IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Rolf H. Reichle aGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Rolf H. Reichle in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5513-0150
,
Qing Liu aGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
bScience Systems and Applications, Inc., Lanham, Maryland

Search for other papers by Qing Liu in
Current site
Google Scholar
PubMed
Close
,
Joseph V. Ardizzone aGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
bScience Systems and Applications, Inc., Lanham, Maryland

Search for other papers by Joseph V. Ardizzone in
Current site
Google Scholar
PubMed
Close
,
Wade T. Crow cHydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

Search for other papers by Wade T. Crow in
Current site
Google Scholar
PubMed
Close
,
Gabrielle J. M. De Lannoy dDepartment of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium

Search for other papers by Gabrielle J. M. De Lannoy in
Current site
Google Scholar
PubMed
Close
,
John S. Kimball eNTSG, University of Montana, Missoula, Montana

Search for other papers by John S. Kimball in
Current site
Google Scholar
PubMed
Close
, and
Randal D. Koster aGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Randal D. Koster in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.

Significance Statement

Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rolf H. Reichle, rolf.reichle@nasa.gov

Abstract

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.

Significance Statement

Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rolf H. Reichle, rolf.reichle@nasa.gov
Save
  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and Coauthors, 2018: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138.

    • Search Google Scholar
    • Export Citation
  • Arnold, N. P., W. M. Putman, and S. R. Freitas, 2020a: Impact of resolution and parameterized convection on the diurnal cycle of precipitation in a global nonhydrostatic model. J. Meteor. Soc. Japan, 98, 12791304, https://doi.org/10.2151/jmsj.2020-066.

    • Search Google Scholar
    • Export Citation
  • Arnold, N. P., W. M. Putman, S. R. Freitas, L. Takacs, and S. Rabenhorst, 2020b: Impacts of new atmospheric physics in the updated GEOS FP system (version 5.25). NASA GMAO Research Brief, 13 pp., https://gmao.gsfc.nasa.gov/researchbriefs/new_atmos_phys_GEOS-FP/new_atmos_phys_GEOS-FP.pdf.

  • Babaeian, E., M. Sadeghi, S. B. Jones, C. Montzka, H. Vereecken, and M. Tuller, 2019: Ground, proximal, and satellite remote sensing of soil moisture. Rev. Geophys., 57, 530616, https://doi.org/10.1029/2018RG000618.

    • Search Google Scholar
    • Export Citation
  • Balsamo, G., and Coauthors, 2018: Satellite and in situ observations for advancing global earth surface modelling: A review. Remote Sens., 10, 2038, https://doi.org/10.3390/rs10122038.

    • Search Google Scholar
    • Export Citation
  • Bauer-Marschallinger, B., and Coauthors, 2019: Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles. IEEE Trans. Geosci. Remote Sens., 57, 520539, https://doi.org/10.1109/TGRS.2018.2858004.

    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2017: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci., 21, 62016217, https://doi.org/10.5194/hess-21-6201-2017.

    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2021: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrol. Earth Syst. Sci., 25, 1740, https://doi.org/10.5194/hess-25-17-2021.

    • Search Google Scholar
    • Export Citation
  • Berg, A., B. R. Lintner, K. L. Findell, S. Malyshev, P. C. Loikith, and P. Gentine, 2014: Impact of soil moisture–atmosphere interactions on surface temperature distribution. J. Climate, 27, 79767993, https://doi.org/10.1175/JCLI-D-13-00591.1.

    • Search Google Scholar
    • Export Citation
  • Bircher, S., N. Skou, K. H. Jensen, J. P. Walker, and L. Rasmussen, 2012: A soil moisture and temperature network for SMOS validation in western Denmark. Hydrol. Earth Syst. Sci., 16, 14451463, https://doi.org/10.5194/hess-16-1445-2012.

    • Search Google Scholar
    • Export Citation
  • Bojinski, S., M. Verstraete, T. C. Peterson, C. Richter, A. Simmons, and M. Zemp, 2014: The concept of essential climate variables in support of climate research, applications, and policy. Bull. Amer. Meteor. Soc., 95, 14311443, https://doi.org/10.1175/BAMS-D-13-00047.1.

    • Search Google Scholar
    • Export Citation
  • Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie, 2012: EASE-Grid 2.0: Incremental but significant improvements for Earth-gridded data sets. ISPRS Int. J. Geoinf., 1, 3245, https://doi.org/10.3390/ijgi1010032.

