. J. Hydrometeor., 15, 2446–2469. doi: 10.1175/JHM-D-13-0132.1
[112] Kumar, S. V., M. Jasinski, D. Mocko, et al., 2018: NCA-LDAS land analysis: Development and performance of a multi-sensor, multivariate land data assimilation system for the National Climate Assessment. J. Hydrometeor., . doi: 10.1175/JHM-D-17-0125.1
[113] Lahoz, W. A., and P. Schneider, 2014: Data assimilation: Making sense of earth observation. Front. Environ. Sci., 2, 16. doi: 10.3389/fenvs.2014.00016
[114] Laloyaux, P., M. Balmaseda, D. Dee, et al., 2016: A coupled data assimilation system for climate reanalysis. Quart. J. Roy. Meteor. Soc., 142, 65–78. doi: 10.1002/qj.2629
[115] Lawrence, D. M., K. W. Oleson, M. G. Flanner, et al., 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001. doi: 10.1029/2011MS00045
[116] Lawston, P. M., J. A. Santanello, Jr. B. F. Zaitchik, et al., 2015: Impact of irrigation methods on land surface model spinup and initialization of WRF forecasts. J. Hydrometeor., 16, 1135–1154. doi: 10.1175/JHM-D-14-0203.1
[117] Lee, D. E., and M. Biasutti, 2014: Climatology and variability of precipitation in the twentieth-century reanalysis. J. Climate, 27, 5964–5981. doi: 10.1175/JCLI-D-13-00630.1
[118] Leng, G. Y., M. Y. Huang, Q. H. Tang, et al., 2013: Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. J. Geophys. Res. Atmos., 118, 9789–9803. doi: 10.1002/jgrd.50792
[119] Leng, G. Y., M. Y. Huang, Q. H. Tang, et al., 2015: A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate. J. Adv. Model. Earth Syst., 7, 1285–1304. doi: 10.1002/2015MS000437
[120] Lewis, P., J. Gómez-Dans, T. Kaminski, et al., 2012: An earth observation land data assimilation system (EO-LDAS). Remote Sens. Environ., 120, 219–235. doi: 10.1016/j.rse.2011.12.027
[121] Li, R., C. J. Li, Y. Y. Dong, et al., 2011: Assimilation of remote sensing and crop model for LAI estimation based on ensemble Kalman filter. Agric. Sci. China, 10, 1595–1602. doi: 10.1016/S1671-2927(11)60156-9
[122] Li, X., C. L. Huang, C. Tao, et al., 2007: Development of a Chinese land data assimilation system: Its progress and prospects. Prog. Natural Sci., 17, 163–173. (in Chinese)
[123] Li, X., S. M. Liu, H. X. Li, et al., 2018: Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel. J. Geophys. Res. Atmos., 123, 6777–6803. doi: 10.1029/2018JD028422
[124] Li, Y., Q. G. Zhou, J. Zhou, et al., 2014: Assimilating remote sensing information into a coupled hydrology–crop growth model to estimate regional maize yield in arid regions. Ecological Modelling, 291, 15–27. doi: 10.1016/j.ecolmodel.2014.07.013
[125] Li, Z. L., B. H. Tang, H. Wu, et al., 2013: Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ., 131, 14–37. doi: 10.1016/j.rse.2012.12.008
[126] Liang, S. L., K. C. Wang, X. T. Zhou, et al., 2010: Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 225–240. doi: 10.1109/JSTARS.2010.2048556
[127] Liang, X., D. P. Lettenmaier, E. F. Wood, et al., 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos., 99, 14415–14428. doi: 10.1029/94JD00483
[128] Liao, W. L., D. G. Wang, G. L. Wang, et al., 2019: Quality control and evaluation of the observed daily data in North Ameri-can Soil Moisture Database. J. Meteor. Res., 33, . doi: 10.1007/s13351-019-8121-2
