National Aeronautics and Space Administration

Documentation of the SMAP Level-4 products can be found here.

A complete archive of GMAO publications can be found here.

The following list is a selection of peer-reviewed articles that are directly relevant to the SMAP Level-4 algorithms and data products:

Colliander, A., R.H. Reichle, W.T. Crow, M.H. Cosh, F. Chen, S. Chan, N. Das, R. Bindlish, J. Chaubell, S.B. Kim, Q. Liu, P. O'Neill, R.S. Dunbar, L. Dang, J. Kimball, T.J. Jackson, H.K. al Jassar, J. Asanuma, B. K. Bhattacharya, A. Berg, D.D. Bosch, L. Bourgeau-Chavez, T. Caldwell, J-C. Calvet, C. Holifield Collins, K.H. Jensen, S. Livingston, E. Lopez-Baeza, J. Martínez-Fernández, H. McNairn, M. Moghaddam, C. Montzka, C. Notarnicola, T. Pellarin, I. Pfeil, J. Pulliainen, J. Ramos, M. Seyfried, P. Starks, Z. Su, Rogier van der Velde, Yijian Zeng, M. Thibeault, M. Vreugdenhil, J.P. Walker, M. Zribi, D. Entekhabi, and S. Yueh, 2022. Validation of Soil Moisture Data Products from the NASA SMAP Mission. IEEE JSTARS, 15, 364-392. doi: 10.1109/JSTARS.2021.3124743.

Beck, H. E., M. Pan, D. Miralles, R. H. Reichle, W. Dorigo, S. Hahn, J. Sheffield, L. Karthikeyan, G. Balsamo, R. M. Parinussa, A. I. J. M. VanDijk N. Vergopolan, and E. F. Wood, 2021. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrology and Earth System Sciences, 25, 17-40. doi:10.5194/hess-25-17-2021.

Brust, C., J. S. Kimball, M. P. Maneta, K. Jencso, M. He, and R. H. Reichle, 2021. Using SMAP Soil Moisture to Constrain MOD16 Evapotranspiration Over the Contiguous USA. Remote Sensing of Environment, 255, 112277. doi: 10.1016/j.rse.2020.112277.

Brust, C., J. S. Kimball, M. P. Maneta, K. Jensco, and R. H. Reichle, 2021. DroughtCast: A Machine Learning Forecast of the United States Drought Monitor. Frontiers in Big Data, 4, 114. doi:10.3389/fdata.2021.773478.

Crow, W. T., R. H. Reichle, and J. Dong, 2021. Expanding the application of soil moisture monitoring systems through regression-based transformation. J. Hydrometeorology, 22, 10, 2601-2615. 10.1175/JHM-D-21-0061.1.

Felsberg, A., G. J. M. De Lannoy, M. Girotto, J. Poeseny, R. H. Reichle, and T. Stanley, 2021. Global soil water estimates as landslide predictor: the effectiveness of SMOS, SMAP and GRACE observations, land surface simulations and data assimilation. J. Hydrometeor., 22, 1065-1084. doi:10.1175/JHM-D-20-0228.1.

Madani, N., N. C. Parazoo, J. S. Kimball, R. H. Reichle, A.Chatterjee, J. D. Watts, S. Saatchi, Z. Liu, A. Endsley, T. Tagesson, B. M. Rogers, A. Xu, J. A. Wang, T. Magney, and C. E. Miller, 2021. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska. J. Geophys. Res. - Biogeosci., 126, e2020JG006078. doi: 10.1029/2020jg006078.

Peng, J., C. Albergel, A. Balenzano, L. Brocca, O. Cartus, M. Cosh, W. Crow, et al., 2021. A roadmap for high-resolution satellite soil moisture applications - confronting product characteristics with user requirements. Remote Sensing of Environment, 252, 112162. doi:10.1016/j.rse.2020.112162.

