{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:20:07Z","timestamp":1760242807981,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2016,11,30]],"date-time":"2016-11-30T00:00:00Z","timestamp":1480464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005808","name":"IIT Bombay","doi-asserted-by":"publisher","award":["Project 15IRCCSG016"],"award-info":[{"award-number":["Project 15IRCCSG016"]}],"id":[{"id":"10.13039\/501100005808","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM) and the Soil Moisture Operational Products System (SMOPS) observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb) using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM) maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS) using the Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS) SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD) of 0.0303 m3\/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3\/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb) at an incidence angle of 55\u00b0 using the Community Microwave Emission Model (CMEM) Radiative transfer Model (RTM). This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA) brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.<\/jats:p>","DOI":"10.3390\/rs8120976","type":"journal-article","created":{"date-parts":[[2016,11,30]],"date-time":"2016-11-30T10:10:36Z","timestamp":1480500636000},"page":"976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture"],"prefix":"10.3390","volume":"8","author":[{"given":"Akhilesh","family":"Nair","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India"}]},{"given":"J.","family":"Indu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kumar, S.V., Harrison, K.W., Peters-Lidard, C.D., Santanello, J.A., and Kirschbaum, D. (2014). Assessing the impact of L-band observations on drought and flood risk estimation: a decision-theoretic approach in an OSSE environment. J. Hydrometeorol.","DOI":"10.1175\/JHM-D-13-0204.1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Koster, R.D., Suarez, M.J., Liu, P., Jambor, U., Berg, A., Kistler, M., Reichle, R., Rodell, M., and Famiglietti, J. (2004). Realistic initialization of land surface states: Impacts on subseasonal Forecast skill. J. Hydrometeorol.","DOI":"10.1175\/JHM-387.1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H., Koren, V., Duan, Q.Y., Ek, M., and Betts, A. (1996). Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res.","DOI":"10.1029\/95JD02165"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Koren, V., Schaake, J., Mitchell, K., Duan, Q.Y., Chen, F., and Baker, J.M. (1999). A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res.","DOI":"10.1029\/1999JD900232"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1175\/BAMS-84-8-1013","article-title":"The common land model","volume":"84","author":"Dai","year":"2003","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","unstructured":"Xu, L., Dennis, P.L., Eric, F.W., and Burges, J.S. (1994). A simple hydrological based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res."},{"key":"ref_7","unstructured":"Randal, D.K., and Max, J.S. (1996). Energy and Water Balance Calculations in the Mosaic LSM, National Aeronautics and Space Administration."},{"key":"ref_8","unstructured":"Rolf, H.R., McLaughlin, D.B., and Entekhabi, D. (2002). Hydrological data assimilation with the ensemble Kalman Filter. J. Hydrometeorol."},{"key":"ref_9","unstructured":"Daley, R. (1991). Atmospheric Data Analysis, Cambridge University Press."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bennett, A.F. (1992). Inverse Methods in Physical Oceanography, Cambridge University Press.","DOI":"10.1017\/CBO9780511600807"},{"key":"ref_11","unstructured":"Dennis, M. (2002). An integrated approach to hydrological data assimilation: Interpolation, smoothing, and filtering. Adv. Water Resour."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11761","DOI":"10.1029\/2001JD900149","article-title":"A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations","volume":"106","author":"Walker","year":"2001","journal-title":"J. Geophys. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Walker, J.P., Garry, W.R., and Kalma, J.D. (2002). Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application. Water Resour. Res.","DOI":"10.1029\/2002WR001545"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Che, T., Li, X., Jin, R., and Huang, C. (2014). Assimilating passive microwave remote sensing data into a land surface model to improve the estimation of snow depth. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2013.12.009"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dunne, S., and Entekhabi, D. (2005). An ensemble-based reanalysis approach to land data assimilation. Water Resour. Res.","DOI":"10.1029\/2004WR003449"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhu, L., Chen, Y., Zhao, L., Qin, J., Lu, H., Tang, W., Han, M., Ding, B., and Fang, N. (2015). Land surface model calibration through microwave data assimilation for improving soil moisture simulations. J. Hydrol.","DOI":"10.1016\/j.jhydrol.2015.12.018"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The global land data assimilation system","volume":"3","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TGRS.2002.808243","article-title":"Soil moisture retrieval from AMSR-E","volume":"41","author":"Njoku","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/TGRS.2009.2037749","article-title":"WindSat global soil moisture retrieval and