• Bell, J. E., and et al. , 2013: U.S. Climate Reference Network soil moisture and temperature observations. J. Hydrometeor., 14, 977988, https://doi.org/10.1175/JHM-D-12-0146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bongiovanni, T., and T. G. Caldwell, 2019: Texas Soil Observation Network (TxSON). Texas Data Repository Dataverse, V3, accessed 7 July 2020, https://doi.org/10.18738/T8/JJ16CF.

    • Crossref
    • Export Citation
  • Boryan, C., Z. Yang, R. Mueller, and M. Craig, 2011: Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto Int., 26, 341358, https://doi.org/10.1080/10106049.2011.562309.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brocca, L., T. Moramarco, F. Melone, W. Wagner, S. Hasenauer, and S. Hahn, 2012: Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall-runoff modeling. IEEE Trans. Geosci. Remote Sens., 50, 25422555, https://doi.org/10.1109/TGRS.2011.2177468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brock, F. V., K. C. Crawford, G. W. Elliott, S. J. Cuperus, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519, https://doi.org/10.1175/1520-0426(1995)012<0005:TOMATO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caldwell, T. G., and et al. , 2019: The Texas soil observation network: A comprehensive soil moisture dataset for remote sensing and land surface model validation. Vadose Zone J., 18, 120, https://doi.org/10.2136/vzj2019.04.0034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Champagne, C., A. Davidson, P. Cherneski, J. L’Heureux, and T. Hawden, 2015: Monitoring agricultural risk in Canada using L-band passive microwave soil moisture from SMOS. J. Hydrometeor., 16, 518, https://doi.org/10.1175/JHM-D-14-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and et al. , 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 72517268, https://doi.org/10.1029/95JD02165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and et al. , 2017: Application of triple collocation in ground-based validation of Soil Moisture Active/Passive (SMAP) level 2 data products. IEEE Appl. Earth Obs. Rem. Sens., 10, 489502, https://doi.org/10.1109/JSTARS.2016.2569998.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clayton, J. A., S. M. Quiring, T. Ochsner, M. Cosh, B. Baker, T. W. Ford, J. D. Bolten, and M. Woloszyn, 2019: Building a one-stop shop for soil moisture information. Eos, Trans. Amer. Geophys. Union, 100, https://doi.org/10.1029/2019EO123631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crow, W. T., and et al. , 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.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Rosnay, P., G. Balsamo, C. Albergel, J. Muñoz-Sabater, and L. Isaksen, 2014: Initialization of land surface variables for numerical weather prediction. Surv. Geophys., 35, 607621, https://doi.org/10.1007/s10712-012-9207-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diamond, H. J., and et al. , 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 485498, https://doi.org/10.1175/BAMS-D-12-00170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and et al. , 2016: Confronting weather and climate models with observational data from soil moisture networks over the United States. J. Hydrometeor., 17, 10491067, https://doi.org/10.1175/JHM-D-15-0196.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., C. A. Schlosser, and K. L. Brubaker, 2009: Precipitation, recycling, and land memory: An integrated analysis. J. Hydrometeor., 10, 278288, https://doi.org/10.1175/2008JHM1016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dorigo, W. A., and et al. , 2011: The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 16751698, https://doi.org/10.5194/hess-15-1675-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dorigo, W. A., and et al. , 2017: ESA CCI soil moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ., 203, 185215, https://doi.org/10.1016/j.rse.2017.07.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dumedah, G., and P. Coulibably, 2013: Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data. Adv. Water Resour., 53, 231241, https://doi.org/10.1016/j.advwatres.2012.07.012.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, Y., and H. van Den Dool, 2004: Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present. J. Geophys. Res., 104, D10102, https://doi.org/10.1029/2003JD004345.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flores, A. N., D. Entekhabi, and R. L. Bras, 2014: Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment. J. Terramech., 51, 5366, https://doi.org/10.1016/j.jterra.2013.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., and C. F. Labosier, 2017: Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. For. Meteor., 247, 414423, https://doi.org/10.1016/j.agrformet.2017.08.031.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., D. B. McRoberts, S. M. Quiring, and R. E. Hall, 2015: On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma. Geophys. Res. Lett., 42, 97909798, https://doi.org/10.1002/2015GL066600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., Q. Wang, and S. M. Quiring, 2016: The observation record length necessary to generate robust soil moisture percentiles. J. Appl. Meteor. Climatol., 55, 21312149, https://doi.org/10.1175/JAMC-D-16-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., P. A. Dirmeyer, and D. O. Benson, 2018: Evaluation of heat wave forecasts seamlessly across subseasonal timescales. NPJ Climate Atmos. Sci., 1, 20, https://doi.org/10.1038/s41612-018-0027-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gruber, A., W. A. Dorigo, S. Zwieback, A. Xaver, and W. Wagner, 2013: Characterizing coarse-scale representativeness of in situ soil moisture measurements from the International Soil Moisture Network. Vadose Zone J., 12, 116, https://doi.org/10.2136/vzj2012.0170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gruber, A., W. A. Dorigo, W. Crow, and W. Wagner, 2017: Triple collocation-based merging of satellite soil moisture retrievals. IEEE Trans. Geosci. Remote Sens., 55, 67806792, https://doi.org/10.1109/TGRS.2017.2734070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Z., P. A. Dirmeyer, and T. DelSole, 2011: Land surface impacts on subseasonal and seasonal predictability. Geophys. Res. Lett., 38, L24812, https://doi.org/10.1029/2011GL049945.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, E., G. C. Heathman, V. Merwade, and M. H. Cosh, 2012: Application of observation operators for field scale soil moisture averages and variances in agricultural landscapes. J. Hydrol., 444–445, 3450, https://doi.org/10.1016/j.jhydrol.2012.03.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoogenboom, G., 1993: The Georgia automated environmental monitoring network. Georgia Institute of Technology, 5 pp., http://www.gwri.gatech.edu/sites/default/files/files/docs/1993/HoogenboomG-93.pdf.

