ESTIMATING SURFACE SOIL-MOISTURE AND LEAF-AREA INDEX OF A WHEAT CANOPY USING A DUAL-FREQUENCY (C AND X-BANDS) SCATTEROMETER

Since microwave remote sensing techniques are insensitive to cloud cov er, they can, overcome this strong limitation, of optical remote sensi ng. As in the optical domain, their use for monitoring vegetation cano pies requires the development of suitable inversion algorithms. These would allow the estimation of variables such as LAI from radar data. T his article investigates the possible use of a semiempirical water-clo ud model in an inversion scheme. Using radar data obtained with a grou nd-based dual-frequency (C and X bands, 5.7 and 3.3 cm wavelength, res pectively) scatterometer on, experimental winter wheat fields, it is f irst verified that a semiempirical water-cloud model can adequately si mulate the backscattering coefficients obtained over the growing seaso n, as a function of LAI and surface soil moisture. Then it is shown th at the model can be numerically inverted. This yields simultaneous est imation of LAI and surface soil moisture, the standard deviations of t he residuals being respectively 0.64 m(2) m(-2) and 0.065 cm(3) cm(-3) . Finally, the influence of radar measurement errors on the inversion scheme is quantified by means of a simulation study. This shows that a 1 dB accuracy of the radar is required for a 1 m(2) m(-2) precision, of the estimated LAI.