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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

This thesis provides a new explanation for the behaviour of radar backscatter of forests using vegetation structure models from the field of macroecology. The forests modelled in this work are produced using allometry-based ecological models with backscatter derived from the parameterisation of a radiative transfer model. This work is produced as a series of papers, each portraying the importance of macroecology in defining the forest radar response. Each contribution does so by incorporating structural and dynamic effects of forest growth using one of two allometric models to expose variations in backscatter as a response to vertical and horizontal forest profiles. The major findings of these studies concern the origin of backscatter saturation effects from forest SAR surveys. In each work the importance of transition from Rayleigh to Optical scattering, combined with the scaling effects of forest structure, is emphasised. These findings are administered through evidence including the transition’s emergence as the region of dominant backscatter in a vertical profile (according to a dominant canopy scattering layer), also through the existence of a two trend backscatter relationship with volume in the shape of the typical “saturation curve” (in the absence of additional attenuating factors). The importance of scattering regime change is also demonstrated through the relationships with volume, basal area and thinning. This work’s findings are reinforced by the examination of the relationships between forest height and volume, as collective values, providing evidence to suggest the non-uniqueness of volume-toheight relationships. Each of the studies refer to growing forest communities not single trees, so that unlike typical studies of radar remote sensing of forests the impact of the macroecological structural aspects are more explicit. This study emphasises the importance of the overall forest structure in producing SAR backscatter and how backscatter is not solely influenced by electrical properties of scatteres or the singular aspects of a tree but also by the collective forest parameters defining a dynamically changing forest.

Radar observations of the ocean surface are becoming increasingly important. Common applications are wind retrieval and global weather forecasting and characterization. Because of the common use of ocean radar measurements, it is important to understand the sensitivity of the backscatter to both radar parameters and surface parameters. At near-normal incidence angles, it has been assumed that the radar backscatter exhibits little or no azimuth dependence (Colton, 1989). However, recent data taken by the BYU YSCAT radar system suggests that this is not the case. At an incidence angle of 10°, the YSCAT radar data shows from a fraction of a decibel to up to 10 decibels of azimuth modulation, depending on the surface conditions. In this thesis, a physical optics approach is used with a two-dimensional surface model to derive the electromagnetic backscatter from the ocean surface. If the waves on the ocean surface are directed, azimuth modulation is predicted at near-normal incidence angles. The effects of surface and radar parameters on the azimuth modulation are studied, and the results are compared to data taken by the YSCAT radar system. It is shown that the theory correctly predicts of the shape of the curve when the normalized radar cross-section is plotted as a function of azimuth angle. The theory also predicts the correct trend of the modulation magnitude as function the surface roughness. However, the simplifications in the model limit its prediction of the frequency dependence of the modulation. Relaxing some of the assumptions of the model is likely to correct this problem.

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La rétrodiffusion associée aux effets de géométrie de surface à des angles d’incidence élevés (62°—76°) et pour quatres angles azimutaux (Q0, 3Q0, 60° et 90°) a été étudiée pour des champs de maïs du Québec (Canada) à l’aide de 3 images du ROS-C du Centre canadien de télédétection. Selon les conditions de drainage des champs, le couvert végétal apporte une plus ou moins grande contribution à la rétrodiffusion. L’effet de géométrie de surface est nettement plus prononcé (amplitude 3,2 dB) pour les champs de maïs sans drainage souterrain que ceux ayant un drainage souterrain (1,5 dB). Le contraste entre ces deux types de champ est rattaché aux différences de pente du couvert végétal ainsi qu’aux variations de biomasse.

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.