  • Illinois State Water Survey, 2015: Illinois Climate Network. Water and Atmospheric Resources Monitoring Program, https://doi.org/10.13012/J8MW2F2Q.

    • Crossref
    • Export Citation
  • Kansas Climate Office, 2020: Kansas Mesonet Historical Data. Accessed 5 January 2020, https://mesonet.k-state.edu/weather/historical.

  • Kerr, Y., and et al. , 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and et al. , 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krueger, E. S., T. E. Ochsner, and S. M. Quiring, 2019: Development and evaluation of soil moisture-based indices for agricultural drought monitoring. Agron. J., 111, 13921406, https://doi.org/10.2134/agronj2018.09.0558.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legates, D., and et al. , 2005: DEOS: The Delaware Environmental Observing System. 21st Int. Conf. on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, San Diego, CA, Amer. Meteor. Soc., 18.10, https://ams.confex.com/ams/Annual2005/techprogram/paper_87687.htm.

  • Liu, Y. Y., W. A. Dorigo, R. M. Parinussa, R. A. M. de Jeu, W. Wagner, M. F. McCabe, J. P. Evans, and A. I. J. M. van Dijk, 2012: Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Envion., 123, 280297, https://doi.org/10.1016/j.rse.2012.03.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lloyd, C. E. M., J. E. Freer, P. J. Johnes, and A. L. Collins, 2016: Using hysteresis analysis of high-resolution water quality monitoring data, including uncertainty, to infer controls on nutrient and sediment transfer in catchments. Sci. Total Environ., 543, 388404, https://doi.org/10.1016/j.scitotenv.2015.11.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loew, A., and F. Schlenz, 2011: A dynamic approach for evaluating coarse scale satellite soil moisture products. Hydrol. Earth Syst. Sci., 15, 7590, https://doi.org/10.5194/hess-15-75-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lollato, R. P., A. Patrignani, T. E. Ochsner, and J. T. Edwards, 2016: Prediction of plant available water at sowing for winter wheat in the Southern Great Plains. Agron. J., 108, 745757, https://doi.org/10.2134/agronj2015.0433.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, R., E. B. Jaeger, and S. I. Seneviratne, 2010: Persistence of heat waves and its link to soil moisture memory. Geophys. Res. Lett., 37, L09703, https://doi.org/10.1029/2010GL042764.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahanama, S. P., R. D. Koster, R. H. Reichle, and L. Zubair, 2008: The role of soil moisture initialization in subseasonal and seasonal streamflow prediction-A case study in Sri Lanka. Adv. Water Resour., 31, 13331343, https://doi.org/10.1016/j.advwatres.2008.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNutt, C., M. Strobel, J. Lucido, and S. M. Quiring, 2016: National Soil Moisture Network Workshop 2016: Progress made, future directions. 8 pp., https://www.drought.gov/drought/documents/national-soil-moisture-network-workshop-2016-progress-made-future-directions.

