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This paper discusses in detail the characteristics of the AIRSAR data acquired, and provides an initial quantitative assessment of the accuracy of the radar inversion algorithms under these vegetated conditions.
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In this paper, a low-frequency UHF/VHF radar mission concept is presented and technology challenges to implement it are discussed. This mission concept is currently being studied under a NASA/ESTO instrument incubator program project. The progress of several aspects of the project are discussed.
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The success of the GPS atmospheric occultation technique, first performed on the GPS/MET flight experiment aboard NASA's MicroLab-1 spacecraft, has established the potential of active radio occultation sounding.
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A concept has been studied for remote sensing of sea surface salinity and soil moistute from space using a large deployable mesh antenna system.
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During several field campaigns in spring and summer of '94, the NASA/JPL air-borne sythetic aperature radar (AIRSAR) collected data over the southern and northern study sites of BOREAS. Among the areas over which radar data were collected was the young jack pine tower site, which is generally characterized as have shot but closely space trees with a dense crown layer.
The influence of ocean on the continental rainfall, in seasonal and intraseasonal scales were examined. A method has been developed and validated to estimate IWT from the product of SWV and IWV.
preliminary results will be shown to demonstrate the application of spacebased IMT and fresh water flux in ocean-atmosphere-land interaction studies, such as the hydrologica balance on Amazon rainfall and Indian monsoon.
An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere ((Theta)) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data ((Theta) calculated from rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of (Theta) derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation comparedwith rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.
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No abstract available