Over the last three decades, microwave remote sensing has played a significant role in ocean surface wind measurement, and several scatterometer missions have flown in space since early 1990's. Although they have been extremely successful for measuring ocean surface winds with high accuracy for the vast majority of marine weather conditions, unfortunately, the conventional scatterometer cannot measure extreme winds condition such as hurricane. The SeaWinds scatterometer, onboard the QuikSCAT satellite is NASA's only operating scatterometer at present. Like its predecessors, it measures global ocean vector winds; however, for a number of reasons, the quality of the measurements in hurricanes are significantly degraded. The most pressing issues are associated with the presence of precipitation and Ku-band saturation effects, especially in extreme wind speed regime such as tropical cyclones (hurricanes and typhoons). Under this dissertation, an improved hurricane ocean vector wind retrieval approach, named as Q-Winds, was developed using existing SeaWinds scatterometer data. This unique data processing algorithm uses combined SeaWinds active and passive measurements to extend the use of SeaWinds for tropical cyclones up to approximately 50 m/s (Hurricane Category-3). Results show that Q-Winds wind speeds are consistently superior to the standard SeaWinds Project Level 2B wind speeds for hurricane wind speed measurement, and also Q-Winds provides more reliable rain flagging algorithm for quality assurance purposes. By comparing to H*Wind, Q-Winds achieves ~9% of error, while L2B-12.5km exhibits wind speed saturation at ~30 m/s with error of ~31% for high wind speed ( > 40 m/s).

After the launch of NASA's SeaWinds scatterometer in 1999, a radiometer function was implemented in the Science Ground Data Processing Systems to allow the measurement of the earth's microwave brightness temperature. This dissertation presents results of a comprehensive validation to assess the quality of QRad brightness temperature measurements using near-simultaneous ocean Tb comparisons between the SeaWinds on QuikSCAT (QRad) and WindSat polarimetric radiometer on Coriolis. WindSat was selected because it is a well calibrated radiometer that has many suitable collocations with QuikSCAT; and it has a 10.7 GHz channel, which is close to QRad frequency of 13.4 GHz. Brightness temperature normalizations were made for WindSat before comparison to account for expected differences in Tb with QRad because of incidence angle and channel frequency differences. Brightness temperatures for nine months during 2005 and 2006 were spatially collocated for rain-free homogeneous ocean scenes (match-ups) within 1° latitude x longitude boxes and within a ± 60 minute window. To ensure high quality comparison, these collocations were quality controlled and edited to remove non-homogenous ocean scenes and/or transient environmental conditions, including rain contamination. WindSat and QRad Tb's were averaged within 1° boxes and these were used for the radiometric inter-calibration analysis on a monthly basis. Results show that QRad calibrations are stable in the mean within ± 2K over the yearly seasonal cycle.

In recent years, the scientific community has expressed interest in the ability to observe global climate indicators such as polar sea ice. Advances in microwave remote sensing technology have allowed a large-scale and detailed study of sea ice characteristics. This thesis provides the analysis and development of sea ice mapping algorithms for the SeaWinds scatterometer. First, an in-depth analysis of the Remund Long (RL) algorithm for SeaWinds is performed. From this study, several improvements are made to the RL algorithm which enhance its performance. In addition, a new method for automated polar sea ice mapping is developed for the SeaWinds instrument. This method is rooted in Bayes decision theory, and incorporates an adaptive model for seasonally fluctuating sea ice and ocean microwave signatures. The new approach is compared to the RL algorithm, to passive microwave data, and to high-resolution SAR imagery for validation.

Ku-band dual-polarization radar backscatter measurements from the SeaWinds on QuikScat scatterometer and microwave radiometer measurements from the Special Sensor Microwave/Imager (SSM/I) are used to determine periods of surface melt and freeze in the Antarctic ice-shelves. The normalized radar backscatter (sigma-0) and backscatter polarization ratio (PR) are used in the maximum likelihood estimation of the ice-state. This method is used to infer the daily ice-surface conditions for 25 selected study points located on the Ronne, Ross, Larsen, Fimbul, Amery, and Shackleton Ice-shelves. The temporal and spatial variations of the radar response are also observed for various neighborhood sizes surrounding each given point during the study period. Criteria for determining the dates of melt-onset and freeze-up for each Austral summer are also presented. Validation of the ice-state and melt-onset date estimates is performed by analyzing corresponding brightness temperature (Tb) measurements from radiometers. QuikScat sigma-0 measurements from 1999 through 2003 are analyzed and it is shown that Ku-band scatterometers are very useful for determining periods of melt in Antarctic ice-sheets and provide high temporal and spatial resolution ice-state estimates. These estimates can be important for long-term studies of the climatic effects of the seasonal and inter-annual melting of the Antarctic ice-sheets. The SeaWinds on QuikScat (QuikScat) and SeaWinds on ADEOS-2 (SeaWinds) scatterometers are identical radar sensors on different spaceborne platforms traversing similar orbits. QuikSCAT and SeaWinds data are used to infer near-surface wind vectors, polar sea-ice extent, polar-ice melt events, among others. In order to verify the relative calibration of these two sensors a simple cross-calibration method is implemented based on land measurements. A first-order polynomial model for the incidence angle dependence of sigma-0 is used to account for biases in the sigma-0 measurements. This model is applied to selected regions of the Amazon rainforest and the Sahara desert. It is shown that the two sensors are well calibrated. Additionally, evidence of a previously presumed diurnal cycle in the Amazon rainforest backscatter is given.