Qiu, J., J. Dong, W. T. Crow, X. Zhang, R. H. Reichle, and G. J. M. De Lannoy, 2021. The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis over mainland China. Hydrology and Earth System Sciences, 25, 3, 1569-1586. doi: 10.5194/hess-25-1569-2021.

Reichle, R. H., Q. Liu, J. V. Ardizzone, W. T. Crow, G. J. M. De Lannoy, J. Dong, J. S. Kimball, and R. D. Koster, 2021. The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product. J. Hydrometeorology, 22, 405-424. doi:10.1175/JHM-D-20-0217.1.

Reichle, R. H., S. Q. Zhang, Q. Liu, C. S. Draper, J. Kolassa, and R. Todling, 2021. Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System. IEEE JSTARS, 14, 10628-10643. doi:10.1109/JSTARS.2021.3118595.

Seo, E., M.-I. Lee and R. H. Reichle, 2021. Assimilation of SMAP and ASCAT Soil Moisture Retrievals into the JULES Land Surface Model Using the Local Ensemble Transform Kalman Filter. Remote Sensing of Environment, 253, 112222. doi:10.1016/j.rse.2020.112222.

Bechtold, M., G. De Lannoy, R. H. Reichle, D. Roose, N. Balliston, I. Burdun, K. Devito, J. Kurbatova, T. Munir, and E. Zarov, 2020. Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. Remot Sens. Environ., 246, 111805. doi:10.1016/j.rse.2020.111805.

Endsley, K. A., J. S. Kimball, R. H. Reichle, and J. D. Watts, 2020. Satellite monitoring of global surface soil organic carbon dynamics using the SMAP Level 4 Carbon product. Journal of Geophysical Research - Biogeoscience, 125, e2020JG006100. doi:10.1029/2020JG006100.

Gruber, A., G. De Lannoy, C. Albergel, A. Al-Yaari, L. Brocca, J.-C. Calvet, A. Colliander, M. Cosh, W. Crow, W. Dorigo, C. Draper, M. Hirschi, Y. Kerr, A. Konings, W. Lahoz, C. McColl, C. Montzka, J. Munoz Sabater, J. Peng, R. Reichle, P. Richaume, C. Rudiger, T. M. Scanlon, R. van der Schalie, and W. Wagner, 2020. Validation practices for satellite soil moisture products: What are (the) errors?. Remote Sens. Environ., 244, 111806. doi: 10.1016/j.rse.2020.111806.

Kolassa, J., R. H. Reichle, R. Koster, Q. Liu, S. Mahanama, and F. Zeng, 2020. An observation-driven approach to improve vegetation phenology in a global land surface model. J. Adv. Model. Earth Sys, 12, e2020MS002083. doi: 10.1029/2020MS002083.

Li, X., J. Xiao, J. S. Kimball, R. H. Reichle, R. L. Scott, M. E. Litbak, G. Bohrer, and C. Frankenberg, 2020. Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought. Remot Sens. Environ., 251, 112062. doi: 10.1016/j.rse.2020.112062.

Liu, Z., J. S. Kimball, N. C. Parazoo, A. P. Ballantyne, W. J. Wang, N. Madan, C. G. Pan, J. D. Watts, R. H. Reichle, O. Sonnentag, P. Marsh, M. Hurkuck, M. Helbig, W. Quinton, D. Zona, M. Ueyama, H. Kobayashi, and E. S. Euskirchen, 2020. Increased high-latitude photosynthetic carbon gain during an anomalously warm spring offset by respiration carbon loss during preceding winter. Global Change Bio., 26, 682-696. doi: 10.1111/gcb.14863.

Madani, N., J. S. Kimball, N. C. Parazoo, A. P. Ballantyne, T. Tagesson, L. A. Jones, R. H. Reichle, P. I. Palmer, I. Velicogna, A. A. Bloom, S. Saatchi, Z. Liu, and G.A, 2020. Below-surface water mediates the response of African forests to reduced rainfall. Environ. Res. Lett., 15, 034063. doi:10.1088/1748-9326/ab724a.

