International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / A scatterometer is a radar system designed to make precise measurements of the magnitude of the radar echo scattered from surface. If the measurement is made over the ocean's surface, the surface wind speed and direction can be inferred. In order to better understand the relationship between the radar return and the ocean winds we have developed a unique ultra-wide band research scatterometer known as Yscat. The Yscat radar system is computer controlled, with a separate computer collecting environmental data. During a typical deployment, such as a recently completed 7 month deployment on Lake Ontario, the radar system is required to operate unmanned for weeks at a time, collecting data at a rate of up to 2 GB per week. Controlling such a complex system, and handling such large amounts of data presents a challenging remote operation problem. We used a novel combination of personal computers, telephone controlled switches, modems, and off the shelf software packages to enable us to perform daily monitoring, trouble shooting, and data transfer via a simple telephone connection. Data was stored on 4 mm DAT tapes for weekly pickup by a technician. This paper describes the Yscat system and our approach to control, monitoring, and data storage. While our approach is relatively "low tech", it has been very cost effective. This type of approach may be of interest to other designers of unique instrumentation at remote sites.

RapidScat is a Ku-band scatterometer that was launched September 2014 and is currently operating on the International Space Station. It estimates ocean vector winds through accurate measurement of the normalized radar coefficient (σ0) of the ocean surface. In order to ensure the accuracy of σ0 measurements and consistency with previous Ku-band scatterometers, post-launch calibration and validation is necessary. Calibration and validation is performed using natural land targets, namely the Amazon and Congo rainforests, to complement calibration efforts over the ocean. The σ0 response of the targets is estimated with respect to viewing angle and time of year using previous Ku-band scatterometers. Taking advantage of the ISS orbit, the diurnal response of each target is estimated using RapidScat. Normalizing factors for incidence angle, azimuth angle, local time of day, and time of year are derived from these measured responses. RapidScat σ0 measurements are found to be consistent throughout its mission life with instrumental drift less than 0.3 dB. The effectiveness of slice balancing is evaluated and found to be highly dependent on the pitch of the ISS. Understanding of the diurnal backscatter response and incidence response allow comparison of RapidScat measurements with measurements from the QuikSCAT, NSCAT, and Oceansat-II scatterometers. RapidScat σ0 is found to be biased low compared to QuikSCAT by 0.1--0.3 dB.

Radar backscatter intensity data from the ERS-1 and ERS-2 scatterometers are compared with daily rainfall data in two areas in the Free State province of South Africa. Knowledge of the relation between daily rainfall data and ERS C-band scatterometer data for a specific area can be useful to make reliable soil moisture measurements. The assumption is made that an increase in rainfall will lead to higher radar backscatter data values. This is based on the fact that moisture increases the dielectric properties of surfaces. This leads to higher backscatter intensities when incident radar energy is reflected back to the sensor. Various techniques are used to study the relationship between daily rainfall data and ERS scattrerometer data. It includes correlations, interpolations, visual interpretations, statistical analysis, and a simple model. Weak positive correlations were found between radar and rainfall data in arid areas. This is supported by literature regarding the Sahel. No correlation was found in agricultural areas receiving more rainfall. Vegetation also increases radar backscatter intensities, even in the absence of rain. There is thus a relationship between rainfall and radar data but it is more visible in arid areas and over longer periods of time. / Dissertation (MA)--University of Pretoria, 2008. / Geography, Geoinformatics and Meteorology / MA / Unrestricted