[129] Lim, Y.-J., K.-Y. Byun, T.-Y. Lee, et al., 2012: A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia. Asia–Pacific J. Atmos. Sci., 48, 83–95. doi: 10.1007/s13143-012-0008-4
[130] Liou, Y.-A., and S. K. Kar, 2014: Evapotranspiration estimation with remote sensing and various surface energy balance algorithms—A review. Energies, 7, 2821–2849. doi: 10.3390/en7052821
[131] Liu, S. M., Z. W. Xu, L. S. Song, et al., 2016: Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agric. Forest Meteor., 230–231, 97–113. doi: 10.1016/j.agrformet.2016.04.008
[132] Liu, X. M., T. T. Yang, K. Hsu, et al., 2017: Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau. Hydrol. Earth Syst. Sci., 21, 169–181. doi: 10.5194/hess-21-169-2017
[133] Liu, Y., A. H. Weerts, M. Clark, et al., 2012: Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci., 16, 3863–3887. doi: 10.5194/hess-16-3863-2012
[134] Livneh, B., Y. L. Xia, K. E. Mitchell, et al., 2010: Noah LSM snow model diagnostics and enhancements. J. Hydrometeor., 11, 721–738. doi: 10.1175/2009JHM1174.1
[135] Lohmann, D., K. E. Mitchell, P. R. Houser, et al., 2004: Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project. J. Geophys. Res. Atmos., 109, D07S91. doi: 10.1029/2003JD003517
[136] Luo, L. F., A. Robock, K. E. Mitchell, et al., 2003: Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains. J. Geophys. Res. Atmos., 108, 8843. doi: 10.1029/2002JD003246
[137] Ma, Y. P., S. L. Wang, L. Zhang, et al., 2008: Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. Int. J. Appl. Earth Obs. Geoinfo., 10, 426–437. doi: 10.1016/j.jag.2007.09.002
[138] Machwitz, M., L. Giustarini, C. Bossung, et al., 2014: Enhanced biomass prediction by assimilating satellite data into a crop growth model. Environ. Model. Soft., 62, 437–453. doi: 10.1016/j.envsoft.2014.08.010
[139] Mahfouf, J. F., 2010: Assimilation of satellite-derived soil moisture from ASCAT in a limited-area NWP model. Quart. J. Roy. Meteor. Soc., 136, 784–798. doi: 10.1002/qj.602
[140] Martens, B., D. G. Miralles, H. Lievens, et al., 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 1903–1925. doi: 10.5194/gmd-10-1903-2017
[141] McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Am. Meteor. Soc., Anaheim, CA, USA, 179–184.
[142] McNally, A., K. Arsenault, S. Kumar, et al., 2017: A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4, 170012. doi: 10.1038/sdata.2017.12
[143] 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
[144] Mesinger, F., G. DiMego, E. Kalnay, et al., 2006: North American regional reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360. doi: 10.1175/BAMS-87-3-343
[145] Miller, D. A., and R. A. White, 1998: A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interaction, 2, 1–26. doi: 10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2
[146] Milly, P. C. D., S. L. Malyshev, E. Shevliakova, et al., 2014: An enhanced model of land water and energy for global hydrologic and earth-system studies. J. Hydrometeor., 15, 1739–1761. doi: 10.1175/JHM-D-13-0162.1
[147] Miralles, D. G., T. R. H. Holmes, R. A. M. de Jeu, et al., 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453–469. doi: 10.5194/hess-15-453-2011
[148] Mitchell, K., P. Houser, E. Wood, et al., 1999: GCIP Land Data Assimilation System (LDAS) Project now underway. GEWEX News, 9, 3–6.
