Soil Moisture Estimation Using Thermal Inertia: Potential and Sensitivity to Data Conditions

Dai Matsushima Department of Architecture and Civil Engineering, Chiba Institute of Technology, Narashino, Japan

Search for other papers by Dai Matsushima in
Current site
Google Scholar
PubMed
Close
,
Reiji Kimura Arid Land Research Center, Tottori University, Tottori, Japan

Search for other papers by Reiji Kimura in
Current site
Google Scholar
PubMed
Close
, and
Masato Shinoda Arid Land Research Center, Tottori University, Tottori, Japan

Search for other papers by Masato Shinoda in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Thermal inertia retrieval using a thermal infrared remote sensing technique has been examined as a possible method for estimating soil moisture. This method is an application of the theory that thermal inertia highly correlates with soil water content. This study shows a method for retrieving thermal inertia from a heat budget model of the earth’s surface using radiative surface temperatures, insolation, and meteorological data observed in field experiments. In bare to sparsely vegetated areas, this method has the potential to estimate subsurface soil moisture with a precision of ±3%–4% of the daily volumetric soil moisture content at a significance level of 5%, which is enough to roughly classify thermal inertia estimates into a few levels of soil moisture (e.g., wet, middle, and dry). The analysis also includes an examination of the practical performance of the thermal inertia estimation according to the temporal resolution of the data, assuming the use of satellite and routine meteorological data. It is found that the following combination of data can achieve the precision given above: radiative surface temperature from geostationary/multiple polar orbiting satellites, insolation retrieved from geostationary satellite data, and routine meteorological data.

Corresponding author address: Dai Matsushima, Department of Architecture and Civil Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan. E-mail: matsushima.dai@it-chiba.ac.jp

This article is included in the Hydrology in Earth System Science and Society (HESSS) special collection.

Abstract

Thermal inertia retrieval using a thermal infrared remote sensing technique has been examined as a possible method for estimating soil moisture. This method is an application of the theory that thermal inertia highly correlates with soil water content. This study shows a method for retrieving thermal inertia from a heat budget model of the earth’s surface using radiative surface temperatures, insolation, and meteorological data observed in field experiments. In bare to sparsely vegetated areas, this method has the potential to estimate subsurface soil moisture with a precision of ±3%–4% of the daily volumetric soil moisture content at a significance level of 5%, which is enough to roughly classify thermal inertia estimates into a few levels of soil moisture (e.g., wet, middle, and dry). The analysis also includes an examination of the practical performance of the thermal inertia estimation according to the temporal resolution of the data, assuming the use of satellite and routine meteorological data. It is found that the following combination of data can achieve the precision given above: radiative surface temperature from geostationary/multiple polar orbiting satellites, insolation retrieved from geostationary satellite data, and routine meteorological data.

Corresponding author address: Dai Matsushima, Department of Architecture and Civil Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan. E-mail: matsushima.dai@it-chiba.ac.jp

This article is included in the Hydrology in Earth System Science and Society (HESSS) special collection.

Save
  • Akima, H., 1970: A new method of interpolation and smooth curve fitting based on local procedures. J. Assoc. Comput. Mach., 17, 589602.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., Norman J. M. , Diak G. R. , Kustas W. P. , and Mecikalski J. R. , 1997: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195216.

    • Search Google Scholar
    • Export Citation
  • Caparrini, F., Castelli F. , and Entekhabi D. , 2004: Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resour. Res., 40, W12515, doi:10.1029/2004WR003358.

    • Search Google Scholar
    • Export Citation
  • FAO, 2007: FAO/UNESCO Digital Soil Map of the World. [Available online at http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116.]

  • Jury, W. A., and Horton R. , 2004: Soil Physics. 6th ed. John Wiley & Sons, 384 pp.

  • Kamichika, M., Ed., 1988: Studies on Soil Environment in a Sand Dune and Soil Temperature Control Using Multi Materials (in Japanese). Tottori University Press, 82 pp.

    • Search Google Scholar
    • Export Citation
  • Kawamura, H., Takahashi S. , and Takahashi T. , 1998: Estimation of insolation over the Pacific Ocean off the Sanriku Coast. J. Oceanogr., 54, 457464.

    • Search Google Scholar
    • Export Citation
  • Kimura, R., and Shinoda M. , 2010: Spatial distribution of threshold wind speeds for dust outbreaks in northeast Asia. Geomorphology, 114, 319325.

    • Search Google Scholar
    • Export Citation
  • Kondo, J., 2000: Atmospheric Science near the Ground Surface (in Japanese). University of Tokyo Press, 324 pp.

  • Kondo, J., and Watanabe T. , 1992: Studies on the bulk transfer coefficients over a vegetated surface with a multilayer energy budget model. J. Atmos. Sci., 49, 21832199.

