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The positions of the ground track of the aircraft and of the aircraft sensor footprints, in particular the metric camera and the radar scatterometer on the C-130 aircraft, are estimated by a program called ACTRK. The program uses the altitude, speed, and attitude informaton contained in the radar scatterometer data files to calculate the positions. The ACTRK program is documented.
The response of the microwave scatterometer to meter scale roughness of first year sea ice was investigated. An empirical, one dimensional model was developed to predict the back scattered power from ridged first year sea ice of known surface topography, by interpreting the surface as a series of angular facets and summing the proportional power from each facet. The model is evaluated by comparing the predicted values to scatterometer measurements obtained during NASA's Sea Ice Radar Experiment (SIRE). The input to the model is surface slope, which for SIRE was calculated from the ice topography, as measured by an airborne laser profiler. The correlation between the predicted and the measured power is .62 to .68. It is concluded that the model adequately describes the backscatter from regions of first year sea ice whose dimensions are of a few tens of meters.
A set of hardware similar to the SEASAT A configuration requirement, suitable for installation and operation aboard a NOAA-D bus and a budgetary cost for one (1) protoflight model was provided. The scatterometer sensor is conceived as one of several sensors for the Navy Remote Ocean Sensing System (NROSS) Satellite Program. Deliverables requested were to include a final report with appropriate sketches and block diagrams showing the scatterometer design/configuration and a budgetary cost for all labor and materials to design, fabricate, test, and integrate this hardware into a NOAA-D satellite bus. This configuration consists of two (2) hardware assembles - a transmitter/receiver (T/R) assembly and an integrated electronics assembly (IEA). The T/R assembly as conceived is best located at the extreme opposite end of the satellite away from the solar array assembly and oriented in position to enable one surface of the assembly to have unobstructed exposure to space. The IEA is planned to be located at the bottom (Earth viewing) side of the satellite and requires a radiating plate.
The Seasat scanning multichannel microwave radiometer (SMMR) is able to measure the wind speed at the ocean surface through the change in ocean surface microwave emissivity caused by the wind. In this paper the SMMR-derived wind speeds are compared to the wind speeds derived from an active microwave scatterometer also aboard the Seasat, the Seasat A scanning scatterometer (SASS). Four orbits that passed over the severe storm that damaged the Queen Elizabeth II are examined in detail. These orbits and five others were used to investigate effects which degrade the SMMR wind retrievals. When the data are filtered for such effects, it is found that the SMMR winds agree with the SASS winds with a scatter (1 sigma) of less than 2 m/s about a bias of 1 to 2 m/s.
Four frequency like polarized scatterometer data over a sand dune field and a volcanic field were analyzed to understand the scattering characteristics of these two terrain types as a function of frequency and incidence angle. For the frequency range studied (400 MHz to 13.3 GHz) unvegetated sand dunes are specular reflectors that return an echo to the radar antenna particularly when the geometry is such that the dune slope is nearly perpendicular to the antenna. Vegetation on the dunes can cause significant backscatter. Lava flows are strong diffuse scatterers which have backscatter values similar to coniferous forest at certain frequencies. Different observation frequencies are required for different situations. The scatterometer data correlate well with radar images.
Airborne L- and C-band scatterometer data, taken over both vegetation-covered and bare fields, were systematically analyzed and theoretically reproduced, using a recently developed model for calculating radar backscattering coefficients of rough soil surfaces. The results show that the model can reproduce the observed angular variations of radar backscattering coefficient quite well via a least-squares fit method. Best fits to the data provide estimates of the statistical properties of the surface roughness, which is characterized by two parameters: the standard deviation of surface height, and the surface correlation length. In addition, the processes of vegetation attenuation and volume scattering require two canopy parameters, the canopy optical thickness and a volume scattering factor. Canopy parameter values for individual vegetation types, including alfalfa, milo and corn, were also determined from the best-fit results. The uncertainties in the scatterometer data were also explored.
The digital design of an adaptive digital filter to be employed in the processing of microwave remote sensor data was developed. In particular, a complex demodulation approach was developed to provide narrow band power estimation for a proposed Doppler scatterometer system. This scatterometer was considered for application in the proposed National Oceanographic survey satellite, on an improvement of SEASAT features. A generalized analysis of complex diagrams for the digital architecture component of the proposed system.