[149] Mitchell, K. E., D. Lohmann, P. R. Houser, et al., 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res. Atmos., 109, D07S90. doi: 10.1029/2003JD003823
[150] Mizukami, N., M. P. Clark, E. D. Gutmann, et al., 2016: Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models. J. Hydrometeor., 17, 73–98. doi: 10.1175/JHM-D-14-0187.1
[151] Mizukami, N., M. P. Clark, A. J. Newman, et al., 2017: Towards seamless large-domain parameter estimation for hydrologic models. Water Resour. Res., 53, 8020–8040. doi: 10.1002/2017WR020401
[152] Mo, K. C., L. C. Chen, S. Shukla, et al., 2012: Uncertainties in North American land data assimilation systems over the contiguous United States. J. Hydrometeor., 13, 996–1009. doi: 10.1175/JHM-D-11-0132.1
[153] Mokhtari, A., H. Noory, and M. Vazifedoust, 2018: Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model. Agric. Forest Meteor., 250–251, 159–170. doi: 10.1016/j.agrformet.2017.12.250
[154] Mu, Q. Z., M. S. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 1781–1800. doi: 10.1016/j.rse.2011.02.019
[155] Mu, Q. Z., M. S. Zhao, J. S. Kimball, et al., 2013: A remotely sensed global terrestrial drought severity index. Bull. Amer. Meteor. Soc., 94, 83–98. doi: 10.1175/BAMS-D-11-00213.1
[156] Munier, S., A. Polebistki, C. Brown, et al., 2015: SWOT data assimilation for operational reservoir management on the upper Niger River basin. Water Resour. Res., 51, 554–575. doi: 10.1002/2014WR016157
[157] Nearing, G. S., D. M. Mocko, C. D. Peters-Lidard, et al., 2016: Benchmarking NLDAS-2 soil moisture and evapotranspiration to separate uncertainty contributions. J. Hydrometeor., 17, 745–759. doi: 10.1175/JHM-D-15-0063.1
[158] Nijssen, B., S. Shukla, C. Y. Lin, et al., 2014: A prototype Global Drought Information System based on multiple land surface models. J. Hydrometeor., 15, 1661–1676. doi: 10.1175/JHM-D-13-090.1
[159] 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
[160] Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536–549. doi: 10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2
[161] Nouvellon, Y., M. S. Moran, D. Lo Seen, et al., 2001: Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sens. Environ., 78, 131–149. doi: 10.1016/S0034-4257(01)00255-3
[162] Novick, K. A., J. A. Biederman, A. R. Desai, et al., 2018: The AmeriFlux network: A coalition of the willing. Agric. Forest Meteor., 249, 444–456. doi: 10.1016/j.agrformet.2017.10.009
[163] Oleson, K. W., G.-Y. Niu, Z.-L. Yang, et al., 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res. Biogeo., 113, G01021. doi: 10.1029/2007JG000563
[164] Onogi, K., J. Tsutsui, H. Koide, et al., 2007: The JRA-25 reanaly-sis. J. Meteor. Soc. Japan Ser. II, 85, 369–432. doi: 10.2151/jmsj.85.369
[165] Osuri, K. K., R. Nadimpalli, U. C. Mohanty, et al., 2017: Improved prediction of severe thunderstorms over the Indian monsoon region using high-resolution soil moisture and temperature initialization. Scientific Reports, 7, 41377. doi: 10.1038/srep41377
[166] Palmer, W. C., 1965: Meteorological Drought. Research Paper No. 45, U.S. Weather Bureau, Washington, D. C., 58 pp.
[167] Pan, M., J. Sheffield, E. F. Wood, et al., 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res. Atmos., 108, 8850. doi: 10.1029/2003JD003994
[168] Parastatidis, D., Z. Mitraka, N. Chrysoulakis, et al., 2017: Online global land surface temperature estimation from landsat. Remote Sens., 9, 1208. doi: 10.3390/rs9121208
[169] Pellenq, J., and G. Boulet, 2004: A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie, 24, 197–204. doi: 10.1051/agro:2004017
[170] Peng, J., A. Loew, O. Merlin, et al., 2017: A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys., 55, 341–366. doi: 10.1002/2016RG000543
[171] Penny, S. G., and T. M. Hamill, 2017: Coupled data assimilation for integrated earth system analysis and prediction. Bull. Amer. Meteor. Soc., 98, ES169–ES172. doi: 10.1175/BAMS-D-17-0036.1
[172] Penny, S. G., S. Akella, O. Alves, et al., 2017: Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges and Recommendations. World Wea-ther Research Programme (WWRP 2017–3), World Meteorological Organization, Geneva, Switzerland, 59 pp.
