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
fDate :
4/1/2001 12:00:00 AM
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;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on