    • Search Google Scholar
    • Export Citation
  • Brust, C., J. S. Kimball, M. P. Maneta, K. Jencso, M. He, and R. H. Reichle, 2021: Using SMAP level-4 soil moisture to constrain MOD16 evapotranspiration over the contiguous USA. Remote Sens. Environ., 255, 112277, https://doi.org/10.1016/j.rse.2020.112277.

    • Search Google Scholar
    • Export Citation
  • Carrera, M. L., B. Bilodeau, S. Bélair, M. Abrahamowicz, A. Russell, and X. Wang, 2019: Assimilation of passive L-band microwave brightness temperatures in the Canadian land data assimilation system: Impacts on short-range warm season numerical weather prediction. J. Hydrometeor., 20, 10531079, https://doi.org/10.1175/JHM-D-18-0133.1.

    • Search Google Scholar
    • Export Citation
  • Chan, S. K., and Coauthors, 2016: Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sens., 54, 49945007, https://doi.org/10.1109/TGRS.2016.2561938.

    • Search Google Scholar
    • Export Citation
  • Chan, S. K., E. G. Njoku, and A. Colliander, 2020: SMAP L1C radiometer half-orbit 36 km EASE-grid brightness temperatures, version 5. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 1 November 2022, https://doi.org/10.5067/JJ5FL7FRLKJI.

  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Colliander, A., and Coauthors, 2017a: SMAP/In situ core validation site land surface parameters match-up data, version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 1 November 2022, https://doi.org/10.5067/DXAVIXLY18KM.

  • Colliander, A., and Coauthors, 2017b: Validation of SMAP surface soil moisture products with core validation sites. Remote Sens. Environ., 191, 215231, https://doi.org/10.1016/j.rse.2017.01.021.

    • Search Google Scholar
    • Export Citation
  • Colliander, A., Z. Yang, R. Mueller, A. Sandborn, R. Reichle, W. Crow, D. Entekhabi, and S. Yueh, 2019: Consistency between NASS surveyed soil moisture conditions and SMAP soil moisture observations. Water Resour. Res., 55, 76827693, https://doi.org/10.1029/2018WR024475.

    • Search Google Scholar
    • Export Citation
  • Colliander, A., and Coauthors, 2022: Validation of soil moisture data products from the NASA SMAP mission. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15, 364392, https://doi.org/10.1109/JSTARS.2021.3124743.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., and Coauthors, 2012: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys., 50, RG2002, https://doi.org/10.1029/2011RG000372.

    • Search Google Scholar
    • Export Citation
  • 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 prestorm soil moisture conditions for hydrologic forecasting. Geophys. Res. Lett., 44, 54955503, https://doi.org/10.1002/2017GL073642.

    • Search Google Scholar
    • Export Citation
  • 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. Geophys. Res. Lett., 45, 48694878, https://doi.org/10.1029/2018GL077193.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., F. Chen, R. H. Reichle, and Y. Xia, 2019: Diagnosing bias in modeled soil moisture/runoff coefficient correlation using the SMAP level 4 soil moisture product. Water Resour. Res., 55, 70107026, https://doi.org/10.1029/2019WR025245.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., J. Dong, and R. H. Reichle, 2022: Leveraging pre‐storm soil moisture estimates for enhanced land surface model calibration in ungauged hydrologic basins. Water Resour. Res., 58, e2021WR031565, https://doi.org/10.1029/2021WR031565.

    • Search Google Scholar
    • Export Citation
  • Dannenberg, M. P., and Coauthors, 2022: Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought. Global Change Biol., 28, 47944806, https://doi.org/10.1111/gcb.16214.

    • Search Google Scholar
    • Export Citation
  • Das, N. N., and Coauthors, 2019: The SMAP and Copernicus sentinel 1A/B microwave active-passive high resolution surface soil moisture product. Remote Sens. Environ., 233, 111380, https://doi.org/10.1016/j.rse.2019.111380.

    • Search Google Scholar
    • Export Citation
  • De Lannoy, G. J. M., R. H. Reichle, and V. R. N. Pauwels, 2013: Global calibration of the GEOS-5 l-band microwave radiative transfer model over nonfrozen land using SMOS observations. J. Hydrometeor., 14, 765785, https://doi.org/10.1175/JHM-D-12-092.1.