YSCAT is a unique ultrawideband microwave scatterometer developed to investigate the sea surface under a variety of environmental and radar parameters. The YSCAT94 experiment consisted of a six month deployment on the WAVES research tower operated by the Canada Center for inland Waters (CCIW). Over 3500 hours of data were collected at 2Γ 3.05Γ 5.3Γ 10.02Γ and 14 GHz and at a variety of wind speeds, relative azimuth angles, and incidence angle. A low wind speed "rolloff" of the normalized radar cross section (σ°) in YSCAT94 data is found and quantified. The rolloff wind speedΓ γΓ is estimated through regression estimation analysis using an Epanechnikov kernel. For YSCAT94 data, the rolloff is most noticeable at mid-range incidence angles with γ values ranging from 3 to 6 m/s. In order to characterized YSCAT94 backscatter distributions, a second order polynomial in log space is developed as a model for the probability of the radar cross sectionΓρ(σ°). Following Gotwols and ThompsonΓρ(σ°) is found to adhere to a log-normal distribution for horizontal polarization and a generalized log-normal distribution for vertical polarization. If ρ(α|σ°) is assumed to be Rayleigh distributed, the instantaneous amplitude distribution ρ(α) is found to be the integral of a Rayleigh/generalized log-normal distribution. A robust algorithm is developed to fit this probability density function to YSCAT94 backscatter distributions. The mean and variance of the generalized log-normal distribution are derived to facilitate this algorithm. Over 2700 distinct data cases sorted according to five different frequencies, horizontal and vertical polarizations, upwind and downwind, eight different incidence angles Γ1-10 m/s wind speeds, and 0.1-0.38 mean wave slope are considered. Definite trends are recognizable in the fitted parameters a1Γ a2Γ and C of the Rayleigh/generalized log-normal distribution when sorted according to wind speed and mean wave slope. At mid-range incidence angles, the Rayleigh/generalized log-normal distribution is found to adequately characterize both low and high amplitude portions of YSCAT94 backscatter distributions. However, at higher incidence angels (50°and 60°) the more general Weibull/generalized log-normal distributions is found to better characterized the low amplitude portion of the backscatter distributions.

In May of 2000 Jason-1, a joint project between NASA and the French space agency CNES, will be launched. Its mission is to continue the highly successful gathering of data which TOPEX/Poseidon has collected since August of 1992. The main goal of Jason-1 is to achieve higher accuracy in measuring the mean sea level (MSL). In order to do so, the electromagnetic (EM) bias must be estimated more accurately because it is the largest contributing error. This thesis presents two different studies which add to the knowledge and improve estimation of the EM bias, and thus assists Jason-1 in achieving its primary goal. Oceanographic data collected from two different experiments are analyzed; on in the Gulf of Mexico (GME) and the other in Bass Strait, Australia (BSE). The first study is a spatial analysis of the backscattered power versus the phase of the wave. Its purpose is to determine why the normalized EM bias stops increasing and levels out at high wind speeds (about 11 m/s) and then decreases at higher wind speeds. Two possible causes are investigated. First, it could be due to a shift in the backscatter power modulation to the forward or rear face of the wave crests. Second, it may be due to the backscatter power becoming more homogeneous throughout the wave profile. This study is novel because it uses the knowledge of the spatial distribution of both the backscatter and wave displacement for the study of the EM bias. Both contribute to the EM bias decrease, but the latter cause seems to be the dominant effect. This study is performed on GME data. The second study uses two different nonparametric regression (NPR) techniques to estimate the EM bias. A recent study of satellite data from the TOPEX/Poseidon altimeter supports that the bias is modeled better using NPR regression. A traditional parametric fit is compared to two NPR techniques with GME data. The parametric fit is a variation of NASA's equation used to estimate EM bias for their Geophysical Data Records (GDRs). The two NPR techniques used are the Nadaraya-Watson Regression (NWR) and Local Linear Regression (LLR) estimators. Two smoothing kernel functions are used with each NPR technique, namely the Gaussian and the Epanechnikov kernels. NPR methods essentially consist of statistically smoothing the measured EM bias estimates are compared in the wind and significant wave height plane. Another recent study has shown that wave slope is strongly correlated to EM bias. With this knowledge, EM bias is estimated over several two-dimensional planes which include wave slope in attempt to reduce the residual bias. This portion of the study is performed on GME and BSE data. It is shown that a combination of slope, significant wave height, and wind speed used in conjunction with these NPR methods produces the best EM bias estimate for tower data.