Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

A Perspective on Radar Remote Sensing of Soil Moisture

  • Park, Sang-Eun (Graduate School of Science and Technology, Niigata University)
  • Received : 2011.10.02
  • Accepted : 2011.11.01
  • Published : 2011.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

The sensitivity of microwave scattering to the dielectric properties and the geometric structure of soil surfaces makes radar remote sensing a challenge for a wide range of environmental issues directly related to the condition of natural surfaces. Especially, the potential for retrieving soil moisture with a high spatial and/or temporal resolution represents a significant contribution to hydrological and ecological modeling. This paper aims to review the current state of the art in SAR technology and methodological issues towards the discovery of a new potential accurate monitoring of soil moisture changes. In this paper, important parameters or constraints significantly affect the sensitivity of the measurements to soil moisture, such as roughness statistics, spatial resolution, and local topography, are discussed to improve the applicability of SAR remote sensing techniques. This study particularly intends to discuss important notes for developing smart and reliable methods capable of retrieving geophysical information.

Keywords

References

  1. Baghdadi, N., I. Gherboudj, M. Zirbi, M. Sahebi, C. King, and F. Bonn, 2004. Semiempirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements, International Journal of Remote Sensing, 25(18): 3593-3623. https://doi.org/10.1080/01431160310001654392
  2. Bass, F.G. and I.M. Fuks, 1979. Wave Scattering from Statistically Rough Surfaces, Oxford, Pergamon Press, Ltd. (International Series in Natural Philosophy. Volume 93).
  3. Beckmann, P. and A. Spizzichino, 1963. The scattering of electromagnetic waves from rough surfaces, New York: Pergamon.
  4. Bindlish, R. and A.P. Barros, 2000. Multifrequency soil moisture inversion from SAR measurements with the use of IEM, Remote Sensing of Environment, 71(1): 67-88. https://doi.org/10.1016/S0034-4257(99)00065-6
  5. Davidson, M.W.J., T. Le Toan, F. Mattia, G. Satalino, T. Manninen, and M. Borgeaud, 2000. On the characterization of agricultural soil roughness for radar remote sensing studies, IEEE Transactions on Geoscience and Remote Sensing, 38(2): 630-640. https://doi.org/10.1109/36.841993
  6. Dobson, M.C., F.T. Ulaby, M.T. Hallikainen, and M.A. El-Rayes, 1985. Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models, IEEE Transactions on Geoscience and Remote Sensing, GE-23(1): 35-46. https://doi.org/10.1109/TGRS.1985.289498
  7. Dubois, P.C., J.J. van Zyl, and T. Engman, 1995. Measuring Soil Moisture with Imaging Radars, IEEE Transactions on Geoscience and Remote Sensing, 33(4): 916-926.
  8. Fung, A.K., 1994. Microwave Scattering and Emission Models and Their Applications. Norwood, MA, Artech House.
  9. Fung, A.K. and K.-S. Chen. 2010. Microwave Scattering and Emission Models for Users, Norwood, MA, Artech House.
  10. Gade, M., W. Alper, C. Melsheimer, and G. Tanck, 2008. Classification of sediments on exposed tidal flats in the German Bight using multifrequency radar data, Remote Sensing of Environment, 112(4): 1603-1613. https://doi.org/10.1016/j.rse.2007.08.015
  11. Glenn, N.F. and J.R. Carr, 2003. The use of geostatistics in relating soil moisture to RADARSAT-1 SAR data obtained over the Great Basin, Nevada, USA, Computers and Geosciences, 29: 577-586. https://doi.org/10.1016/S0098-3004(03)00050-5
  12. Haddad, Z.S., P. Dubois, and J.J. van Zyl, 1996. Bayesian Estimation of Soil Parameters from Radar Backscatter Data, IEEE Transactions on Geoscience and Remote Sensing, 34(1): 76-82. https://doi.org/10.1109/36.481895
  13. Hajnsek, I., E. Pottier, and S.R. Cloude, 2003. Inversion of Surface Parameters from Polarimetric SAR, IEEE Transactions on Geoscience and Remote Sensing, 41(4): 727-744. https://doi.org/10.1109/TGRS.2003.810702
  14. Kim, Y. and J.J. van Zyl, 2009. Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data, IEEE Transactions on Geoscience and Remote Sensing, 47(8): 2519-2527. https://doi.org/10.1109/TGRS.2009.2014944