    • Search Google Scholar
    • Export Citation
  • 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, 00344257, https://doi.org/10.1016/j.rse.2014.03.030.

    • Search Google Scholar
    • Export Citation
  • De Lannoy, G. J. M., and Coauthors, 2022: Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication. Front. Water, 4, 981745, https://doi.org/10.3389/frwa.2022.981745.

    • Search Google Scholar
    • Export Citation
  • Dong, J., W. Crow, R. 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, 66046613, https://doi.org/10.1029/2019GL083398.

    • Search Google Scholar
    • Export Citation
  • Dong, J., F. Lei, and W. T. Crow, 2022: Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States. Nat. Commun., 13, 336, https://doi.org/10.1038/s41467-021-27938-6.

    • Search Google Scholar
    • Export Citation
  • Draper, C. S., R. H. Reichle, G. J. M. De Lannoy, and Q. Liu, 2012: Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett., 39, L04401, https://doi.org/10.1029/2011GL050655.

    • Search Google Scholar
    • Export Citation
  • Draper, C. S., R. H. 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, 288298, https://doi.org/10.1016/j.rse.2013.06.013.

    • Search Google Scholar
    • Export Citation
  • Ducharne, A., R. D. Koster, M. J. Suarez, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 2. Parameter estimation and model demonstration. J. Geophys. Res., 105, 24 82324 838, https://doi.org/10.1029/2000JD900328.

    • Search Google Scholar
    • Export Citation
  • 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. J. Geophys. Res. Biogeosci., 125, e2020JG006100, https://doi.org/10.1029/2020JG006100.

    • Search Google Scholar
    • Export Citation
  • Endsley, K. A., J. S. Kimball, and R. H. Reichle, 2022: Soil respiration phenology improves modeled phase of terrestrial net ecosystem exchange in Northern Hemisphere. J. Adv. Model. Earth Syst., 14, e2021MS002804, https://doi.org/10.1029/2021MS002804.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2010a: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704716, https://doi.org/10.1109/JPROC.2010.2043918.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., R. H. Reichle, R. D. Koster, and W. T. Crow, 2010b: Performance metrics for soil moisture retrievals and application requirements. J. Hydrometeor., 11, 832840, https://doi.org/10.1175/2010JHM1223.1.

    • Search Google Scholar
    • Export Citation
  • Fan, L., and Coauthors, 2022: Evaluation of satellite and reanalysis estimates of surface and root-zone soil moisture in croplands of Jiangsu Province, China. Remote Sens. Environ., 282, 113283, https://doi.org/10.1016/j.rse.2022.113283.

    • Search Google Scholar
    • Export Citation
  • Fang, K., M. Pan, and C. Shen, 2019: The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Trans. Geosci. Remote Sens., 57, 22212233, https://doi.org/10.1109/TGRS.2018.2872131.

    • Search Google Scholar
    • Export Citation
  • Felsberg, A., G. J. M. De Lannoy, M. Girotto, J. Poesen, 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, 10651084, https://doi.org/10.1175/JHM-D-20-0228.1.

    • Search Google Scholar
    • Export Citation
  • Fernandez-Moran, R., and Coauthors, 2017: SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product. Remote Sens., 9, 457, https://doi.org/10.3390/rs9050457.

    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär, 2007: Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 50815099, https://doi.org/10.1175/JCLI4288.1.

    • Search Google Scholar
    • Export Citation
  • Ford, T. W., and S. M. Quiring, 2019: Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring. Water Resour. Res., 55, 15651582, https://doi.org/10.1029/2018WR024039.

    • Search Google Scholar
    • Export Citation
  • Galle, S., and Coauthors, 2018: AMMA‐CATCH, a critical zone observatory in West Africa monitoring a region in transition. Vadose Zone J., 17 (1), 124, https://doi.org/10.2136/vzj2018.03.0062.

    • Search Google Scholar
    • Export Citation
  • Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E. Trenberth, 2016: Comparison of global precipitation estimates across a range of temporal and spatial scales. J. Climate, 29, 77737795, https://doi.org/10.1175/JCLI-D-15-0618.1.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation
  • González-Zamora, Á., N. Sánchez, J. Martínez-Fernández, Á. Gumuzzio, M. Piles, and E. Olmedo, 2015: Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain). Phys. Chem. Earth, 83–84, 123136, https://doi.org/10.1016/j.pce.2015.05.009.

