This paper demonstrates the technique to estimate ground surface and vegetation scattering components, based on the backscattering model and the radar decomposition theory, under configuration of multi-temporal L-band polarimetric SAR measurement. This technique can be used to estimate soil moisture of vegetated surfaces

Infiltration is a time varying process of water entry into soil. Experiments were conducted using truck based microwave radiometers to observe small plots during and following sprinkler irrigation. Experiments were conducted on a sandy loam soil in 1994 and a silt loam in 1995. Sandy loam soils typically have higher infiltration capabilities than clays. For the sandy loam, the observed brightness temperature (TB) quickly reached a nominally constant value during irrigation. When the irrigation was stopped the TB began to increase as drainage took place. The irrigation rates in 1995 with the silt loam soil exceeded the saturated conductivity of the soil. During irrigation the TB values exhibited a pattern that suggests the occurrence of coherent reflection, a rarely observed phenomena under natural conditions. These results suggested the existence of a sharp dielectric boundary (wet over dry soil) that was increasing in depth with time

Microwave radiometers operating at low frequencies are sensitive to surface soil moisture changes. Few studies have been conducted that have involved multifrequency observations at frequencies low enough to measure a significant soil depth and not be attenuated by the vegetation cover. Another unexplored aspect of microwave observations at low frequencies has been the impact of diurnal variations of the soil moisture and temperature on brightness temperature. In this investigation, observations were made using a dual frequency radiometer (1.4 and 2.65 GHz) over bare soil and corn for extended periods in 1994. Comparisons of emissivity and volumetric soil moisture at four depths for bare soils showed that there was a clear correspondence between the 1 cm soil moisture and the 2.65-GHz emissivity and between the 3-5 cm soil moisture and the 1.4-GHz emissivity, which confirms previous studies. Observations during drying and rainfall demonstrate that new and unique information for hydrologic and energy balance studies can be extracted from these data.

SIR-C L-band measurements over the Little Washita River watershed in Chickasha, Oklahama during 11–17 April 1994 have been analyzed for studying the change of soil moisture in the region. Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05–0.06 cm3/cm3, which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. The other algorithm generally gives a larger fraction of these pixels when the fields are vegetation-covered. The implication and impact of these features are discussed in this article.

During 1992 and 1994 NASA/GSFC, USDA, and Princeton University conducted hydrology field experiments in the Little Washita River watershed near Chickasha, Oklahoma, with a goal of characterizing the spatial and temporal variability of soil moisture using microwave sensors from ground, aircraft, and space platforms. A major objective of these activities included the subsequent incorporation of the microwave-derived soil moisture patterns in models of larger scale water balance and partial area hydrology. While work is continuing to improve the accuracy of microwave soil moisture inversion algorithms for both bare and vegetated soils, the impact of errors in estimated soil moisture on hydrological modeling of the watershed has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework was used to predict surface soil moisture fields for the Little Washita watershed for an eight-day period in June, 1992 which covered a wide range of soil moisture conditions. The model was first driven by meteorological forcing data, and the model-generated soil moisture fields are compared in space and time to those produced for the watershed by the airborne passive microwave ESTAR sensor for the same time period. In a second analysis, the model was initialized by the remote sensing data, and subsequent model predictions of soil moisture are compared to measured values.