Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

Wesley J. Rondinelli Iowa State University of Science and Technology, Ames, Iowa

Search for other papers by Wesley J. Rondinelli in
Current site
Google Scholar
PubMed
Close
,
Brian K. Hornbuckle Iowa State University of Science and Technology, Ames, Iowa

Search for other papers by Brian K. Hornbuckle in
Current site
Google Scholar
PubMed
Close
,
Jason C. Patton Iowa State University of Science and Technology, Ames, Iowa

Search for other papers by Jason C. Patton in
Current site
Google Scholar
PubMed
Close
,
Michael H. Cosh Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

Search for other papers by Michael H. Cosh in
Current site
Google Scholar
PubMed
Close
,
Victoria A. Walker Iowa State University of Science and Technology, Ames, Iowa

Search for other papers by Victoria A. Walker in
Current site
Google Scholar
PubMed
Close
,
Benjamin D. Carr Iowa State University of Science and Technology, Ames, Iowa

Search for other papers by Benjamin D. Carr in
Current site
Google Scholar
PubMed
Close
, and
Sally D. Logsdon National Laboratory for Agriculture and the Environment, Agricultural Research Service, USDA, Ames, Iowa

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

Abstract

Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

Current affiliation: National Laboratory for Agriculture and the Environment, Agricultural Research Service, USDA, Ames, Iowa.

Current affiliation: Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, Oklahoma.

Corresponding author address: Brian K. Hornbuckle, 3007 Agronomy Hall, Iowa State University of Science and Technology, Ames, IA 50011-1010. E-mail: bkh@iastate.edu

This article is included in the IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission Special Collection.

Abstract

Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

Current affiliation: National Laboratory for Agriculture and the Environment, Agricultural Research Service, USDA, Ames, Iowa.

Current affiliation: Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, Oklahoma.

Corresponding author address: Brian K. Hornbuckle, 3007 Agronomy Hall, Iowa State University of Science and Technology, Ames, IA 50011-1010. E-mail: bkh@iastate.edu

This article is included in the IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission Special Collection.

Save
  • Adams, J. R., Berg A. A. , and McNairn H. , 2013: Field level soil moisture variability at 6- and 3-cm sampling depths: Implications for microwave sensor validation. Vadose Zone J., 12 (3), doi:10.2136/vzj2012.0070.

    • Search Google Scholar
    • Export Citation
  • Al Bitar, A., Leroux D. , Kerr Y. H. , Merlin O. , Richaume P. , Sahoo A. , and Wood E. F. , 2012: Evaluation of SMOS soil moisture products over continental U.S. using the SCAN/SNOTEL network. IEEE Trans. Geosci. Remote Sens., 50, 1572–1586, doi:10.1109/TGRS.2012.2186581.

    • Search Google Scholar
    • Export Citation
  • Calvet, J.-C., and Noilhan J. , 2000: From near-surface to root-zone soil moisture using year-round data. J. Hydrometeor., 1, 393411, doi:10.1175/1525-7541(2000)001<0393:FNSTRZ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Campbell, G. S., and Norman J. M. , 1998: An Introduction to Environmental Biophysics. Springer-Verlag, 286 pp.

  • Carr, B. D., 2014: Evaluation of an agroecosystem model using cosmic-ray neutron soil moisture. M.S. thesis, Paper 14006, Dept. of Agronomy, Iowa State University of Science and Technology, 122 pp.

  • Collow, T. W., Robock A. , Basara J. B. , and Illston B. G. , 2012: Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations. J. Geophys. Res.,117, D09113, doi:10.1029/2011JD017095.

  • Coopersmith, E., Cosh M. H. , Petersen W. , Prueger J. H. , and Niemeier J. J. , 2015: Soil moisture model calibration and validation: An ARS watershed on the South Fork of the Iowa River. J. Hydrometeor., doi:10.1175/JHM-D-14-0145.1, in press.

    • Search Google Scholar
    • Export Citation
  • Cosh, M. H., Jackson T. J. , Bindlish R. , and Prueger J. H. , 2004: Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates. Remote Sens. Environ., 92, 427435, doi:10.1016/j.rse.2004.02.016.

    • Search Google Scholar
    • Export Citation
  • Cosh, M. H., Jackson T. J. , Starks P. , and Heathman G. , 2006: Temporal stability of surface soil moisture in the Little Washita River watershed and its applications in satellite soil moisture product validation. J. Hydrol., 323, 168177, doi:10.1016/j.jhydrol.2005.08.020.

    • 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, doi:10.1029/2011RG000372.

  • Dimitrov, M., and Coauthors, 2014: Soil hydraulic parameters and surface soil moisture of a tilled bare soil plot inversely derived from L-band brightness temperatures. Vadose Zone J., 13 (1), doi:10.2136/vzj2013.04.0075.

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

    • Search Google Scholar
    • Export Citation
  • Escorihuela, M. J., Chanzy A. , Wigneron J.-P. , and Kerr Y. H. , 2010: Effective soil moisture sampling depth of L-band radiometry: A case study. Remote Sens. Environ., 114, 9951001, doi:10.1016/j.rse.2009.12.011.

