An empirical algorithm for the retrieval of soil moisture content and surface Root Mean Square (RMS) height from remote.ly sensed radar data was developed using scatterorneter data. The algorithm is optimized for bare surfaces and requires two co-polarized channels at a frequency between 1.5 G1lz and 11 GHz. It gives best results for kh <2.5, p, < 35% and 0230°O mitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplify the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI > 0.4) cause the algorithm to underestimate soil moisture and overestimate RMS hc.ight. A simple criteria based on the @v/cJ~v ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometm data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1989 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 3.5% soil moisture.

At equivalent noise levels, speech recognition performance was enhanced and subjectively less effortful in the AV than A-only modality. At equivalent accuracy levels, the dual-task performance decrements (for both tasks) suggest that the noisier AV modality was more effortful than the A-only modality.

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Arid and semiarid rangelands comprise a significant portion of the earth's land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface-atmosphere interactions. This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model calculations for different climates and ecosystems need verification. This requires that the remotely sensed data and model computations be evaluated with ground-truth data collected at the same areal scales. The present study (MONSOON 90) attempts to address this issue for semiarid rangelands. The experimental plan included remotely sensed data in the visible, near-infrared, thermal, and microwave wavelengths from ground and aircraft platforms and, when available, from satellites. Collected concurrently were ground measurements of soil moisture and temperature, energy and water fluxes, and profile data in the atmospheric boundary layer in a hydrologically instrumented semiarid rangeland watershed. Field experiments were conducted in 1990 during the dry and wet or "monsoon season" for the southwestern United States. A detailed description of the field campaigns, including measurements and some preliminary results are given.