Radar scatterometry - An active remote sensing tool

Radar scatterometer for measuring scattering coefficient variation with angle, wavelength, and polarizatio

Design data for radars based on 13.9 GHz Skylab scattering coefficient measurements

The author has identified the following significant results. Measurements made at 13.9 GHz with the radar scatterometer on Skylab have been combined to produce median curves of the variation of scattering coefficient with angle of incidence out to 45 deg. Because of the large number of observations, and the large area averaged for each measured data point, these curves may be used as a new design base for radars. A reasonably good fit at larger angles is obtained using the theoretical expression based on an exponential height correlation function and also using Lambert's law. For angles under 10 deg, a different fit based on the exponential correlation function, and a fit based on geometric optics expressions are both reasonably valid

Skylab-4 radar scatterometer measurements over land

The author has identified the following significant results. Analysis of the SL4 S193 scatterometer observations shows that the winter measurements were reasonably consistent with summer measurements. The signals over land fall-off more rapidly in winter than in summer, probably because of the lack of vegetation return in winter; but the winter and summer results over land do not differ enough to cause changes in the general conclusions for the design of radars to be constructed for future space use. No consistent difference was found between snow-covered and snow-free terrain radar returns. The oceanic returns in winter were significantly different from those in summer, with a much less rapid fall-off with angle and a lower return at vertical. This is a true seasonal bias, for the winter seas tend to be much stormier than those in summer

Large space antenna technology applied to radar-imaging, rain-rate measurements, and ocean wind sensing

During the last decade, the utility of spaceborne microwave remote sensing systems for ocean windspeed measurement, ocean wave imaging and sea ice studies was demonstrated. Development of large space antennas offers some interesting possibilities for rain rate measurements, ocean and ice studies, and radar imaging. The joint use of active and passive sensors using the 15 m antenna for ocean, ice, and soil moisture studies; rain rate measurements; and radar imaging is considered. Verification of the frequency agile rain radar concept with Shuttle offers the possibility of much needed rain rate statistics over the ocean

Scanning wind-vector scatterometers with two pencil beams

A scanning pencil-beam scatterometer for ocean windvector determination has potential advantages over the fan-beam systems used and proposed heretofore. The pencil beam permits use of lower transmitter power, and at the same time allows concurrent use of the reflector by a radiometer to correct for atmospheric attenuation and other radiometers for other purposes. The use of dual beams based on the same scanning reflector permits four looks at each cell on the surface, thereby improving accuracy and allowing alias removal. Simulation results for a spaceborne dual-beam scanning scatterometer with a 1-watt radiated power at an orbital altitude of 900 km is described. Two novel algorithms for removing the aliases in the windvector are described, in addition to an adaptation of the conventional maximum likelihood algorithm. The new algorithms are more effective at alias removal than the conventional one. Measurement errors for the wind speed, assuming perfect alias removal, were found to be less than 10%

Determination of Backscattering Sources in Various Targets

The objectives of this research are to identify the primary contributors to 10 GHz radar backscatter from various natural and man-made surfaces and objects, and to use this information in developing new and better models for the scatter. When the true sources are known for the scattering that leads to variation in intensity on radar images, the images (and sets of them) may be interpreted more meaningfully in terms of the variation of parameters of interest for science or application. For example, better interpretation of vegetation images may be possible for yield forecasting and stress detection