[173] Pinker, R. T., J. D. Tarpley, I. Laszlo, et al., 2003: Surface radiation budgets in support of the GEWEX continental-scale international project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American land data assimilation system (NLDAS) project. J. Geophys. Res. Atmos., 108, 8844. doi: 10.1029/2002JD003301
[174] Qin, J., K. Yang, N. Lu, et al., 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sens. Environ., 138, 1–9. doi: 10.1016/j.rse.2013.07.003
[175] Qin, J., L. Zhao, Y. Y. Chen, et al., 2015: Inter-comparison of spatial upscaling methods for evaluation of satellite-based soil moisture. J. Hydrol., 523, 170–178. doi: 10.1016/j.jhydrol.2015.01.061
[176] Quiring, S .M., T. W. Ford, J. K. Wang, et al., 2016: The North American soil moisture database: Development and applications. Bull. Amer. Meteor. Soc., 97, 1441–1459. doi: 10.1175/BAMS-D-13-00263.1
[177] Rasmussen, R., B. Baker, J. Kochendorfer, et al., 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811–829. doi: 10.1175/BAMS-D-11-00052.1
[178] Reichle, R. H., and R. D. Koster, 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA catchment land surface model. Geophys. Res. Lett., 32, L02404. doi: 10.1029/2004GL021700
[179] Reichle, R. H., W. T. Crow, R. D. Koster, et al., 2008: Contribution of soil moisture retrievals to land data assimilation products. Geophys. Res. Lett., 35, L01404. doi: 10.1029/2007GL031986
[180] Reichle, R. H., G. J. M. De Lannoy, Q. Liu, et al., 2017a: Assessment of the SMAP level-4 surface and root-zone soil moisture product using in situ measurements. J. Hydrometeor., 18, 2621–2645. doi: 10.1175/JHM-D-17-0063.1
[181] Reichle, R., Q. Liu, R. D. Koster, et al., 2017b: Land surface precipitation in MERRA-2. J. Climate, 30, 1643–1664. doi: 10.1175/JCLI-D-16-0570.1
[182] Rennie, J. J., J. H. Lawrimore, B. E. Gleason, et al., 2014: The international surface temperature initiative global land surface databank: Monthly temperature data release description and methods. Geosci. Data J., 1, 75–102. doi: 10.1002/gdj3.8
[183] Reynolds, C. A., T. J. Jackson, and W. J. Rawls, 2000: Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions. Water Resour. Res., 36, 3653–3662. doi: 10.1029/2000WR900130
[184] Rienecker, M. M., M. J. Suarez, R. Gelaro, et al., 2011: MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Climate, 24, 3624–3648. doi: 10.1175/JCLI-D-11-00015.1
[185] Robock, A., L. F. Luo, E. F. Wood, et al., 2003: Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season. J. Geophys. Res. Atmos., 108, 8846. doi: 10.1029/2002JD003245
[186] 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
[187] Saha, S., S. Moorthi, H.-L. Pan, et al., 2010: The NCEP climate forecast system reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1058. doi: 10.1175/2010BAMS3001.1
[188] Saha, S., S. Moorthi, X. R. Wu, et al, 2014: The NCEP climate forecast system version 2. J. Climate, 27, 2185–2208. doi: 10.1175/JCLI-D-12-00823.1
[189] Santanello, Jr. J. A., S. V. Kumar, C. D. Peters-Lidard, et al., 2016: Impact of soil moisture assimilation on land surface model spinup and coupled land–atmosphere prediction. J. Hydrometeor., 17, 517–540. doi: 10.1175/JHM-D-15-0072.1
[190] Sawada, Y., and T. Koike, 2016: Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010–2011). J. Geophy. Res. Atmos., 121, 8229–8242. doi: 10.1002/2015JD024705
[191] Schaake, J. C., Q. Y. Duan, V. Koren, et al., 2004: An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res. Atmos., 109, D01S90. doi: 10.1029/2002JD003309
[192] Schaefer, G. L., M. H. Cosh, and T. J. Jackson, 2007: The USDA natural resources conservation service soil climate analysis network (SCAN). J. Atmos. Oceanic Technol., 24, 2073–2077. doi: 10.1175/2007JTECHA930.1