The NASA/JSC ground scatterometer system was used in a row structure and row direction effects experiment to understand these effects on radar remote sensing of soil moisture. Also, a modification of the scatterometer system was begun and is continuing, to allow cross-polarization experiments to be conducted in fiscal years 1982 and 1983. Preprocessing of the 1978 agricultural soil moisture experiment (ASME) data was completed. Preparations for analysis of the ASME data is fiscal year 1982 were completed. A radar image simulation procedure developed by the University of Kansas is being improved. Profile soil moisture model outputs were compared quantitatively for the same soil and climate conditions. A new model was developed and tested to predict the soil moisture characteristic (water tension versus volumetric soil moisture content) from particle-size distribution and bulk density data. Relationships between surface-zone soil moisture, surface flux, and subsurface moisture conditions are being studied as well as the ways in which measured soil moisture (as obtained from remote sensing) can be used for agricultural applications.
The role of canopy structure on microwave backscattering is studied. Structure refers to the size, orientation, and vertical placement of scatterers in the canopy. Models to predict the backscattering coefficient, SIGMAO, of vegetation with explicit biophysical and explicit polarization-dependent parameters were developed. Preparation for field measurements with radar scatterometers was completed. Technical progress included: (1) the modification of the Attema and Ulaby (1977) model and its multilayer variation by Hoekman et al. (1983) to include polarization explicitly (i.e., to allow for separate backscattering cross sections, SIGMA, and extinction cross sections, Q, for each polarization), (2) the investigation of the modified model to isolate canopy element orientation parameters by the rationing of SIGMAO measurements for different polarization combinations, (3) the development of expressions for bistatic scattering and canopy-substrate scattering to supplement the models, (4) the performance of sensitivity analyses on these models, (5) the modification of the Attema and Ulaby model to alloy for changes, (6) the use of the modified model with a seasonal corn data set from Kansas (Eger et al., 1983), and (7) the initiation of preparations for empirical measurements with radar spectrometer and the Mobile Radar Scatterometer in irrigated cropland to test the models.
The terrain analysis software package was restructured and documentation was added. A program was written to test Johnson Space Center's four band scatterometer data for spurious signals data. A catalog of terrain roughness statistics and calibrated four frequency multipolarization scatterometer data is being published to support the maintenance of Death Valley as a radar backscatter calibration test site for all future airborne and spacecraft missions. Test pits were dug through sand covered terrains in the Eastern Sahara to define the depth and character of subsurface interfaces responsible for either backscatter or specular response in SIR-A imagery. Blocky sandstone bedrock surfaces at about 1 m depth were responsible for the brightest SIR-A returns. Irregular very dense CaCO3 cemented sand interfaces were responsible for intermediate grey tones. Ancient river valleys had the weakest response. Reexamination of SEASAT l-band imagery of U.S. deserts continues.
A two frequency microwave radar technique for the remote sensing of directional ocean wave spectra and surface currents is investigated. This technique is conceptually attractive because its operational physical principle involves a spatial electromagnetic scattering resonance with a single, but selectable, long gravity wave. Multiplexing of signals having different spacing of the two transmitted frequencies allows measurements of the entire long wave ocean spectrum to be carried out. A new scatterometer is developed and experimentally tested which is capable of making measurements having much larger signal/background values than previously possible. This instrument couples the resonance technique with coherent, frequency agility radar capabilities. This scatterometer is presently configured for supporting a program of surface current measurements.
Laboratory measurements using both wave probes (contact and optical) and radar confirm the existence of a new type of overshoot phenomenon in the wind-wave development processes. This type of overshoot, which occurs at gravity-capillary wavelengths, is practically a function of local wind stress only. It is named the overshoot phenomenon of the third kind. With the existence of this overshoot it becomes obvious that the assumed simple power-law relationship between the rms value of surface slope at a fixed wavelength and the wind stress is untenable. Since such a relationship is one of the key assumptions invoked in the development of algorithms used in scatterometry, the published scatterometer data are reexamined, pointing out the implications of this overshoot phenomenon. Recommendations for improvement of the performance of the scatterometer are also made in light of this new understanding of the wind-wave development processes.