  15. Lee, J.S., D.L. Schuler, and T.L. Ainsworth, 2000. Polarimetric SAR data compensation for terrain azimuth slope variation, IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2153-2163. https://doi.org/10.1109/36.868874
  16. Lemaire, D., P. Sobieski, A. Guissard, and C. Craeye, 2002. Two-scale models for rough surface scattering : comparison between the Boundary Perturbation Method and the Integral Equation Method, Radio Science, 37(1): 1-16. https://doi.org/10.1029/2000RS002569
  17. Oh, Y., K. Sarabandi, and F.T. Ulaby, 1992. An empirical model and an inversion technique for radar scattering from bare soil surfaces, IEEE Transactions on Geoscience and Remote Sensing, 30(2): 370-381. https://doi.org/10.1109/36.134086
  18. Park, S.-E., J. Kim, W.M. Moon, and W.M. Boerner, 2007. Inversion of surface parameters from NASA/JPL AIRSAR polarimetric SAR data, Proc. of POLinSAR 2007, ESRIN, Frascati, Italy, Jan. 22-26.
  19. Park, S.-E., L. Ferro-Famil, S. Allain, and E. Pottier, 2008. Analysis of Polarimetric Surface Scattering in High Resolution SAR, Proc. of 2008 International Geoscience and Remote Sensing Symposium, Boston, USA, pp. 394-397.
  20. Park, S.-E., W.M. Moon, D. Kim, and J.-E. Kim, 2009. Estimation of surface roughness parameter in intertidal mudflat using airborne polarimetric SAR data, IEEE Transactions on Geoscience and Remote Sensing, 47(4): 1022-1031. https://doi.org/10.1109/TGRS.2008.2008908
  21. Pathe C., W. Wagner, D. Sabel, M. Doubkova, and J. Basara, 2009. Using ENVISAT ASAR Global Mode Data for Surface Soil Moisture Retrieval Over Oklahoma, USA, IEEE Transactions on Geoscience and Remote Sensing, 47(2): 468-480. https://doi.org/10.1109/TGRS.2008.2004711
  22. Quesney, A., S. Le H?garat-Mascle, O. Taconet, D. Vidal- Madjar, J.P. Wigneron, C. Loumagne, and M. Normand, 2000. Estimation of watershed soil moisture index from ERS/SAR data, Remote Sensing of Environment, 72(3): 290-303. https://doi.org/10.1016/S0034-4257(99)00102-9
  23. Rahman, M.M., M.S. Moran, D.P. Thoma, R. Bryant, C.D. Holifield Collins, T. Jackson, B.J. Orr, and M. Tischler, 2008. Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data, Remote Sensing of Environment, 112(2): 391-402. https://doi.org/10.1016/j.rse.2006.10.026
  24. Shi, J., J. Wang, A.Y. Hsu, P.E. O'Neill, and E.T. Engman, 1997. Estimation of bare surface soil moisture and surface roughness parameters using L-band SAR image data, IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1254-1266. https://doi.org/10.1109/36.628792
  25. Schuler, D.L., J.S. Lee, D. Kasilingam, and G. Nesti, 2002. Surface Roughness and Slope Measurements using Polarimetric SAR Data, IEEE Transactions on Geoscience and Remote Sensing, 40(3): 687-698. https://doi.org/10.1109/TGRS.2002.1000328
  26. Ulaby, F.T., R.K. Moore, and A.K. Fung, 1982. Microwave Remote Sensing Volume II, Addison-Wesley, Reading, MA.
  27. Ulaby F.T., 1998. SAR Biophysical Retrievals: Lessons learned and challenges to overcome, Proc. of 2nd Int. Workshop on Retrieval of Bio-&Geo-Physical Parameters from SAR Data for Land Applications, ESTEC, The Netherlands, Oct. 21-23.
  28. Ulaby, F.T. and C. Elachi, 1990. Radar polarimetry for geoscience applications, Artech House, Norwood, MA.
  29. Wagner, W., G. Lemoine, and H. Rott, 1999. A method for estimating soil moisture from ERS scatterometer and soil data - Empirical data and model results, Remote Sensing of Environment, 70(2): 191-207. https://doi.org/10.1016/S0034-4257(99)00036-X
  30. Wang, J.R. and T.J. Schmugge, 1980. An empirical model for the complex dielectric permittivity of soils as a function of water content, IEEE Transactions on Geoscience and Remote Sensing, GE-18(4): 288-295. https://doi.org/10.1109/TGRS.1980.350304
  31. Wen J. and Z. Su, 2003. A time series based method for estimating relative soil moisture with ERS wind scatterometer data. Geophysical Research Letter, 30.1397, doi:10.1029/2002GL016557.
  32. Wu, T.D., K.S. Chen, J. Shi, and A.K. Fung, 2001. A transition model for the reflection coefficient in surface scattering, IEEE Transactions on Geoscience and Remote Sensing, 39(9): 2040-2050. https://doi.org/10.1109/36.951094
  33. Zribi, M. and M. Dechambre, 2002. A new empirical model to retrieve soil moisture and roughness from radar data, Remote Sensing of Environment, 84(1): 42-52. https://doi.org/10.1016/S0034-4257(02)00069-X