  • McPherson, R. A., 2007: A review of vegetation–atmosphere interactions and their influences on mesoscale phenomena. Prog. Phys. Geogr., 31, 261285, https://doi.org/10.1177/0309133307079055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and et al. , 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301321, https://doi.org/10.1175/JTECH1976.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miralles, D. G., W. T. Crow, and M. H. Cosh, 2010: Estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations. J. Hydrometeor., 11, 14231429, https://doi.org/10.1175/2010JHM1285.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nateghi, R., S. D. Guikema, and S. M. Quiring, 2014: Forecasting hurricane-induced power outage durations. Nat. Hazards, 74, 17951811, https://doi.org/10.1007/s11069-014-1270-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ochsner, T. E., and et al. , 2013: State of the art in large-scale soil moisture monitoring. Soil Sci. Soc. Amer. J., 77, 18881919, https://doi.org/10.2136/sssaj2013.03.0093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orth, R., and S. I. Seneviratne, 2014: Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe. Climate Dyn., 43, 34033418, https://doi.org/10.1007/s00382-014-2112-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J., M. Svoboda, E. D. Hunt, T. W. Ford, M. C. Anderson, C. Hain, and J. B. Basara, 2018: Flash droughts: A review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Amer. Meteor. Soc., 99, 911919, https://doi.org/10.1175/BAMS-D-17-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, W., R. P. Boyles, J. G. White, and J. L. Heitman, 2012: Characterizing soil physical properties for soil moisture monitoring in the North Carolina Environment and Climate Observing Network. J. Atmos. Oceanic Technol., 29, 933943, https://doi.org/10.1175/JTECH-D-11-00104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Patrignani, A., and T. E. Ochsner, 2018: Modeling transient soil moisture dichotomies in landscapes with intermixed land covers. J. Hydrol., 566, 783794, https://doi.org/10.1016/j.jhydrol.2018.09.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Patrignani, A., C. B. Godsey, T. E. Ochsner, and J. T. Edwards, 2012: Soil water dynamics of conventional and no-till wheat in the Southern Great Plains. Soil Sci. Soc. Amer. J., 76, 17681775, https://doi.org/10.2136/sssaj2012.0082.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, A. J., N. K. Newlands, S. H. Liang, and B. H. Ellert, 2014: Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support. Comp. Elec. Ag., 107, 7388, https://doi.org/10.1016/j.compag.2014.02.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quinn, N. W., R. Ortega, P. J. Rahilly, and C. W. Royer, 2010: Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal and wetland real-time water quality management. Environ. Modell. Software, 25, 10451058, https://doi.org/10.1016/j.envsoft.2009.10.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quiring, S. M., and T. N. Papakryiakou, 2003: An evaluation of agricultural drought indices for the Canadian prairies. Agric. For. Meteor., 118, 4962, https://doi.org/10.1016/S0168-1923(03)00072-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quiring, S. M., L. Zhu, and S. D. Guikema, 2011: Importance of soil and elevation characteristics for modeling hurricane-induced power outages. Nat. Hazards, 58, 365390, https://doi.org/10.1007/s11069-010-9672-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robinson, D. A., 2005: The New Jersey Weather and Climate Network: Providing environmental information for a myriad of applications. 15th Conf. on Applied Climatology, Savannah, GA, Amer. Meteor. Soc., J2.1, https://ams.confex.com/ams/15AppClimate/webprogram/Paper94206.html.

  • Sanchez, N., J. Martinez-Fernández, A. Scaini, and C. Perez-Gutierrez, 2012: Validation of the SMOS L2 soil moisture data in the REMEDHUS network (Spain). IEEE Trans. Geosci. Remote Sens., 50, 16021611, https://doi.org/10.1109/TGRS.2012.2186971.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sandborn, A., and et al. , 2019: NASS geospatial applications from the cropland data layer. ISI World Statistics Conf., Kuala Lumpur, Malaysia, International Statistical Institute, 6 pp., https://www.nass.usda.gov/Research_and_Science/Cropland/docs/ISI%20WSC%20Paper%20-%20Sandborn.pdf.

  • Santanello, J. A., Jr., C. D. Peters-Lidard, and S. V. Kumar, 2011: Diagnosing the sensitivity of local land–atmosphere coupling via the soil moisture–boundary layer interaction. J. Hydrometeor., 12, 766786, https://doi.org/10.1175/JHM-D-10-05014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., and R. F. Paetzold, 2001: SNOTEL (SNOwpack TELemetry) and SCAN (Soil Climate Analysis Network). Automated Weather Stations for Applications in Agriculture and Water Resources Management: Current Perspectives. K. G. Hubbard and M. V. K. Sivakuman, Eds., WMO/TD 1074, 187194, http://www.wamis.org/agm/pubs/agm3/WMO-TD1074.pdf.