Ku-band dual-polarization radar backscatter measurements from the SeaWinds on QuikScat (QuikScat) and SeaWinds on ADEOS-2 (SeaWinds) scatterometers are used to classify the melt state and estimate melt severity in Greenland. Backscatter measurements are organized into high temporal and high spatial resolution images created using the Scatterometer Image Reconstruction (SIR) algorithm and a new temporal data segmentation technique. Melt detection is performed using a layered electromagnetic model combined with a Markov chain model. The new melt detection method allows classification of the snow-pack into three states: melt, refreeze, and frozen. Melt severity and refreeze severity indexes are also developed. The melt detection methods developed in this thesis are verified by using a one-dimensional geophysical/electromagnetic model simulation of the snow-pack under melting conditions and by comparison with in situ weather station data at the ETH Camp in western Greenland. The diurnal cycle of backscatter measurements is also analyzed at this location. The melt detection and estimation method is applied to the entire Greenland ice-sheet. The resulting melt classifications and melt severity indexes are used to generate a number of maps outlining the features of the 2003 melt season. Good agreement of the melt severity and a 1978 SASS Greenland ice facies map is observed.

The NASA SeaWinds scatterometer is a radar remote sensor which operates on two satellites; NASA's QuikSCAT launched in June 1999 and on Japan's ADEOS-II satellite launched in December 2002. The purpose of SeaWinds is to provide global measurements of the ocean surface wind vector. On QuikSCAT, a ground data processing algorithm was developed, which allowed the instrument to function as a QuikSCAT Radiometer (QRad) and measure the ocean microwave emissions (brightness temperature, Tb) simultaneously with the backscattered power. When SeaWinds on ADEOS was launched, this same algorithm was applied, but the results were anomalous. The initial SRad brightness temperatures exhibited significant, unexpected, ascending/descending orbit Tb biases. This thesis presents an empirical correction algorithm to correct the anomalous SeaWinds Radiometer (SRad) ocean brightness temperature measurements. I use the Advanced Microwave Scanning Radiometer (AMSR) as a brightness temperature standard to calibrate and then, with independent measurements, validate the corrected SRad Tb measurements. AMSR is a well-calibrated multi-frequency, dual-polarized microwave radiometer that also operates on ADEOS-II. These results demonstrate that, after tuning the Tb algorithm, good quality SRad brightness temperature measurements are obtained over the oceans.

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.

Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributers to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events. Ambiguity selection errors are partly due to the "nudging" step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging. Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not "go to zero" beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold. Rain is the largest weather-related contributer to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous wind/rain (SWR) estimation procedure can improve wind estimates during rain, while providing a scatterometer-based rain rate estimate. SWR also affords improved rain flagging for low to moderate rain rates. QuikSCAT-retrieved rain rates correlate well with TRMM PR instantaneous measurements and TMI monthly rain averages. SeaWinds rain measurements can be used to supplement data from other rain-measuring instruments, filling spatial and temporal gaps in coverage.

The Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure caused a premature end to the ADEOS-II satellite mission. SeaWinds operates at a frequency of 13.4 GHz, and was originally designed to measure the speed and direction of the ocean surface wind vector by relating the normalized radar backscatter measurements to the near surface wind vector through a geophysical model function (GMF). In addition to the backscatter measurement capability, SeaWinds simultaneously measures the polarized radiometric emission from the surface and atmosphere, utilizing a ground signal processing algorithm known as the QuikSCAT / SeaWinds Radiometer (QRad / SRad). This dissertation presents the development and validation of a mathematical inversion algorithm that combines the simultaneous active radar backscatter and the passive microwave brightness temperatures observed by the SeaWinds sensor to retrieve the oceanic rainfall. The retrieval algorithm is statistically based, and has been developed using collocated measurements from SeaWinds, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, and Numerical Weather Prediction (NWP) wind fields from the National Centers for Environmental Prediction (NCEP). The oceanic rain is retrieved on a spacecraft wind vector cell (WVC) measurement grid that has a spatial resolution of 25 km. To evaluate the accuracy of the retrievals, examples of the passive-only, as well as the combined active / passive rain estimates from SeaWinds are presented, and comparisons are made with the standard TRMM rain data products. Results demonstrate that SeaWinds rain measurements are in good agreement with the independent microwave rain observations obtained from TMI. Further, by applying a threshold on the retrieved rain rates, SeaWinds rain estimates can be utilized as a rain flag. In order to evaluate the performance of the SeaWinds flag, comparisons are made with the Impact based Multidimensional Histogram (IMUDH) rain flag developed by JPL. Results emphasize the powerful rain detection capabilities of the SeaWinds retrieval algorithm. Due to its broad swath coverage, SeaWinds affords additional independent sampling of the oceanic rainfall, which may contribute to the future NASA's Precipitation Measurement Mission (PMM) objectives of improving the global sampling of oceanic rain within 3 hour windows. Also, since SeaWinds is the only sensor onboard QuikSCAT, the SeaWinds rain estimates can be used to improve the flagging of rain-contaminated oceanic wind vector retrievals. The passive-only rainfall retrieval algorithm (QRad / SRad) has been implemented by JPL as part of the level 2B (L2B) science data product, and can be obtained from the Physical Oceanography Distributed Data Archive (PO.DAAC).