    • Search Google Scholar
    • Export Citation
  • Gruber, A., and Coauthors, 2020: Validation practices for satellite soil moisture retrievals: What are (the) errors? Remote Sens. Environ., 244, 111806, https://doi.org/10.1016/j.rse.2020.111806.

    • Search Google Scholar
    • Export Citation
  • Guo, Z., P. A. Dirmeyer, T. DelSole, and R. D. Koster, 2012: Rebound in atmospheric predictability and the role of the land surface. J. Climate, 25, 47444749, https://doi.org/10.1175/JCLI-D-11-00651.1.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • H SAF, 2020: ASCAT surface soil moisture climate data record v5 12.5 km sampling – Metop. EUMETSAT SAF on Support to Operational Hydrology and Water Management, accessed 1 November 2022, https://doi.org/10.15770/EUM_SAF_H_0006.

  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2019a: NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG). NASA Algorithm Theoretical Basis Doc., version 06, 38 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V06.pdf.

  • Huffman, G. J., D. T. Bolvin, E. J. Nelkin, and J. Tan, 2019b: Integrated Multi-Satellite Retrievals for GPM (IMERG) technical documentation. NASA Tech. Doc., 77 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc_190909.pdf.

  • Huffman, G. J., E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and J. Tan, 2019c: GPM IMERG final precipitation L3 1 month 0.1 degree × 0.1 degree V06. GES DISC, accessed 1 November 2022, https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06.

  • Huffman, G. J., E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and J. Tan, 2019d: GPM IMERG final precipitation L3 half hourly 0.1 degree × 0.1 degree V06. GES DISC, accessed 1 November 2022, https://doi.org/10.5067/GPM/IMERG/3B-HH/06.

  • Huffman, G. J., E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and J. Tan, 2019e: GPM IMERG late precipitation L3 half hourly 0.1 degree × 0.1 degree V06. GES DISC, accessed 1 November 2022, https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06.

  • Humphrey, V., A. Berg, P. Ciais, P. Gentine, M. Jung, M. Reichstein, S. I. Seneviratne, and C. Frankenberg, 2021: Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature, 592, 6569, https://doi.org/10.1038/s41586-021-03325-5.

    • Search Google Scholar
    • Export Citation
  • Jensen, K. H., and J. C. Refsgaard, 2018: HOBE: The Danish hydrological observatory. Vadose Zone J., 17 (1), 124, https://doi.org/10.2136/vzj2018.03.0059.

    • Search Google Scholar
    • Export Citation
  • Juglea, S., and Coauthors, 2010: Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station. Hydrol. Earth Syst. Sci., 14, 831846, https://doi.org/10.5194/hess-14-831-2010.

    • Search Google Scholar
    • Export Citation
  • Kerr, Y. H., and Coauthors, 2010: The SMOS Mission: New tool for monitoring key elements of the global water cycle. Proc. IEEE, 98, 666687, https://doi.org/10.1109/JPROC.2010.2043032.

    • Search Google Scholar
    • Export Citation
  • Kerr, Y. H., and Coauthors, 2016: Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sens. Environ., 180, 4063, https://doi.org/10.1016/j.rse.2016.02.042.

    • Search Google Scholar
    • Export Citation
  • Khodayar, S., A. Coll, and E. Lopez-Baeza, 2019: An improved perspective in the spatial representation of soil moisture: Potential added value of SMOS disaggregated 1 km resolution “all weather” product. Hydrol. Earth Syst. Sci., 23, 255275, https://doi.org/10.5194/hess-23-255-2019.

    • Search Google Scholar
    • Export Citation
  • Kimball, J. S., L. Jones, K. Jensco, M. He, M. Maneta, and R. Reichle, 2019: Smap L4 assessment of the US Northern Plains 2017 flash drought. IGARSS 2019 – 2019 IEEE Int. Geoscience and Remote Sensing Symp., Yokohama, Japan, Institute of Electrical and Electronics Engineers, 5366–5369, https://doi.org/10.1109/IGARSS.2019.8898354.

  • Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res., 105, 24 80924 822, https://doi.org/10.1029/2000JD900327.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, https://doi.org/10.1126/science.1100217.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2011: The second phase of the Global Land–Atmosphere Coupling Experiment: Soil moisture contributions to subseasonal forecast skill. J. Hydrometeor., 12, 805822, https://doi.org/10.1175/2011JHM1365.1.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Y. Chang, H. Wang, and S. D. Schubert, 2016: Impacts of local soil moisture anomalies on the atmospheric circulation and on remote surface meteorological fields during boreal summer: A comprehensive analysis over North America. J. Climate, 29, 73457364, https://doi.org/10.1175/JCLI-D-16-0192.1.