Spaceborne scatterometers have measured the normalized radar cross section (RCS) of the earth's surface for several decades. Two frequencies, C- and Ku-band, have been used in designing scatterometers, such as with the Ku-band NASA Scatterometer (NSCAT) and the C-band Advanced Scatterometer (ASCAT). The scatterometer data record between C- and Ku-band has been disjoint for several decades due to the difficulties in cross calibration of sensors that operate at different frequencies and incidence angles. A model for volume scattering over the Amazon rainforest canopy that includes both the incidence angle and frequency dependence is developed to overcome this challenge in cross calibration. Several models exist for the σ0 incidence angle dependence, however, none of them are based on backscatter physics. This thesis develops a volume scattering model from a simple EM scattering model for cultural vegetation canopies and applies it to the volume scattering of the Amazon rainforest. It is shown that this model has lower variance than previously used models for the incidence angle dependence of σ0, and also enables normalization of σ0 with respect to the incidence angle. In addition, the frequency dependence of σ0 is discovered to be quite sensitive at Ku-band due to the distribution of leaf sizes in the Amazon rainforest. This may limit the accuracy of the model of the frequency dependence of σ0. Although the proposed frequency dependence model may be limited for cross calibrating between C- and Ku-band, it provides the groundwork for future studies.

No description available.

Satellite scatterometers have recently gained popularity due to their unique ability to measure global geophysical data on a daily basis. Increased interest in scatterometry mandates improved design and calibration of these instruments. This dissertation presents new techniques for scatterometer calibration and addresses issues related to the design of future instruments and applications. First, the use of a calibration ground station is considered. A new methodology is established for calibration of SeaWinds, NASA's current scatterometer, using a receive-only ground station. Principles of the methodology are implemented, new analysis techniques developed, and important results obtained for instrument timing, frequency, power, position, and pointing. Second, an investigation into methods for calibration of measurement surface location is conducted. Two new approaches are proposed and results of both approaches using SeaWinds data are provided. Third, measurement correlation, a critical issue related to new scatterometer designs, particularly those which significantly oversample the surface is considered. General statistical expressions for measurement correlation are derived and analysis of the effects on data variance is presented. Finally, a new data simulation model is developed to support instrument and application development. New applications require sophisticated models which are general, yet accurate, enabling them to rapidly and easily simulate data from multiple instruments. The model generates data which is statistically equivalent (in a mean and variance sense) to actual scatterometer measurements by separately accounting for the two main forms of variation present in scatterometer data, multiplicative fading and additive noise, and also accounting for correlation between measurements. The model is valuable for a variety of data applications including image generation and high resolution wind retrieval.

The Ku-band SeaWinds-on-QuikSCAT scatterometer (QuikSCAT) operated continuously from 1999 to 2009. Though its primary mission was to estimate global ocean winds, QuikSCAT has proven useful in a variety of geophysical studies using land backscatter measurements. The end of the primary QuikSCAT mission in 2009 has prompted interest for continuing the QuikSCAT land dataset with other scatterometers. The Oceansat-2 scatterometer (OSCAT), launched in 2009, is a viable candidate for continuing the QuikSCAT time series because of the similarities of both sensors in function and design. An important difference in the sensors is that they operate at slightly different incidence angles. Continuing the time series requires careful cross-calibration of the two sensors. Because the sensor datasets overlapped by only a few weeks in late 2009, the amount of simultaneous data is insufficient to describe temporal and locational variations in the relative calibration, or difference between QuikSCAT and OSCAT measurements. To overcome this limitation, we perform direct and model-based comparisons of temporally-disjoint QuikSCAT and OSCAT global land measurements to describe the relative calibration. Using homogeneous rainforest targets, we also identify drift and azimuthal biases in the OSCAT dataset and present suggestions for removing them. The relative calibration is found to vary locationally by several tenths of a decibel over certain regions. Evidence is presented that suggests the relative calibration is dependent on environmental factors such as vegetation density and freeze-thaw status and results from the different incidence angles of the measurements.

The NASA Scatterometer (NSCAT) was launched August 20, 1996 aboard the National Space Development Agency of Japan's Advanced Earth Observing Spacecraft (ADEOS). NSCAT's primary mission was to measure radar backscatter over the world's oceans. These measurements are used to generate estimates of ocean wind speed and direction. Scatterometers must be calibrated before their measurements are scientifically useful. However, the calibration of NSCAT must be done in orbit. A new methodology for selecting land regions for use in extended target spaceborne scatterometer calibration is first developed. Next, a summary of the calibration technique used in this thesis is presented. While the foundation of this technique was previously developed theoretically, the work in this thesis is its first application for calibration/validation of an on-line spaceborne radar system. The technique is extended to estimate simultaneously NSCAT's calibration and the host spacecraft's attitude error. The attitude references reported by the attitude control system on-board ADEOS are deemed erroneous. Results of this expanded technique, applied under varying assumptions, are presented for consideration. A summary and suggestions for future research conclude this work.