The relationship between the ocean's normalized radar cross section (NRCS) at 14.6 GHz and the surface wind vector is derived using the 3 months of Seasat microwave scatterometer (SASS) measurements. The derivation is based on the statistics of the SASS observations, and no in situ measurements are required, other than a mean global wind speed, which comes from climatology. The frequency distribution of the global wind vectors observed by SASS is assumed to be a bivariate normal probability function. A NRCS model function is found that maps the assumed wind vector statistics into the observed SASS NRCS statistics. This function is compared with a NRCS model coming from the Joint Air Sea Interaction Experiment (JASIN) and with aircraft scatterometer measurements. The results indicate that the statistically derived NRCS model is an improvement over the JASIN model, which was based on a limited number of in situ anemometer measurements.
The variability of the mesoscale wind fields near coastlines which can be caused by mountains that shadow offshore wind and by valleys that enhance them. These wind, provide relatively fixed patterns that must be considered in the development of algorithms for future spaceborne scatterometer systems; mesoscale variability over the offshore regions is random and must be averaged out for forecasting yet nearshore fixed patterns are treated differently. Before the patterns of interest can be defined quantitatively, the scattering response of the ocean to winds at the L-band frequency and SIR-B angles of incidence must be developed from the SIR-B data. Patterns can be analyzed on the images both in regions selected for high probability of the occurrence of suitable patterns, and in other regions where the patterns are observed. The patterns are analyzed for topographic effects and the distance to sea over which these effects cause variations in the oceanic wind patterns. The results are interpreted in terms of quantitative description of the processes involved and in for need of modifications of future scatterometer algorithms.
The scheduled February 1985 launch of a radar altimeter aboard the U.S. Navy satellite Geosat has motivated an in-depth investigation of wind speed retrieval from satellite altimeters. The accuracy of sea surface wind speed estimated by the Seasat altimeter is examined by comparison with wind speed estimated by the Seasat scatterometer. The intercomparison is based on globally distributed spatial and temporal averages of the estimated wind speed. It is shown that there are systematic differences between altimeter and scatterometer wind speed estimates. These differences are traced to errors in the Seasat altimeter geophysical data record wind speed algorithm. A new algorithm is proposed which yields consistent estimates from the two satellite sensors. Using this new algorithm, the rms difference between spatial and temporal averages of the two wind speed estimates is less than 1 m/s, and their correlation is greater than 0.9.
The NASA Langley Research Center analysis of the airborne microwave remote sensing measurements of Hurricane Allen obtained on August 5 and 8, 1980 is summarized. The instruments were the C-band stepped frequency microwave radiometer and the Ku-band airborne microwave scatterometer. They were carried aboard a NOAA aircraft making storm penetrations at an altitude of 3000 m and are sensitive to rain rate, surface wind speed, and surface wind vector. The wind speed is calculated from the increase in antenna brightness temperature above the estimated calm sea value. The rain rate is obtained from the difference between antenna temperature increases measured at two frequencies, and wind vector is determined from the sea surface normalized radar cross section measured at several azimuths. Comparison wind data were provided from the inertial navigation systems aboard both the C-130 aircraft at 3000 m and a second NOAA aircraft (a P-3) operating between 500 and 1500 m. Comparison rain rate data were obtained with a rain radar aboard the P-3. Evaluation of the surface winds obtained with the two microwave instruments was limited to comparisons with each other and with the flight level winds. Two important conclusions are drawn from these comparisons: (1) the radiometer is accurate when predicting flight level wind speeds and rain; and (2) the scatterometer produces well behaved and consistent wind vectors for the rain free periods.
The history and near-term future of ocean remote sensing to study ocean circulation are examined. Seasat provided the first-ever global data sets of sea surface topography (altimeter) and marine winds (scatterometer) and laid the foundation for the next generation of satellite missions planned for the late 1980s. The future missions are the next generation of altimeter and scatterometer to be flown aboard TOPEX (TOPography EXperiment) and NROSS (Navy Remote Sensing System), respectively. The data from these satellites will be coordinated with measurements made at sea to determine the driving forces of ocean circulation and to study the oceans' role in climate variability. The significance of such studies to such matters as climatic changes, fisheries, commerce, waste disposal, and national defense is noted.
Techniques are presented for detecting and correcting errors in a vector field. These methods employ median filters which are frequently used in image processing to enhance edges and remove noise. A detailed example is given for wind field maps produced by a spaceborne scatterometer. The error detection and replacement algorithm was tested with simulation data from the NASA Scatterometer (NSCAT) project.