[193] Sellers, P. J., Y. Mintz, Y. C. Sud, et al., 1986: A simple biosphere model (SIB) for use within general circulation models. J. Atmos. Sci., 43, 505–531. doi: 10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2
[194] Seo, D.-J., Y. Q. Liu, H. Moradkhani, et al., 2014: Ensemble prediction and data assimilation for operational hydrology. J. Hydrol., 519, 2661–2662. doi: 10.1016/j.jhydrol.2014.11.035
[195] Sequera, P., J. E. González, K. McDonald, et al., 2016: Improvements in land-use classification for estimating daytime surface temperatures and sea-breeze flows in Southern California. Earth Interaction, 20, 1–32. doi: 10.1175/EI-D-14-0034.1
[196] Sheffield, J., M. Pan, E. F. Wood, et al., 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent. J. Geophys. Res. Atmos., 108, 8849. doi: 10.1029/2002JD003274
[197] Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 3088–3111. doi: 10.1175/JCLI3790.1
[198] 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
[199] Shuttleworth, W. J., 2007: Putting the " vap” into evaporation. Hydrol. Earth Syst. Sci., 11, 210–244. doi: 10.5194/hess-11-210-2007
[200] Singh, R. S., J. T. Reager, N. L. Miller, et al., 2015: Toward hyper-resolution land-surface modeling: The effects of fine-scale topography and soil texture on CLM4.0 simulations over the Southwestern U.S. Water Resour. Res., 51, 2648–2667. doi: 10.1002/2014WR015686
[201] Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 1870–1884. doi: 10.1175/1520-0493(1997)125<1870:PODSMC>2.0.CO;2
[202] Snauffer, A. M., W. W. Hsieh, and A. J. Cannon, 2016: Compari-son of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. J. Hydrol., 541, 714–726. doi: 10.1016/j.jhydrol.2016.07.027
[203] Sun, Q., C. Miao, Q. Duan, et al., 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79–107. doi: 10.1002/rog.v56.1
[204] Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos., 106, 7183–7192. doi: 10.1029/2000JD900719
[205] Troy, T. J., and E. F. Wood, 2009: Comparison and evaluation of gridded radiation products across northern Eurasia. Environ. Res. Lett., 4, 045008. doi: 10.1088/1748-9326/4/4/045008
[206] Troy, T. J., E. F. Wood, and J. Sheffield, 2008: An efficient calibration method for continental-scale land surface modeling. Water Resour. Res., 44, W09411. doi: 10.1029/2007WR006513
[207] Ungersböck, M., F. Rubel, T. Fuchs, et al., 2001: Bias correction of global daily rain gauge measurements. Phys. Chem. Earth B: Hydrol., Oceans Atmos., 26, 411–414. doi: 10.1016/S1464-1909(01)00027-2
[208] Uppala, S. M., P. W. KÅllberg, A. J. Simmons, et al., 2005: The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012. doi: 10.1256/qj.04.176
[209] van den Hurk, B. J. J. M., P. Viterbo, A. C. M. Beljaars, et al., 2000: Offline Validation of the ERA-40 Surface Scheme. ECMWF Tech. Memo., 295, European Center for Medium-Range Weather Forecasts, Reading, UK, 43 pp.
[210] van Diepen, C. A., J. Wolf, H. van Keulen, et al., 1989: WOFOST: A simulation model of crop production. Soil Use Manag., 5, 16–24. doi: 10.1111/j.1475-2743.1989.tb00755.x
[211] Wagner, W., G. Blöschl, P. Pampaloni, et al., 2007: Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Hydrol. Res., 38, 1–20. doi: 10.2166/nh.2007.029
[212] Wan, Z. M., 2014: New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ., 40, 36–45. doi: 10.1016/j.rse.2013.08.027
[213] Wang, A. H., and X. B. Zeng, 2013: Development of global hourly 0.5° land surface air temperature datasets. J. Climate, 26, 7676–7691. doi: 10.1175/JCLI-D-12-00682.1
[214] Wang, S., B. C. Ancell, G. H. Huang, et al., 2018: Improving robustness of hydrologic ensemble predictions through probabilistic pre- and post-processing in sequential data assimilation. Water Resour. Res., 54, 2129–2151. doi: 10.1002/2018WR022546