Madani, N., N. C. Parazoo, J. S. Kimball,A. P. Ballantyne, R. H. Reichle, M. Maneta, S. Saatchi, P. I. Palmer, Z. Liu, and T. Tagesson, 2020. Recent Amplified Global Gross Primary Productivity Due to Temperature Increase is Offset by Reduced Productivity Due to Water Constraints. AGU Advances, 1, e2020AV000180. doi:10.1029/2020AV000180.

Park, J., B. Forman, R. H. Reichle, G. De Lannoy, and S. Tarik, 2020. Evaluation of GEOS L-Band Microwave Brightness Temperature using Aquarius Observations over Non-Frozen Land across North America. Remote Sensing, 12, 3098. doi: 10.3390/rs12183098.

Colliander, A., Z. Yang, R. Mueller, A. Sandborn, R. H. Reichle, W. Crow, D. Entekhabi, and S. Yueh, 2019. Consistency between NASS Surveyed Soil Moisture Conditions and SMAP Soil Moisture Observations. Water Resources Research, 55, 7682-7693. doi:10.1029/2018WR024475.

Crow, W. T., F. Chen, R. H. Reichle, and Y. Xia, 2019. Diagnosing bias in modeled soil moisture/runoff coupling strength using the SMAP Level 4 soil moisture product. Water Resour. Res., 55, 7010-7026. doi:10.1029/2019WR025245.

Dong, J., W. Crow, R. H. Reichle, Q. Liu, F. Lei, and M. H. Cosh, 2019. A global assessment of added value in the SMAP Level-4 soil moisture product relative to its baseline land surface model. Geophys. Res. Lett., 46, 6604-6613. doi:10.1029/2019GL083398.

Draper, C., and R. H. Reichle, 2019. Assimilation of satellite soil moisture for improved atmospheric reanalyses. Mon. Wea. Rev, 147, 2163-2188. doi:10.1175/MWR-D-18-0393.1.

Girotto, M., R. H. Reichle, M. Rodell, Q. Liu, S. Mahanama, and G. J. M. De Lannoy, 2019. Multi-sensor Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Soil Moisture and Shallow Groundwater Estimation. Remote Sens. Environ., 227, 12-27. doi: 10.1016/j.rse.2019.04.001.

Koster, R. D., R. H. Reichle, S. D. Schubert, and S. P. Mahanama, 2019. Length Scales of Hydrological Variability as Inferred from SMAP Soil Moisture Retrievals. J. Hydrometeorol., 20, 2129-2146. doi:10.1175/JHM-D-19-0070.1.

Reichle, R. H., Q. Liu, R. d. Koster, W. T. Crow, G. J. M. De Lannoy, J. S. Kimball, J. V. Ardizzone, D.Bosch, A. Colliander, M. Cosh, J. Kolassa, S. P. Mahanama, H. McNairn, J. Prueger, P. Starks, and J. P. Walker, 2019. Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. J. Adv. Model. Earth Sys., 11, 3106-3130. doi:10.1029/2019MS001729.

Balsamo, G., A. Agusti-Panareda, C. Alberge, G. Arduini, A. Beljaars, J. Bidlot, N. Bousserez, S. Boussetta, A. Brown, R. Buizza, C. Buontempo, F. Chevallier, M. Choulga, H. Cloke, M. F. Cronin, M. Dahoui, P. De Rosnay, P. A. Dirmeyer, et. al., 2018. Satellite and in situ observations for advancing global Earth surface modelling: a review. Rem. Sens., 10, 2038. doi:10.3390/rs10122038.

Crow, W. T., F. Chen, R. H. Reichle, Y. Xia, and Q. Liu, 2018. Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture-runoff coupling in land surface models. Geophy. Res. Lett, 45, 4869-4878. doi:10.1002/2018GL077193.

