Skip to main content
SpringerLink
Log in

Accuracy Assessment of IWCM Soil Moisture Estimation Model in Different Frequency and Polarization Bands

  • Short Note
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Radar backscattering coefficient has high dependence to dielectric constant of soil and many efforts have been done in the past to estimate soil moisture using Synthetic Aperture Radar (SAR). Soil moisture estimation in vegetated areas has some limitations and difficulties due to the effects of vegetation cover and soil surface roughness on radar backscattering coefficient. One of the widely used soil moisture estimation models in vegetated areas is Water Cloud Model (WCM) which has been improved and known as Improved Water Cloud Model (IWCM) recently. One way of improving soil moisture estimation accuracy in vegetated areas is to use optimum frequency and polarization band so as to minimize the effects of soil surface roughness and vegetation cover on radar back scattering coefficient. In this research, the accuracies of IWCM in different frequencies and polarizations have been assessed. The results showed that the IWCM has its highest accuracy in L-band, HV polarization mode. Also, by using the IWCM, sensitivities of radar waves to moisture of 0–3, 3–6 and 0–6 cm soil depths have been studied. The results demonstrated that radar waves have more sensitivity to the moisture content of 0–3 cm soil depth.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Allen, P. B., & Naney, J. W. (1991). Hydrology of the Little Washita River Watershed, Oklahoma: data and analyses. USDA, ARS-90, 74, Washington, DC.

  • Alvarez-Mozos, J., Casali, J., Gonzalez-Audicana, M., & Verhoest, N. E. C. (2005). Correlation between ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricultural catchment of Navarre (North of Spain). Biosystems Engineering, 92, 119–133.

    Article  Google Scholar 

  • Attema, E. P. W., & Ulaby, F. T. (1978). Vegetation modelled as a water cloud. Radio Science, 13, 357–364.

    Article  Google Scholar 

  • Beljaars, A., Viterbo, P., Miller, M., & Betts, A. (1996). The anomalous rainfall over the United States during July 1993: sensitivity to land surface parameterization and soil moisture anomalies. Monthly Weather Review, 124, 362–383.

    Article  Google Scholar 

  • Champion, I. (1996). Simple modelling of radar backscattering coefficient over a bare soil: variation with incidence angle, frequency and polarization. International Journal of Remote Sensing, 15, 783–800.

    Article  Google Scholar 

  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113, 893–903.

    Article  Google Scholar 

  • Dubois, P. C., van Zyl, J., & Engman, T. (1995). Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33, 915–926.

    Article  Google Scholar 

  • Fung, A. K., Li, Z., & Chen, K. S. (1992). Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 356–369.

    Article  Google Scholar 

  • Georgakakos, K. P., Guetter, A. K., & Sperfslage, J. A., (1997). Estimation of flash flood potential for large areas. In G. H. Leavesley, H. F. Lins, F. Nobilis, R. S. Parker, V. R. Schneider, & F. H. M. van de Von (Eds.), Destructive water: Water-caused natural disasters, their abatement and control (pp. 87–93). IAHS Publ. No. 239. Wallingford: IAHS Press.

  • Graham, A. J., & Harris, R. (2003). Extracting biophysical parameters from remotely sensed radar data: a review of the water cloud model. Progress in Physical Geography, 27, 217–229.

    Article  Google Scholar 

  • Jackson, T. J., & Cosh, M. H. (2006). SMEX03 regional ground soil moisture data: Oklahoma. Boulder: National Snow and Ice Data Center. Digital media.

    Google Scholar 

  • Maity, S., Patnaik, C., Chakraborty, M., & Panigrahy, S. (2004). Analysis of temporal backscattering of cotton crops using a semiempirical model. IEEE Transactions on Geoscience and Remote Sensing, 42, 577–587.

    Article  Google Scholar 

  • Oh, Y., Sarabandi, F. T., & Ulaby, F. (1992). An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30, 370–381.

    Article  Google Scholar 

  • Prévot, L., Champion, I., & Guyot, G. (1993). Estimating surface soil moisture and leaf are index of a wheat canopy using a dual-frequency (C and W bands) scatterometer. Remote Sensing of Environment, 46, 331–339.

    Article  Google Scholar 

  • Saradjian, M. R., & Hosseini, M. (2011). Soil moisture estimation using multipolarization SAR images. Advances in Space Research, 48, 278–286.

    Article  Google Scholar 

  • Shi, J., Wang, J., Hsu, A. Y., O’Neill, P. E., & Engman, E. T. (1997). Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Transactions on Geoscience and Remote Sensing, 35, 1254–1266.

    Article  Google Scholar 

  • Srivastava, H. S., Patel, P., Sharma, Y., & Navalgund, R. R. (2009). Large-area soil moisture estimation using multi-incidence-angle RADARSAT-1 SAR data. IEEE Transactions on Geoscience and Remote Sensing, 47, 2528–2535.

    Article  Google Scholar 

  • Taconet, O., Vidal-Madjar, D., Emblanch, C., & Normand, M. (1996). Taking into account vegetation effects to estimate soil moisture from C-band radar measurements. Remote Sensing of Environment, 56, 52–56.

    Article  Google Scholar 

  • Ulaby, F. T., Batlivala, P. P., & Dobson, M. C. (1978). Microwave backscatter dependence on surface roughness, soil moisture and soil texture. Part I—bare soil. IEEE Transactions on Geoscience and Remote Sensing, 17, 33–40.

    Article  Google Scholar 

  • Ulaby, F. T., Moore, R. K., & Fung, A. K. (1982). Microwave remote sensing, active and passive, volume II: Radar remote sensing and surface scattering and emission theory (p. 608). Norwood: Artech House.

    Google Scholar 

  • Van Leeuwen, H. J. C., Clevers, J. G. P. W., & Rijckenberg, G. J. (1994). Synergetic use of optical and microwave remote sensing data using models and specific features with respect to the sugar beet crop. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS ‘94), California, CA, USA, August 8–12, 2, 827–831.

Download references

Acknowledgments

The authors would like to thank the Soil Moisture Experiment 2003 Science Team and USDA-ARS Grazinglands Research Laboratory for their assistance in the collection of this data set.

Conflict of Interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Remote Sensing Division, Surveying and Geomatics Engineering Department, University College of Engineering, University of Tehran, Tehran, Iran

    Saeed Khabazan, Mehdi Hosseini, Mohammad Reza Saradjian & Mahdi Motagh

  2. Departement de Geomatique Appliqué, FLSH/CARTEL, Universite de Sherbrooke, 2500 Boul. de l’Universite, Sherbrooke, Quebec, J1K 2R1, Canada

    Ramata Magagi

Authors
  1. Saeed Khabazan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehdi Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Reza Saradjian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahdi Motagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ramata Magagi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Hosseini.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khabazan, S., Hosseini, M., Saradjian, M.R. et al. Accuracy Assessment of IWCM Soil Moisture Estimation Model in Different Frequency and Polarization Bands. J Indian Soc Remote Sens 43, 859–865 (2015). https://doi.org/10.1007/s12524-015-0455-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-015-0455-3

Keywords

Search

Navigation