• DocumentCode
    1469635
  • Title

    A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion

  • Author

    De Roo, Roger D. ; Du, Yang ; Ulaby, Fawwaz T. ; Dobson, M. Craig

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
  • Volume
    39
  • Issue
    4
  • fYear
    2001
  • fDate
    4/1/2001 12:00:00 AM
  • Firstpage
    864
  • Lastpage
    872
  • Abstract
    Radar backscatter measurements of a pair of adjacent soybean fields at L-band and C-band are reported. These measurements, which are fully polarimetric, took place over the entire growing season of 1996. To reduce the data acquisition burden, these measurements were restricted to 45° in elevation and to 45° in azimuth with respect to the row direction. Using the first order radiative transfer solution as a form for the model of the data, four parameters were extracted from the data for each frequency/polarization channel to provide a least squares fit to the model. For inversion, particular channel combinations were regressed against the soil moisture and area density of vegetation water mass. Using L-band cross-polarization and VV-polarization, the vegetation water mass can be regressed with an R 2=0.867 and a root mean square error (RMSE) of 0.0678 kg/m 2. Similarly, while a number of channels, or combinations of channels, can be used to invert for soil moisture, the best combination observed, namely, L-band VV-polarization, C-band HV- and VV-polarizations, can achieve a regression coefficient of R2=0.898 and volumetric soil moisture RMSE of 1.75%
  • Keywords
    agriculture; backscatter; geophysical techniques; hydrological techniques; moisture measurement; radar cross-sections; remote sensing by radar; soil; vegetation mapping; AD 1996; C-band; L-band; SHF; UHF; agriculture; canopy; crops; hydrology; inversion; polarization; radar polarimetry; radar remote sensing; radar scattering; row direction; semi-empirical backscattering model; soil moisture; soya; soybean; vegetation mapping; water content; water mass; Azimuth; Backscatter; Data acquisition; Data mining; Frequency; L-band; Radar measurements; Radar polarimetry; Soil moisture; Vegetation;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/36.917912
  • Filename
    917912