| Monitoring soil moisture dynamics is very important for understanding soil-vegetation interactions in both space and time. The effects of the surface roughness and vegetation covers are well-understood problems in estimating soil moisture with SAR imagery. Retrieving soil moisture information with radar measurements could be is achievable by using the multi-frequency and/or multi-polarization measurements to separate the vegetation and surface roughness effects. The currently available satellites, however, are single polarization, single frequency sensors such as ERS-1/2, Radarsat, and JERS-1. There is a need to develop a technique to estimate soil moisture information from these available data sources at both regional and local scales.In this study, we demonstrate a technique using the multi-temporal C band HH polarized Radarsat SCANSAR data to estimate the relative soil moisture change. The experiment data from SGP97 covered a whole range of vegetation growing season and different type agriculture fields. This technique is mainly involved two steps:1) Vegetation effects correction: We used NDVI (Normalized Difference Vegetation Index) derived from TM and AVHRR measurements for spatial andtemporal variations of vegetation covers at different scales. Using a simple radiative transfer model for vegetation volume scattering and the Integral Equation Model (IBM) for surface scattering with the field in situ measurements as the input, we compared the simulated and SAR measured backscattering coefficients in different agricultural fields. We, then, parameterized a semi-empirical model for the different land surface cover types. This semi-empirical model was applied to minimize the effects of the vegetation volume scattering and extinction in radar measurements.2) Radar incidence angle and surface roughness correction: To make radar incidence correction and eliminate the surface roughness effects, a wide range of surface parameters (soil moisture, surface RMS height, correlation length, incidence angle) was input to the IBM model to simulate the effect of surface roughness and radar incidence angle on the sensitivity of soil moisture to the radar backscattering coefficient. A simple model was established to simulate the effects of incidence angle and surface roughness.3) Establishment of soil moisture change inversion model: According to a modified IBM model simulation results, the bare surface backscattering coefficients can be expressed as a funtion of the dielectric component for a given surface roughness when the surface slope greater than 2.0, which is valid for most nature surface:in above equation, R0 is the surface reflectivity at normal incidence. A( 9 ,sr) is a function of surface roughness and Radar incident angle, and B is only influenced by incident angle. IBM simulation results show that in our analysis incident angle range from 20?to 40? the parameters is almost kept constant, its value is from 1.59-1.61. for parameter A, there is a close relationship exist between A( 9 ,sr) in two different Radar incident angle that can be expressed as:with considering the effects of soil texture, we get the final expression of the inversion model:where mv(t1) , mv(t2) is volumetric soil moisture content in two different temp, c,dis soil type related parameters, and v(t1), S(t2) is coresponding bare soil radarbackscattering coefficients.Inversion results show that for the C band HH polarized Radarsat SCANSAR data with a range of incidence angle from 20 to 40 , the soil moisture change value can be derived with an acceptable accuracy using the above model. The temporal and spatial soil moisture change patterns are associated with rainfall and vegetation cover, as well as the soil hydraulic characteristics.
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