[215] Wang, W., W. Cui, X. J. Wang, et al., 2016: Evaluation of GLDAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. J. Hydrometeor., 17, 2815–2833. doi: 10.1175/JHM-D-15-0191.1
[216] Wei, H. L., Y. L. Xia, K. E. Mitchell, et al., 2013: Improvement of the Noah land surface model for warm season processes: Evaluation of water and energy flux simulation. Hydrol. Process., 27, 297–303. doi: 10.1002/hyp.9214
[217] Wei, S. G., Y. J. Dai, Q. Y. Duan, et al., 2014: A global soil data set for earth system modeling. J. Adv. Model. Earth Syst., 6, 249–263. doi: 10.1002/2013MS000293
[218] Wilson, K., A. Goldstein, E. Falge, et al., 2002: Energy balance closure at FLUXNET sites. Agric. Forest Meteor., 113, 223–243. doi: 10.1016/S0168-1923(02)00109-0
[219] Wood, E. F., J. K. Roundy, T. J. Try, et al., 2011: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour. Res., 47, W05301. doi: 10.1029/2010WR010090
[220] Xia, Y. L., A. J. Pitman, H. V. Gupta, et al., 2002: Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset. J. Hydrometeor., 3, 181–194. doi: 10.1175/1525-7541(2002)003<0181:CALSMO>2.0.CO;2
[221] Xia, Y. L., K. E. Mitchell, M. B. Ek, et al., 2012a: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res. Atmos., 117, D03109. doi: 10.1029/2011JD016048
[222] Xia, Y. L., K. E. Mitchell, M. B. Ek, et al., 2012b: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res. Atmos., 117, D03110. doi: 10.1029/2011JD016051
[223] Xia, Y. L., B. A. Cosgrove, M. B. Ek, et al., 2013a: Overview of the North American Land Data Assimilation System (NLDAS). Land Surface Observation, Modeling and Data Assimilation, S. L. Liang, X. Li, and X. H. Xie, Eds., World Scientific, Hackensack NJ, 337–377.
[224] Xia, Y. L., M. B. Ek, J. Sheffield, et al., 2013b: Validation of Noah-simulated soil temperature in the North American Land Data Assimilation System phase 2. J. Appl. Meteor. Climatol., 52, 455–471. doi: 10.1175/JAMC-D-12-033.1
[225] Xia, Y. L., M. B. Ek, D. Mocko, et al., 2014a: Uncertainties, correlations, and optimal blends of drought indices from the NLDAS multiple land surface model ensemble. J. Hydrometeor., 15, 1636–1650. doi: 10.1175/JHM-D-13-058.1
[226] Xia, Y. L., M. B. Ek, C. D. Peters-Lidard, et al., 2014b: Application of USDM statistics in NLDAS-2: Optimal blended NLDAS drought index over the continental United States. J. Geophys. Res. Atmos., 119, 2947–2965. doi: 10.1002/2013JD020994
[227] Xia, Y. L., M. T. Hobbins, Q. Z. Mu, et al., 2015a: Evaluation of NLDAS-2 evapotranspiration against tower flux site observations. Hydrol. Process., 29, 1757–1771. doi: 10.1002/hyp.10299
[228] Xia, Y. L., M. B. Ek, Y. H. Wu, et al., 2015b: Comparison of NLDAS-2 simulated and NASMD observed daily soil moisture. Part I: Comparison and analysis. J. Hydrometeor., 16, 1962–1980. doi: 10.1175/JHM-D-14-0096.1
[229] Xia, Y. L., D. M. Mocko, M. Huang, et al., 2017: Comparison and assessment of three advanced land surface models in simulating terrestrial water storage components over the United States. J. Hydrometeor., 18, 625–649. doi: 10.1175/JHM-D-16-0112.1
[230] Xia, Y. L., D. M. Mocko, S. G. Wang, et al., 2018: Comprehensive evaluation of the variable infiltration capacity (VIC) mo-del in the North American Land Data Assimilation System. J. Hydrometeor., 17, 1853–1879. doi: 10.1175/JHM-D-18-0139.1
[231] Xiao, J. F., J. Q. Chen, K. J. Davis, et al., 2012: Advances in upscaling of eddy covariance measurements of carbon and water fluxes. J. Geophys. Res. Biogeo., 117, G00J01. doi: 10.1029/2011JG001889
[232] Xie, Y., P. X. Wang, X. J. Bai, et al., 2017: Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model. Agric. Forest Meteor., 246, 194–206. doi: 10.1016/j.agrformet.2017.06.015