Kolassa, J., R.H. Reichle, Q. Liu , S.H. Alemohammad, P. Gentine , K.Aida, J. Asanuma, S. Bircher , T. Caldwell , A. Colliander, M. Cosh, C. Holifield Collins, T.J. Jackson, K. H. Jensen , J. Martínez-Fernández , H. McNairn, A. Pacheco, M. Thibeault, and J.P. Walker, 2018. Estimating surface soil moisture from SMAP observations using a Neural Network approach. Remote Sens. Environ, 204, 43-59. doi:10.1016/j.rse.2017.10.045.

Koster, R. D., Q. Liu, S. P. P. Mahanama, and R. H. Reichle, 2018. Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation. J. Hydrometeor., 19, 727-741. doi:10.1175/JHM-D-17-0228.1.

Chen, F., W. T. Crow, A. Colliander, M. Cosh, T. J. Jackson, R. Bindlish, R. H. Reichle, S. K. Chan, D. D. Bosch, P. J. Starks, D. C. Goodrich, and M. Seyfried, 2017. Application of Triple Collocation in Ground-based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products. IEEE J. Sel. Top. Appl, 10, 489-502. doi:10.1109/JSTARS.2016.2569998.

Crow, W. T., F.Chen, R. H. Reichle, and Q. Liu, 2017. L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting. Geophys. Res. Lett., 44, 11, 5495-5503. doi:10.1002/2017GL073642.

Jones, L. A., J. S. Kimball, R. H. Reichle, N.Madani, J. Glassy, J. V. Ardizzone, A. Colliander, J. Cleverly, D. Eamus, E. Euskirchen, L. Hutley, C. Macfarlane, and R. Scott, 2017. The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO₂ Exchange | IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 55, 11, 6517-6532. doi:10.1109/TGRS.2017.2729343.

Kolassa, J., R. H. Reichle, and C. S. Draper, 2017. Merging active and passive microwave observations in soil moisture data assimilation. Remote Sens Environ, 191, 117-130. doi:10.1016/j.rse.2017.01.015.

Kolassa, J., R. H. Reichle, Q. Liu, M. Cosh, D. D. Bosch, T. G. Caldwel, A. Colliander, C. H. Colins, S. J. Livingston, M. Moghaddam, and P. J. Starks, 2017. Data Assimilation to extract Soil Moisture Information from SMAP Observations. Remote Sensing, 9, 11, 1179. DOI: 10.3390/rs9111179.

Koster, R. D., R. H. Reichle, and S. P. Mahanama, 2017. A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture. Journal of Hydrometeorology, 18, 837-843. doi:10.1175/JHM-D-16-0285.1.

Lievens, H., B. Martens, N. Verhoest, S. Hahn, R. H. Reichle, and D. Miralles, 2017. Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates. Remote Sens Environ, 189, 194-210. doi:10.1016/j.rse.2016.11.022.

Lievens, H., R. H. Reichle, Q. Liu, G. J. M. De Lannoy, R. S. Dunbar, S. B. Kim, N. N. Das, M. Cosh, J. P. Walker, and W. Wagner, 2017. Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophys. Res. Lett, 44, 12, 6145–6153. DOI: 10.1002/2017GL073904.

Reichle, R., Q. Liu, R. Koster, C. Draper, S. Mahanama, and G. Partyka, 2017. Land Surface Precipitation in MERRA-2. J. Clim, 30, 5, 1643-1664. doi:10.1175/JCLI-D-16-0570.1.

Reichle, R. H., G. De Lannoy, Q. Liu, J. V. Ardizzone, A. Colliander, A. L. Conaty, W. Crow, T. Jackson, L. Jones, J. Kimball, R. D. Koster, S. P. Mahanama, E. B. Smith, A. Berg, S. Bircher, D. Bosch, T. Caldwell, M. Cosh, A. Gonzáez-Zamora, C. Holifield Collins, K. Jensen, S. Livingston, E. Lopez-Baeza, J. Martínez-Fernández, H. McNairn, M. Moghaddam, A. Pacheco, T. Pellarin, J. Prueger, T. Rowlandson, M. Seyfried, P. Starks, Z. Su, M. Thibeault, R. van der Velde, X. Wu, and Y. Zeng, 2017. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements. J. Hydrometeorl, 18, 2621-2645. doi:10.1175/JHM-D-17-0063.1.