    • Search Google Scholar
    • Export Citation
  • Lu, S., Ju Z. , Ren T. , and Horton R. , 2009: A general approach to estimate soil water content from thermal inertia. Agric. For. Meteor., 149, 16931698.

    • Search Google Scholar
    • Export Citation
  • Matsushima, D., 2006: Retrieval of the surface bulk parameters over Mongolian steppe and forest using a heat budget model incorporating remote sensing data. Proc. Int. Workshop on Terrestrial Change in Mongolia, Tokyo, Japan, JAMSTEC, 72–73.

    • Search Google Scholar
    • Export Citation
  • Matsushima, D., 2007: Estimating regional distribution of surface heat fluxes by combining satellite data and a heat budget model over the Kherlen River Basin, Mongolia. J. Hydrol., 333, 8699.

    • Search Google Scholar
    • Export Citation
  • Minacapilli, M., Iovino M. , and Blanda F. , 2009: High resolution remote estimation of soil surface water content by a thermal inertia approach. J. Hydrol., 379, 229238.

    • Search Google Scholar
    • Export Citation
  • Murray, T., and Verhoef A. , 2007: Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements: I. A universal approach to calculate thermal inertia. Agric. For. Meteor., 147, 8087.

    • Search Google Scholar
    • Export Citation
  • Norman, J. M., Kustas W. P. , Prueger J. H. , and Diak G. R. , 2000: Surface flux estimation using radiometric temperature: A dual temperature-difference method to minimize measurement errors. Water Resour. Res., 36, 22632274.

    • Search Google Scholar
    • Export Citation
  • Press, W. H., Teukolsky S. A. , Vetterling W. T. , and Flannery B. P. , 1986: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 848 pp.

    • Search Google Scholar
    • Export Citation
  • Price, J. C., 1977: Thermal inertia mapping: A new view of the earth. J. Geophys. Res., 82, 25822590.

  • Price, J. C., 1985: On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia. Remote Sens. Environ., 18, 5973.

    • Search Google Scholar
    • Export Citation
  • Shinoda, M., and Coauthors, 2010: Characteristics of dust emission in the Mongolian Steppe during the 2008 DUVEX intensive observational period. SOLA, 6, 912.

    • Search Google Scholar
    • Export Citation
  • Sobrino, J. A., and El Kharraz M. H. , 1999: Combining afternoon and morning NOAA satellites for thermal inertia estimation 1. Algorithm and its testing with Hydrologic Atmospheric Pilot Experiment-Sahel data. J. Geophys. Res., 104, 94459453.

    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1988: An Introduction to Boundary-Layer Meteorology. Kluwer Academic, 666 pp.

  • Sugita, M., Asanuma J. , Tsujimura M. , Mariko S. , Lu M. , Kimura F. , Azzaya D. , and Adyasuren T. , 2007: An overview of the rangelands atmosphere–hydrosphere–biosphere interaction study experiment in northeastern Asia (RAISE). J. Hydrol., 333, 320.

    • Search Google Scholar
    • Export Citation
  • Sugita, M., Asanuma J. , Tsujimura M. , Mariko S. , Lu M. , Kimura F. , Azzaya D. , and Adyasuren T. , 2008: Rangelands Atmosphere–Hydrosphere–Biosphere Interaction Study Experiment in Northeastern Asia (RAISE) Data Base. Terrestrial Environment Research Center, University of Tsukuba, DVD-ROM. [Available online at http://raise.suiri.tsukuba.ac.jp/DVD/top/home.htm.]

    • Search Google Scholar
    • Export Citation
  • Takenaka, H., Nakajima T. Y. , Higurashi A. , Higuchi A. , Takamura T. , Pinker R. T. , and Nakajima T. , 2011: Estimation of solar radiation using a neural network based on radiative transfer. J. Geophys. Res., 116, D08215, doi:10.1029/2009JD013337.

    • Search Google Scholar
    • Export Citation
  • van de Griend, A. A., Camillo P. J. , and Gurney R. J. , 1985: Discrimination of soil physical parameters, thermal inertia, and soil moisture from diurnal surface temperature fluctuations. Water Resour. Res., 21, 9971009.

    • Search Google Scholar
    • Export Citation
  • Verhoef, A., 2004: Remote estimation of thermal inertia and soil heat flux for bare soil. Agric. For. Meteor., 123, 221236.

  • Verstraeten, W. W., Veroustraete F. , van der Sande C. J. , Grootaers I. , and Feyen J. , 2006: Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sens. Environ., 101, 299314.

    • Search Google Scholar
    • Export Citation
  • Wang, K. C., Li Z. Q. , and Cribb M. , 2006: Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sens. Environ., 102, 293305.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., and Cracknell A. P. , 1995: Advanced thermal inertia modelling. Int. J. Remote Sens., 16, 431446.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3465 2705 470
PDF Downloads 454 133 10