    • Search Google Scholar
    • Export Citation
  • Kumar, S., and Coauthors, 2022: An agenda for land data assimilation priorities: Realizing the promise of terrestrial water, energy, and vegetation observations from space. J. Adv. Model. Earth Syst., 14, e2022MS003259, https://doi.org/10.1029/2022MS003259.

    • Search Google Scholar
    • Export Citation
  • Le, M.-H., V. Lakshmi, J. Bolten, and D. D. Bui, 2020: Adequacy of satellite-derived precipitation estimate for hydrological modeling in Vietnam basins. J. Hydrol., 586, 124820, https://doi.org/10.1016/j.jhydrol.2020.124820.

    • Search Google Scholar
    • Export Citation
  • Li, X., J. Xiao, J. S. Kimball, R. H. Reichle, R. L. Scott, M. E. Litvak, 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. Remote Sens. Environ., 251, 112062, https://doi.org/10.1016/j.rse.2020.112062.

    • Search Google Scholar
    • Export Citation
  • Liang, Z., S. Chen, J. Hu, C. Huang, A. Zhang, L. Xiao, Z. Zhang, and X. Tong, 2019: Hydrologic evaluation of integrated multi-satellite retrievals for GPM over Nanliu River basin in tropical humid southern China. Water, 11, 932, https://doi.org/10.3390/w11050932.

    • Search Google Scholar
    • Export Citation
  • Lucchesi, R., 2018: File specification for GEOS-5 FP (forward processing). GMAO Office Note 4 (version 1.2), 62 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Lucchesi1203.pdf.

  • Luong, T. T., J. Pöschmann, I. Vorobevskii, S. Wiemann, R. Kronenberg, and C. Bernhofer, 2020: Pseudo-spatially-distributed modeling of water balance components in the free state of Saxony. Hydrology, 7, 84, https://doi.org/10.3390/hydrology7040084.

    • Search Google Scholar
    • Export Citation
  • Molod, A., and Coauthors, 2020: GEOS‐S2S version 2: The GMAO high‐resolution coupled model and assimilation system for seasonal prediction. J. Geophys. Res. Atmos., 125, e2019JD031767, https://doi.org/10.1029/2019JD031767.

    • Search Google Scholar
    • Export Citation
  • Pablos, M., Á. González-Zamora, N. Sánchez, and J. Martínez-Fernández, 2018: Assessment of root zone soil moisture estimations from SMAP, SMOS and MODIS observations. Remote Sens., 10, 981, https://doi.org/10.3390/rs10070981.

    • Search Google Scholar
    • Export Citation
  • Panciera, R., and Coauthors, 2014: The Soil Moisture Active Passive Experiments (SMAPEx): Toward soil moisture retrieval from the SMAP mission. IEEE Trans. Geosci. Remote Sens., 52, 490507, https://doi.org/10.1109/TGRS.2013.2241774.

    • Search Google Scholar
    • Export Citation
  • Piepmeier, J. R., and Coauthors, 2017: SMAP L-band microwave radiometer: Instrument design and first year on orbit. IEEE Trans. Geosci. Remote Sens., 55, 19541966, https://doi.org/10.1109/TGRS.2016.2631978.

    • Search Google Scholar
    • Export Citation
  • Pradhan, R. K., and Coauthors, 2022: Review of GPM IMERG performance: A global perspective. Remote Sens. Environ., 268, 112754, https://doi.org/10.1016/j.rse.2021.112754.

    • Search Google Scholar
    • Export Citation
  • 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. Hydrol. Earth Syst. Sci., 25, 15691586, https://doi.org/10.5194/hess-25-1569-2021.

    • Search Google Scholar
    • Export Citation
  • Rahman, M. S., L. Di, E. Yu, L. Lin, C. Zhang, and J. Tang, 2019: Rapid flood progress monitoring in cropland with NASA SMAP. Remote Sens., 11, 191, https://doi.org/10.3390/rs11020191.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and Q. Liu, 2021: Observation-corrected precipitation for the SMAP level 4 soil moisture (version 6) product and the GEOS R21C reanalysis. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2021-104606, 32 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Reichle1344.pdf.