Reichle, R. H., G. J. M. De Lannoy, Q. Liu, R. D. Koster, J. S. Kimball, W. T. Crow, J. V. Ardizzone, P. Chakraborty, D. W. Collins, A. L. Conaty, M. Girotto, L. A. Jones, J. Kolassa, H. Lievens, R. A. Lucchesi, and E. B. Smith, 2017. Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics. J. Hydrometeorol, 18, 3217-3237. doi:10.1175/JHM-D-17-0130.1.

De Lannoy, G. J. M., and R. H. Reichle, 2016. Global Assimilation of Multi-Angular SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation. J. Hydrometeorol, 17, 669-691. doi: 10.1175/JHM-D-15-0037.1.

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 Sc, 20, 4895-4911. doi:10.5194/hess-20-4895-2016.

De Lannoy, G. J. M., R. H. Reichle, J. Peng, Y. Kerr, R. Castro, E. Kim, and Q. Liu, 2015. Converting Between SMOS and SMAP Level-1 Brightness Temperature Observations Over Nonfrozen Land. IEEE Geosci. Remote Sens. Lett, 12, 1908-1912. doi: 10.1109/LGRS.2015.2437612.

Draper, C. S., and R. H. Reichle, 2015. The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual time scales. Hydrol. Earth Syst. Sc, 19, 4831-4844. doi: 10.5194/hess-19-4831-2015.

Farhadi, L., R. H. Reichle, G. J. M. De Lannoy, and J. S. Kimball, 2015. Assimilation of freeze/thaw observations into the NASA catchment land surface model. J. Hydrometeorol., 16, 730-743. doi: 10.1175/JHM-D-14-0065.1.

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.

De Lannoy, G. J. M., R. Koster, R. H. Reichle, S. Mahanama, and Q. Liu, 2014. An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. J. Adv. Model. Earth Sys, 6, 957-979. doi: 10.1002/2014MS000330.

Reichle, R. H., 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.

Yi, Y., J. Kimball, and R. H. Reichle, 2014. Spring hydrology determines summer net carbon uptake in northern ecosystems. Environ. Res. Lett, 9, 064003. doi: 10.1088/1748-9326-9-6-064003.

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 Land Using SMOS Observations. J. Hydrometeorol., 14, 765-785. doi: 10.1175/JHM-D-12-092.1.

Draper, C., R. Reichle, R. de Jeu, V. Naeimi, R. Parinussa, and W. Wagner, 2013. Estimating root mean square errors in remotely sensed soil moisture over continental scale domains. Remote Sens. Environ., 137, 288-298. DOI:10.1016/j.rse.2013.06.013.

Yi, Y., J. S. Kimball, L. A. Jones, R. H. Reichle, R. R. Nemani, and H. A. Margolis, 2013. Recent climate and fire disturbance impacts on boreal and arctic ecosystem productivity estimated using a satellite-based terrestrial carbon flux model. J. Geophys. Res.-Biogeo., 118, 1-17. doi: 10.1002/jgrg.20053.

Entekhabi, D., Njoku,E. , O'Neill,P., Kellog,K., Crow, W., Edelsteing,W., Entin,J., Goodman,S., Jackson,T., Johnson,J., Kimball,J., Piepmeier,J., Koster,R., McDonald,K., Moghaddam,M., Moran,S., Reichle,R., el al., 2010. The Soil Moisture Active and Passive (SMAP) Mission. Proceedings of the IEEE, 98, 704-716. doi:10.1109/JPROC.2010.2043918.

Entekhabi, D., Reichle, R., Koster, R., and Crow, W., 2010. Performance Metrics for Soil Moisture Retrievals and Application Requirements. J. Hydrometeorol., 11, 832-840. doi:10.1175/2010JHM1223.1.