RapidScat is a spaceborne wind scatterometer mounted on the International Space Station (ISS). The RapidScat mission lasted from September 2014 to November 2016. RapidScat enables the measurement of diurnal patterns of sigma-0 measurements. This capability is possible because of the non-sun-synchronous orbit of the ISS, in which the local time of day (LTOD) of sigma-0 measurements gradually shifts over time in any given location. The ISS platform is a relatively unstable platform for wind scatterometers. Because of the varying attitude of the ISS, RapidScat experiences a constant variation of its pointing vector. Variations of the pointing vector cause variations in the incidence angle of the measurement on the ground, which has a direct effect on sigma-0 measurements. In order to mitigate sigma-0 variations caused by incidence angle and LTOD, the dependence of on these parameters is modeled in order to enable a normalization procedure for sigma-0 . These models of sigma-0 dependence are determined in part by comparing RapidScat data with other active Ku-band instruments. The normalization procedure is designed to adjust the mean value of sigma-0 to be constant across the full range of significant parameter values to match the mean of sigma-0 at a particular nominal parameter value. The normalization procedure is tested both in simulation and with real sigma-0 measurements. The simulated normalization procedure is effective at modeling and removing sigma-0 dependence on incidence angle and LTOD over a homogeneous region. The variance in simulated images is reduced by the normalization procedure. The normalization procedure also reduces variance in real backscatter images of the Amazon and an arbitrary region in East Africa.

RapidScat is a pencil-beam wind scatterometer which operated from September 2014 to August 2016. Mounted aboard the International Space Station (ISS), RapidScat experiences significant altitude and attitude variations over its dataset. These variations need to be properly accounted for to ensure accurate calibration and to produce high resolution scatterometer images. Both the antenna pose and the one-way antenna pattern need to be validated. The spatial response function (SRF) is the two-way antenna pattern for a scatterometer combined with the processing and filtering done in the radar system electronics, and is dominated by the two-way pattern. To verify the pointing of the RapidScat antenna, the RapidScat SRF is estimated using on-orbit data. A rank reduced least squares estimate is used, which was developed previously for the Oceansat-2 (OSCAT) scatterometer [1]. This algorithm uses a small, isolated island as a delta function to sample the SRF. The island used is Rarotonga Island of the Cook Islands. The previously developed algorithm is updated to estimate the SRF in terms of beam azimuth and elevation angle rather than in kilometers on the ground. The angle-based coordinate system promotes greater understanding of how the SRF responds to biases and errors in antenna geometry. The estimation process is simulated to verify its accuracy by calculating the SRF for several thousand measurements in the region of Rarotonga. The calculated SRFs are multiplied by a corresponding synthetically created surface and integrated to yield simulated backscatter measurements, with added white noise. The SRF estimation algorithm is then performed. The results of the simulation show that the SRF estimation process yields a close estimate of the original SRF. The antenna pointing is validated by introducing a fixed offset in azimuth angle into the simulation and observing that the SRF is correspondingly shifted in the azimuth-elevation grid. The SRF computed from real data shows that there is an azimuth rotation angle bias of about 0.263 degrees for the inner beam and about 0.244 degrees for the outer beam. Since the SRF is dominated by the two-way antenna pattern, it can be modeled as the product of two identical one-way antenna patterns which are slightly offset from each other due to antenna rotation during the transmit/receive cycle. A method is developed based on this model to derive the one-way antenna pattern from the estimated SRF. Using a Taylor series expansion the one-way antenna pattern is computed from the SRF. The derived pattern recovers the SRF with small error, but there is significant error in the inferred one-way pattern when compared to the pre-launch estimated RapidScat one-way antenna pattern.