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.

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.

In the spring of 1999 NASA will launch the scatterometer SeaWinds, beginning a 3 year mission to measure the ocean winds. SeaWinds is different from previous spaceborne scatterometers in that it employs a rotating pencil-beam antenna as opposed to fixed fan-beam antennas. The scanning beam provides greater coverage but causes the wind retrieval accuracy to vary across the swath. This thesis develops a filed-wise wind retrieval algorithm to improve the overall wind retrieval accuracy for use with SeaWinds data. In order to test the field-wise wind retrieval algorithm, methods for simulating wind fields are developed. A realistic approach interpolates the NASA Scatterometer (NSCAT) estimates to fill a SeaWinds swath using optimal interpolation along with linear wind filed models. The two stages of the field-wise wind retrieval algorithm are filed-wise estimation and field-wise ambiguity selection. Field-wise estimation is implemented using a 22 parameter Karhunen-Loeve (KL) wind field model in conjunction with a maximum likelihood objective function. An augmented multi-start global optimization is developed which uses information from the point-wise estimates to aid in a global search of the objective function. The local minima in the objective function are located using the augmented multi-start search techniques and are stored as field-wise ambiguities. The ambiguity selection algorithm uses a field-wise median filter to select the field-wise ambiguity closest to the true wind in each region. Point-wise nudging is used to further improve the filed-wise estimate using information from the point-wise estimates. Combined, these two techniques select a good estimate of the wind 95% of the time. The overall performance of the field-wise wind retrieval algorithm is compared with the performance of the current point-wise techniques. Field-wise estimation techniques are shown to be potentially better than point-wise techniques. The field-wise estimates are also shown to be very useful tools in point-wise ambiguity selection since 95.8%-96.6% of the point-wise estimates closest to the field-wise estimates are the correct aliases.

Oceanic microwave remote sensing provides the data necessary for the estimation of significant geophysical parameters such as the near-surface vector wind. To obtain accurate estimates, a precise understanding of the measurements is critical. This work clarifies and quantifies specific uncertainties in the scattered power measured by an active radar instrument. While there are many sources of uncertainty in remote sensing measurements, this work concentrates on three significant, yet largely unstudied effects. With a theoretical derivation of the backscatter from an ocean-like surface, results from this dissertation demonstrate that the backscatter decays with surface roughness with two distinct modes of behavior, affected by the size of the footprint. A technique is developed and scatterometer data analyzed to quantify the variability of spaceborne backscatter measurements for given wind conditions; the impact on wind retrieval is described in terms of bias and the Cramer-Rao lower bound. The probability density function of modified periodogram averages (a spectral estimation technique) is derived in generality and for the specific case of power estimates made by the NASA scatterometer. The impact on wind retrieval is quantified.

Because the earth's cryosphere influences global weather patterns and climate, the scientific community has had great interest in monitoring this important region. Microwave remote sensing has proven to be a useful tool in estimating sea and glacial ice surface characteristics with both scatterometers and radiometers exhibiting high sensitivity to important ice properties. This dissertation presents an array of studies focused on extracting key surface features from multisensor microwave data sets. First, several enhanced resolution image reconstruction issues are addressed. Among these are the optimization of the scatterometer image reconstruction (SIR) algorithm for NASA scatterometer (NSCAT) data, an analysis of Ku-band azimuthal modulation in Antarctica, and inter-sensor European Remote Sensing Satellite (ERS) calibration. Next, various methods for the removal of atmospheric distortions in image reconstruction of passive radiometer observations are considered. An automated algorithm is proposed which determines the spatial extent of sea ice in the Arctic and Antarctic regions from NSCAT data. A multisensor iterative sea ice statistical classification method which adapts to the temporally varying signatures of ice types is developed. The sea ice extent and classification algorithms are adopted for current SeaWinds scatterometer data sets. Finally, the automated inversion of large-scale forward electromagnetic scattering of models is considered and used to study the temporal evolution of the scattering properties of polar sea ice.