    • Search Google Scholar
    • Export Citation
  • Findell, K. L., and Eltahir E. A. B. , 2003: Atmospheric controls on soil moisture – boundary layer interactions. Part II: Feedbacks within the continental United States. J. Hydrometeor., 4, 570583, doi:10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gherboudj, I., Magagi R. , Goïta K. , Berg A. A. , Toth B. , and Walker A. , 2012: Validation of SMOS data over agricultural and boreal forest areas in Canada. IEEE Trans. Geosci. Remote Sens., 50, 1623–1635, doi:10.1109/TGRS.2012.2188532.

    • Search Google Scholar
    • Export Citation
  • Goodberlet, M. A., and Mead J. B. , 2012: Improved models of soil emission for use in remote sensing of soil moisture. IEEE Trans. Geosci. Remote Sens., 50, 39913999, doi:10.1109/TGRS.2012.2189574.

    • Search Google Scholar
    • Export Citation
  • Gutowski, W. J., Jr., Otieno F. O. , Arritt R. W. , Takle E. S. , and Pan Z. , 2004: Diagnosis and attribution of a seasonal precipitation deficit in a U.S. regional climate simulation. J. Hydrometeor., 5, 230242, doi:10.1175/1525-7541(2004)005<0230:DAAOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hillel, D., 2003: Introduction to Environmental Soil Physics. Academic Press, 494 pp.

  • Hornbuckle, B. K., and England A. W. , 2004: Radiometric sensitivity to soil moisture at 1.4 GHz through a corn crop at maximum biomass. Water Resour. Res., 40, W10204, doi:10.1029/2003WR002931.

    • Search Google Scholar
    • Export Citation
  • Hornbuckle, B. K., and England A. W. , 2005: Diurnal variation of vertical temperature gradients within a field of maize: Implications for satellite microwave radiometry. IEEE Geosci. Remote Sens. Lett., 2, 7477, doi:10.1109/LGRS.2004.841370.

    • Search Google Scholar
    • Export Citation
  • Irvin, S. L., 2013: Correction for rapid-growth vegetation and testing of an upscaling method with a COSMOS probe. M.S. thesis, Paper 13136, Dept. of Agronomy, Iowa State University of Science and Technology, 105 pp.

  • Jackson, T. J., and Schmugge T. J. , 1989: Passive microwave remote sensing system for soil moisture: Some supporting research. IEEE Trans. Geosci. Remote Sens., 27, 225235, doi:10.1109/36.20301.

    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., O’Neill P. E. , and Swift C. T. , 1997: Passive microwave observation of diurnal surface soil moisture. IEEE Trans. Geosci. Remote Sens., 35, 12101222, doi:10.1109/36.628788.

    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., and Coauthors, 2012: Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Trans. Geosci. Remote Sens., 50, 15301543, doi:10.1109/TGRS.2011.2168533.

    • Search Google Scholar
    • Export Citation
  • Jacobs, A. F. G., and van Pul W. A. J. , 1990: Seasonal changes in the albedo of a maize crop during two seasons. Agric. For. Meteor., 49, 351360, doi:10.1016/0168-1923(90)90006-R.

    • 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, doi:10.1109/JPROC.2010.2043032.

    • Search Google Scholar
    • Export Citation
  • Komma, J., Bloeschl G. , and Reszler C. , 2008: Soil moisture updating by ensemble Kalman filtering in real-time flood forecasting. J. Hydrol., 357, 228242, doi:10.1016/j.jhydrol.2008.05.020.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Suarez M. J. , Higgins R. W. , and Van den Dool H. M. , 2003: Observational evidence that soil moisture variations affect precipitation. Geophys. Res. Lett., 30, 1241, doi:10.1029/2002GL016571.

    • Search Google Scholar
    • Export Citation
  • Laymon, C. A., Crosson W. L. , Jackson T. J. , Manu A. , and Tsegaye T. D. , 2001: Ground-based passive microwave remote sensing observations of soil moisture at S-band and L-band with insight into measurement accuracy. IEEE Trans. Geosci. Remote Sens., 39, 18441858, doi:10.1109/36.951075.

    • Search Google Scholar
    • Export Citation
  • Magagi, R., and Coauthors, 2013: Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and preliminary results. IEEE Trans. Geosci. Remote Sens., 51, 347363, doi:10.1109/TGRS.2012.2198920.

    • Search Google Scholar
    • Export Citation
  • Mironov, V. L., Kosolapova L. G. , and Fomin S. V. , 2009: Physically and mineralogically based spectroscopic dielectric model for moist soils. IEEE Trans. Geosci. Remote Sens., 47, 20592070, doi:10.1109/TGRS.2008.2011631.

    • Search Google Scholar
    • Export Citation
  • Newton, R. W., Black Q. R. , Makanvand S. , Blanchard A. J. , and Jean B. R. , 1982: Soil moisture information and thermal microwave emission. IEEE Trans. Geosci. Remote Sens., 20, 275281, doi:10.1109/TGRS.1982.350443.