  • 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, 20732077, https://doi.org/10.1175/2007JTECHA930.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schroeder, J. L., W. S. Burgett, K. B. Haynie, and I. Sonmez, 2005: The West Texas Mesonet: A technical overview. J. Atmos. Oceanic Technol., 22, 211222, https://doi.org/10.1175/JTECH-1690.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scipal, K. M., M. Drusch, and W. Wagner, 2008: Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system. Adv. Water Resour., 31, 11011112, https://doi.org/10.1016/j.advwatres.2008.04.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scott, B. L., T. E. Ochsner, B. G. Illston, C. A. Fiebrich, J. B. Basara, and A. J. Sutherland, 2013: New soil property database improves Oklahoma Mesonet soil moisture estimates. J. Atmos. Oceanic Technol., 30, 25852595, https://doi.org/10.1175/JTECH-D-13-00084.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sehgal, V., V. Sridhar, and A. Tyagi, 2017: Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations. J. Hydrol., 545, 226250, https://doi.org/10.1016/j.jhydrol.2016.12.033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture-climate interactions in a change climate: A review. Earth-Sci. Rev., 99, 125161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., and et al. , 2014: A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull. Amer. Meteor. Soc., 95, 861882, https://doi.org/10.1175/BAMS-D-12-00124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silvestro, F., and N. Rebora, 2014: Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic flood forecasting chain. J. Hydrol., 519, 10521067, https://doi.org/10.1016/j.jhydrol.2014.07.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soulis, K. X., S. Elmaloglou, and N. Dercas, 2015: Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems. Agric. Water Manage., 148, 258268, https://doi.org/10.1016/j.agwat.2014.10.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • South Dakota State University, 2020: South Dakota Mesonet Database. Accessed 5 January 2020, https://climate.sdstate.edu/archive/.

  • State Climate Office of North Carolina, 2020: CRONOS. North Carolina State University, accessed 5 January 2020, https://climate.ncsu.edu/cronos.

  • University at Albany, 2020: New York Mesonet. Accessed 5 January 2020, http://www.nysmesonet.org/.

  • Wanders, N., M. F. Bierkens, S. M. de Jong, A. de Roo, and D. Karssenberg, 2014: The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models. Water Resour. Res., 50, 68746891, https://doi.org/10.1002/2013WR014639.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., 2008: The University of Washington Surface Water Monitor: An experimental platform for national hydrologic assessment and prediction. 22nd Conf. on Hydrology, New Orleans, LA, Amer. Meteor. Soc., 5.2, https://ams.confex.com/ams/88Annual/techprogram/paper_134844.htm.

  • Xia, Y., and et al. , 2012: 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., 117, D03110, https://doi.org/10.1029/2011JD016051.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., J. Sheffield, M. B. Ek, J. Dong, N. Chaney, H. Wei, J. Meng, and E. F. Wood, 2014: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107125, https://doi.org/10.1016/j.jhydrol.2014.02.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, Y., M. B. Ek, Y. Wu, T. W. Ford, and S. M. Quiring, 2015: Comparison of NLDAS-2 simulated and NASMD observed daily soil moisture. Part I: Comparison and analysis. J. Hydrometeor., 16, 19621980, https://doi.org/10.1175/JHM-D-14-0096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yilmaz, M. T., and W. T. Crow, 2013: The optimality of potential rescaling approaches in land data assimilation. J. Hydrometeor., 14, 650660, https://doi.org/10.1175/JHM-D-12-052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zamora, R. J., F. M. Ralph, E. Clark, and T. Schneider, 2011: The NOAA Hydrometeorology Testbed soil moisture observing networks: Design, instrumentation, and preliminary results. J. Atmos. Oceanic Technol., 28, 11291140, https://doi.org/10.1175/2010JTECHA1465.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

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  • 1 Illinois State Climatologist Office, Climate and Atmospheric Science Section, Division of State Water Survey, Prairie Research Institute, University of Illinois at Urbana–Champaign, Champaign, Illinois
  • | 2 Atmospheric Sciences Program, Department of Geography, The Ohio State University, Columbus, Ohio
  • | 3 School of Earth Systems and Sustainability, Southern Illinois University at Carbondale, Carbondale, Illinois
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Abstract

Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Trent W. Ford, twford@illinois.edu

Abstract

Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Trent W. Ford, twford@illinois.edu
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