validation","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2014.04.006","article-title":"Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to land data assimilation system estimates","volume":"149","author":"Wigneron","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9965","DOI":"10.3390\/s120809965","article-title":"Validation of SMOS soil moisture products over the Maqu and Twente Regions","volume":"12","author":"Dente","year":"2012","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1109\/TGRS.2011.2168533","article-title":"Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the U.S.","volume":"50","author":"Jackson","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2012.2186971","article-title":"Validation of the SMOS L2 soil moisture data in the REMEDHUS Network (Spain)","volume":"50","author":"Sanchez","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Rolf, H.R. (2008). Data assimilation methods in the Earth sciences. Adv. Water Resour."},{"key":"ref_25","unstructured":"Zhan, X., Liu, J., Zhao, L., and Jensen, K. (2011). Soil Moisture Operational Product System (SMOPS) Algorithm Theoretical Basis Document, NOAA NESDIS STAR."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Owe, M., de Jeu, R., and Holmes, T. (2008). Multisensor historical climatology of satellite-derived global land surface moisture. J. Geophys. Res.","DOI":"10.1029\/2007JF000769"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"R\u00fcdiger, C., Calvet, J., Gruhier, C., Holmes, T.R.H., de Jeu, R.A.M., and Wagner, W. (2009). An intercomparison of ERS-Scat and AMSR-E soil moisture observations with model simulations over France. J. Hydrometeorol.","DOI":"10.1175\/2008JHM997.1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Derber, J.C., Parrish, D.F., and Lord, S.J. (1991). The new global operational analysis system at the national meteorological center. Weather Forecast.","DOI":"10.1175\/1520-0434(1991)006<0538:TNGOAS>2.0.CO;2"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chempeaux, J.L., Masson, V., and Chauvin, F. (2005). ECOCLIMAP: A global database of land surface parameters at 1 km resolution. Meteorol. Appl.","DOI":"10.1017\/S1350482705001519"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, K., Qin, J., Zhao, L., Chen, Y., Tang, W., Han, M., Chen, Z., Lv, N., Ding, B., Wu, H., and Lin, C. (2013). A multi-scale soil moisture and freeze-thaw monitoring network on the third pole. Bull. Am. Meteorol. Soc.","DOI":"10.1175\/BAMS-D-12-00203.1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/j.envsoft.2005.07.004","article-title":"Land Information system\u2014An interoperable framework for high resolution land surface modeling","volume":"21","author":"Kumar","year":"2006","journal-title":"Environ. Model. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Peters-Lidard, C.D., Houser, P.R., Tian, Y., Kumar, S.V., Geiger, J., Olden, S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., and Mitchell, K. (2007). High-performance Earth system modeling with NASA\/GSFC\u2019s land information system. Innov. Syst. Softw. Eng.","DOI":"10.1007\/s11334-007-0028-x"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mahrt, L., and Pan, H. (1984). A Two-Layer model of soil hydrology. Boundary-Layer Meteorol.","DOI":"10.1007\/BF00119116"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ek, M.B., Mitchell, K.E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J.D. (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res.","DOI":"10.1029\/2002JD003296"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mahrt, L., and Ek, M. (1984). The influence of atmospheric stability on potential evaporation. J. Clim. Appl. Meteorol.","DOI":"10.1175\/1520-0450(1984)023<0222:TIOASO>2.0.CO;2"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pan, H.L., and Mahrt, L. (1987). Interaction between soil hydrology and boundary-layer development. Boundary-Layer Meteorol.","DOI":"10.1007\/BF00121563"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rodell, M., Houser, P.R., Berg, A.A., and Famiglietti, J.S. (2005). Evaluation of 10 methods for initializing a land surface model. J. Hydrometeorol.","DOI":"10.1175\/JHM414.1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, Z.L., Dickinson, R.E., Henderson-Sellers, A., and Pitman, A.J. (1995). Preliminary study of spin-up processes in land surface models with the first stage data project for Intercomparison of Land Surface Parameterization Schemes Phase 1(a). J. Geophys. Res.","DOI":"10.1029\/95JD01076"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Margulis, S.A., McLaughlin, D., Entekhabi, D., and Dunne, S. (2002). Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 field experiment. Water Resour. Res.","DOI":"10.1029\/2001WR001114"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4463","DOI":"10.5194\/hess-19-4463-2015","article-title":"Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes","volume":"19","author":"Kumar","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yin, J., Zhan, X., Zheng, Y., Liu, J., Fang, L., and Hain, C.R. (2015). Enhancing Model Skill by assimilating SMOPS blended soil moisture product into Noah land surface model. J. Hydrometeorol.","DOI":"10.1175\/JHM-D-14-0070.1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Reichle, R.H., and Koster, R.D. (2003). Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J. Hydrometeorol.","DOI":"10.1175\/1525-7541(2003)004<1229:ATIOHE>2.0.CO;2"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.advwatres.2008.01.013","article-title":"A land surface data assimilation framework using the land information system: Description and applications","volume":"31","author":"Kumar","year":"2008","journal-title":"Adv. Water Resour."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Drusch, M., Holmes, T., de Rosnay, P., and Balsamo, G. (2009). Comparing ERA-40-based L-band brightness Temperature with Skylab Observations: A calibration\/validation study using the community microwave emission model. J. Hydrometeorol.","DOI":"10.1175\/2008JHM964.1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.rse.2006.10.014","article-title":"L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields","volume":"107","author":"Wigneron","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"de Rosnay, P., Drusch, M., Boone, A., Balsamo, G., Decharme, B., Harris, P., Kerr, Y., Pellarin, T., Polcher, J., and Wigneron, J.P. (2009). AMMA land surface model intercomparison experiment coupled to the Community Microwave Emission Model: ALMIP-MEM. J. Geophys. Res.","DOI":"10.1029\/2008JD010724"},{"key":"ref_47","unstructured":"Jia, B., and Xie, Z. (2015). Evaluation of the community microwave emission model coupled with the community land model over East Asia. Atmos. Ocean. Sci. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2015.01.013","article-title":"An assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast Asia","volume":"160","author":"Cho","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pellarin, T., Calvet, J., and Wagner, W. (2006). Evaluation of ERS scatterometer soil moisture products over a half-degree region in southwestern France. Geophys. Res. Lett.","DOI":"10.1029\/2006GL027231"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bi, H., Ma, J., Zheng, W., and Zeng, J. (2016). Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau. J. Geophys. Res. Atm.","DOI":"10.1002\/2015JD024131"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Taylor, K.E. (2001). Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res.","DOI":"10.1029\/2000JD900719"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7755","DOI":"10.1029\/97JC03180","article-title":"Towards the true near-surface wind speed: Error modeling and calibration using triple collocation","volume":"103","author":"Stoffelen","year":"1998","journal-title":"J. Geophys. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"McColl, K.A., Vogelzang, J., Konings, A.G., Entekhabi, D., Piles, M., and Stoffelen, A. (2014). Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target. Geophys. Res. Lett.","DOI":"10.1002\/2014GL061322"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Su, C., Ryu, D., Crow, W.T., and Western, A.W. (2014). Beyond triple collocation: Applications to soil moisture monitoring. J. Geophys. Res. Atmos.","DOI":"10.1002\/2013JD021043"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tian, X., Xie, Z., Dai, A., Jia, B., and Shi, C. (2010). A microwave land data assimilation system: Scheme and preliminary evaluation over China. J. Geophys. Res.","DOI":"10.1029\/2010JD014370"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Jia, B., Tian, X., Xie, Z., Liu, J., and Shi, C. (2013). Assimilation of microwave brightness temperature in land data assimilation system with multi-observation operators. J. Geophys. Res. Atmos.","DOI":"10.1002\/jgrd.50377"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"de Lannoy, G.J.M., Reichle, R.H., and Pauwels, V.R.N. (2013). Global calibration of the GEOS-5 L-band microwave radiative transfer model over nonfrozen land using SMOS observations. J. Hydrometeorol.","DOI":"10.1175\/JHM-D-12-092.1"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1016\/j.rse.2011.08.003","article-title":"Soil Moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe","volume":"115","author":"Brocca","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"687","DOI":"10.5194\/hess-13-687-2009","article-title":"Some pratical notes on the land surface modeling in the Tibetan Plateau","volume":"13","author":"Yang","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, K., Qin, J., Zhao, L., Tang, W., and Han, M. (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.","DOI":"10.1002\/jgrd.50301"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Reynolds, C.A., Jackson, T.J., and Rawls, W.J. (2000). Estimating soil water-holding capacities by linking the Food And Agriculture Organization soil map of the world with global pedon database and continuous pedotransfer functions. Water Resour. Res.","DOI":"10.1029\/2000WR900130"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.5194\/hess-14-2177-2010","article-title":"Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France","volume":"14","author":"Albergel","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_63","unstructured":"Wei, S., Dai, Y., Liu, B., Zhu, A., Duan, Q., Wu, L., Ji, D., Ye, A., Yuan, H., Zhang, Q., and Chen, D. (2013). A China data set of soil properties for land surface modeling. J. Adv. Model. Earth Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/12\/976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:27:40Z","timestamp":1760210860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/12\/976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,30]]},"references-count":63,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["rs8120976"],"URL":"https:\/\/doi.org\/10.3390\/rs8120976","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,11,30]]}}}