    • Search Google Scholar
    • Export Citation
  • Njoku, E. G., and O’Neill P. E. , 1982: Multifrequency microwave radiometer measurements of soil moisture. IEEE Trans. Geosci. Remote Sens., 20, 468475, doi:10.1109/TGRS.1982.350412.

    • Search Google Scholar
    • Export Citation
  • Pampaloni, P., Paloscia S. , Chiarantini L. , Coppo P. , Gagliani S. , and Luzi G. , 1990: Sampling depth of soil moisture content by radiometric measurement at 21 cm wavelength: Some experimental results. Int. J. Remote Sens., 11, 10851092, doi:10.1080/01431169008955080.

    • Search Google Scholar
    • Export Citation
  • Patton, J., and Hornbuckle B. , 2013: Initial validation of SMOS vegetation optical thickness over Iowa. IEEE Geosci. Remote Sens. Lett., 10, 647651, doi:10.1109/LGRS.2012.2216498.

    • Search Google Scholar
    • Export Citation
  • Raju, S., Chanzy A. , Wigneron J.-P. , Calvet J.-C. , Kerr Y. , and Laguerre L. , 1995: Soil moisture and temperature profile effects on microwave emission at low frequencies. Remote Sens. Environ., 54, 8597, doi:10.1016/0034-4257(95)00133-L.

    • Search Google Scholar
    • Export Citation
  • Rowlandson, T. L., Hornbuckle B. K. , Bramer L. M. , Patton J. C. , and Logson S. D. , 2012: Comparisons of evening and morning SMOS passes over the Midwest United States. IEEE Trans. Geosci. Remote Sens., 50, 15441555, doi:10.1109/TGRS.2011.2178158.

    • Search Google Scholar
    • Export Citation
  • Schmugge, T. J., and Choudhury B. J. , 1981: A comparison of radiative transfer models for predicting the microwave emission from soils. Radio Sci., 16, 927938, doi:10.1029/RS016i005p00927.

    • Search Google Scholar
    • Export Citation
  • Schmugge, T. J., Gloersen P. , Wilheit T. , and Geiger F. , 1974: Remote sensing of soil moisture with microwave radiometers. J. Geophys. Res., 79, 317323, doi:10.1029/JB079i002p00317.

    • Search Google Scholar
    • Export Citation
  • Schmugge, T. J., O’Neill P. E. , and Wang J. R. , 1986: Passive microwave soil moisture research. IEEE Trans. Geosci. Remote Sens., 24, 1222, doi:10.1109/TGRS.1986.289584.

    • Search Google Scholar
    • Export Citation
  • Schneeberger, K., Stamm C. , Mätzler C. , and Flühler H. , 2004: Ground-based dual-frequency radiometry of bare soil at high temporal resolution. IEEE Trans. Geosci. Remote Sens., 42, 588595, doi:10.1109/TGRS.2003.821058.

    • Search Google Scholar
    • Export Citation
  • Soylu, M. E., Kucharik C. J. , and Loheide S. P. , 2014: Influence of groundwater on plant water use and productivity: Development of an integrated ecosystem—Variably saturated soil water flow model. Agric. For. Meteor., 189–190, 198210, doi:10.1016/j.agrformet.2014.01.019.

    • Search Google Scholar
    • Export Citation
  • Takle, E. S., 1995: Variability of Midwest summertime precipitation. Preparing for Global Change: A Midwestern Perspective, G. R. Carmichael, G. E. Folk, and J. L. Schnoor, Eds., Progress in Biometeorology, Vol. 9, SPB Academic Publishing, 43–59.

  • Ulaby, F. T., and Long D. G. , 2013: Microwave Radar and Radiometric Remote Sensing. University of Michigan Press, 1116 pp.

  • Wanders, N., Karssenberg D. , de Roo A. , de Jong S. M. , and Bierkens M. F. P. , 2014: The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol. Earth Syst. Sci., 18, 23432357, doi:10.5194/hess-18-2343-2014.

    • Search Google Scholar
    • Export Citation
  • Wang, J. R., 1987: Microwave emission from smooth bare fields and soil moisture sampling depth. IEEE Trans. Geosci. Remote Sens., 25, 616622, doi:10.1109/TGRS.1987.289840.

    • Search Google Scholar
    • Export Citation
  • Wang, J. R., O’Neill P. E. , Jackson T. J. , and Engman E. T. , 1983: Multifrequency measurements of the effects of soil moisture, soil texture, and surface roughness. IEEE Trans. Geosci. Remote Sens., 21, 4451, doi:10.1109/TGRS.1983.350529.

    • Search Google Scholar
    • Export Citation
  • Wilheit, T. T., 1978: Radiative transfer in a plane stratified dielectric. IEEE Trans. Geosci. Electron., 16, 138143, doi:10.1109/TGE.1978.294577.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2532 660 51
PDF Downloads 1546 360 32