[233] Xu, T. R., S. L. Liang, and S. M. Liu, 2011: Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter. J. Geophys. Res. Atmos., 116, D09109. doi: 10.1029/2010JD015150
[234] Xu, T. R., S. M. Liu, Z. W. Xu, et al., 2015: A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature. Sci. China Earth Sci., 58, 211–230. doi: 10.1007/s11430-014-4964-7
[235] Xu, T. R., Z. X. Guo, S. M. Liu, et al., 2018: Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. J. Geophysics. Res. Atmos., 123, 8674–8690. doi: 10.1029/2018JD028447
[236] Xu, T. R., X. L. He, S. M. Bateni, et al., 2019: Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites. Remote Sens. Environ., 221, 444–461. doi: 10.1016/j.rse.2018.11.023
[237] Yang, D. Q., B. E. Goodison, J. R. Metcalfe, et al., 1998: Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 54–68. doi: 10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2
[238] Yang, D. Q., D. Kane, Z. P. Zhang, et al., 2005: Bias corrections of long-term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501. doi: 10.1029/2005GL024057
[239] Yang, F., H. Lu, K. Yang, et al., 2017: Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China. Hydrol. Earth Syst. Sci., 21, 5805–5821. doi: 10.5194/hess-21-5805-2017
[240] 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 Ser. II, 85, 229–242.
[241] Yang, K., T. Koike, I. Kaihotsu, et al., 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
[242] Yang, K., L. Zhu, Y. Y. Chen, et al., 2016: Land surface model calibration through microwave data assimilation for improving soil moisture simulations. J. Hydrol., 523, 266–276. doi: 10.1016/j.jhydrol.2015.12.018
[243] Yang, R. Q., K. Mitchell, J. Meng, et al., 2011: Summer-season forecast experiments with the NCEP Climate Forecast System using different land models and different initial land states. J. Climate, 24, 2319–2334. doi: 10.1175/2010JCLI3797.1
[244] Yilmaz, M. T., W. T. Crow, M. C. Anderson, et al., 2012: An objective methodology for merging satellite- and model-based soil moisture products. Water Resour. Res., 48, W11502. doi: 10.1029/2011WR011682
[245] Yu, Y. Y., D. Tarpley, J. L. Privette, et al., 2009: Developing algorithm for operational GOES-R land surface temperature product. IEEE Trans. Geosci. Remote Sens., 47, 936–951. doi: 10.1109/TGRS.2008.2006180
[246] Yuan, X., P. Ji, L. Y. Wang, et al., 2018: High-resolution land surface modeling of hydrological changes over the Sanjiangyuan region in the eastern Tibetan Plateau: 1. Model development and evaluation. J. Adv. Model. Earth Syst., 10, 2806–2828. doi: 10.1029/2018MS001412
[247] Zaitchik, B. F., M. Rodell, and F. Olivera, 2010: Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme. Water Resour. Res., 46, W06507. doi: 10.1029/2009WR007811
[248] Zhang, K., J. S. Kimball, and S. W. Running, 2016: A review of remote sensing based actual evapotranspiration estimation. WIREs Water, 3, 834–853. doi: 10.1002/wat2.1168
[249] Zhang, T. P., P. W. Stackhouse, S. K. Gupta, et al., 2013: The validation of the GEWEX SRB surface shortwave flux data products using BSRN measurements: A systematic quality control, production and application approach. J. Quant. Spectrosc. Radiat. Transfer, 122, 127–140. doi: 10.1016/j.jqsrt.2012.10.004
[250] Zhang, T. P., P. W. Stackhouse, J. S. Gupta, et al., 2015: The validation of the GEWEX SRB surface longwave flux data products using BSRN measurements. J. Quant. Spectrosc. Radiat. Transfer, 150, 134–147. doi: 10.1016/j.jqsrt.2014.07.013
[251] Zheng, H., and Z. L. Yang, 2016: Effects of soil-type datasets on regional terrestrial water cycle simulations under different climatic regimes. J. Geophys. Res. Atmos., 121, 14,387–14,402. doi: 10.1002/2016JD025187