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.

Polar sea ice measurements are an important contribution to global climate models. Passive and active microwave remote sensing instruments are used to track global trends in polar sea ice growth and retreat from day to day. A scatterometer sea ice extent data set is valuable for comparison with other radiometer data sets and ground based measurements. This scatterometer sea ice record began with the NASA Scatterometer (NSCAT) and continued with the Quick Scatterometer (QuikSCAT) data set. The Ku-band Oceansat-2 scatterometer (OSCAT) is very similar to the Quick Scatterometer, which operated from 1999 to 2009. OSCAT continues the Ku-band scatterometer data record through 2014 with an overlap of eighteen days with QuikSCATs mission in 2009. This thesis discusses a particular climate application of the time series for sea ice extent observation. In this thesis, a QuikSCAT sea ice extent algorithm is modified for OSCAT. Gaps in OSCAT data are accounted for using a reverse time processing approach. The data gaps are filled in to support sea ice extent mapping. The data set is validated with overlapping data from QuikSCAT as well as the sea ice extent data set calculated from Special Sensor Microwave Imager data by the NASA Team algorithm.Data from the Advanced Scatterometer (ASCAT), which operates at C-band, are processed using a Bayesian classification algorithm for a stand-alone C-band sea ice extent product to continue scatterometer sea ice extent observation past 2014. ASCAT azimuth dependence data is developed for use as a parameter in the ASCAT sea ice extent algorithm. Image dilation and erosion techniques are employed to smooth the sea ice edge and correct misclassifications. ASCAT sea ice extent data is validated to overlapping OSCAT data.

In this thesis, the development and methodology of a new, consolidated BYU/NIC Antarctic Iceberg Tracking Database is presented. The new database combines data from the original BYU daily iceberg tracking database derived from scatterometers, and the National Ice Center's weekly Antarctic iceberg tracking database derived from mostly optical and infrared sensors. Using this data, interpolation methods and statistical analyses of iceberg locations are discussed. The intent of this database is to consolidate iceberg location data in order to increase accessibility to users.Active microwave remote sensing instruments are used to track tabular icebergs and provide a daily estimate of their positions and sizes. A consolidated data set of these positions from several different instruments is valuable to ensure accurate positional data. The scatterometer iceberg positional record began with the Seasat-A Satellite Scatterometer (SASS) and is continued with the Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) data sets.A reliable method of automatically estimating Antarctic iceberg contours and sizes from satellite data is desirable to help better understand patterns in iceberg formation and behavior. Starting from scatterometer images, this thesis develops a method of using the relatively constant backscatter values across the surface of an iceberg to derive a contour of its shape. Contours are then used to find an angle of rotation between images taken on successive days. This method produces size estimates that are within 10% of the area given by the National Ice Center (NIC). The size estimates and rotation angles are included in the new consolidated database.

Spaceborne microwave sensors are capable of estimating various properties of many geophysical phenomena, including the age and extent of Arctic sea ice and the relative soil moisture over land. The measurement and classification of such geophysical phenomena are used to refine climate models, localize and predict drought, and better understand the water cycle. Data from the active Ku-band scatterometers, the Quick Scatterometer (QuikSCAT), and the Oceansat-2 Scatterometer (OSCAT), are here used to classify areas of first-year and multiyear Arctic sea ice using a temporally adaptive threshold on reported radar backscatter values. The result is a 15-year data record of daily ice classification images. An additional ice age data record is produced using the C-band Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager Sounder (SSMIS) with an alternate classification methodology based on Bayesian decision theory. The ASCAT/SSMIS classification methodology results in a record which is generally consistent with the QuikSCAT and OSCAT classifications, which conclude in 2014. With multiple ASCAT and SSMIS sensors still operational, the ASCAT/SSMIS ice classifications can continue to be produced into the future. In addition to ice classification, ASCAT is used to estimate the relative surface soil moisture at high-resolution (4.45 — 4.45 km per pixel). The soil moisture estimates are obtained using enhanced resolution image reconstruction techniques and an altered version of the Water Retrieval Package (WARP) algorithm. The high-resolution soil moisture estimates are shown to agree well with the existing lower resolution WARP products while also revealing finer details.

RapidScat 2.5 km ultrahigh resolution (UHR) wind estimation is introduced and validated it in near-coastal regions. In addition, this thesis applies direction interval retrieval techniques and develops a new wind processing method to enhance the performance of RapidScat UHR wind estimation in the nadir region. The new algorithm is validated with L2B wind estimates, Numerical Weather Prediction (NWP) wind products, and buoy measurements. The wind processing improvements produce more spatially consistent UHR winds that compare well with the wind products mentioned above. Hawaii regional climate model (HRCM), QuikSCAT, and ASCAT wind estimates are compared in the lee of the Big Island with the goal of understanding UHR scatterometer wind retrieval capabilities in this area. UHR wind vectors better resolve fine resolution wind speed features compared to L2B, but still do not resolve the expected wind direction features. A comparison of scatterometer measured σ 0 and HRCM and NWP predicted σ 0 suggests that scatterometers can detect a reverse flow in the lee of the island. Differences between scatterometer measured σ 0 and HRCM predicted σ 0 indicate error in the placement of key reverse flow features by the model. Coarse initialization fields and a large fixed size median filter window are also shown to impede UHR wind retrieval in this area.

The ASCAT scatterometer measures the Earth surface microwave radar backscatter in order to estimate the near-surface winds over the oceans. While the spatial resolution of the conventional applications is sufficient for many purposes, other geoscience applications benefit from an improved spatial resolution. Specialized algorithms may be applied to the scatterometer data in order to reconstruct the radar backscatter on a high-resolution grid. Image reconstruction requires the spatial response function (SRF) of each measurement, which is not reported with the measurement data. To address this need, I precisely model the SRF incorporating (1) the antenna beam response, (2) the processing performed onboard ASCAT before telemetering to the ground, and (3) the Doppler shift induced by a satellite orbiting the rotating Earth. I also develop a simple parameterized model of the SRF to reduce computational complexity. The accuracy of both models is validated.Image reconstruction of the ASCAT data is performed using the modeled SRF. I discuss the spatial resolution of the reconstructed ASCAT images and consider the first- and second-order statistics of the reconstructed data. Optimum values for the parameters of the reconstruction algorithms are also considered. The reconstructed radar backscatter data may be used for enhanced-resolution wind retrieval and for geoscience applications. In this dissertation, the reconstructed backscatter data is used to map the surface extent of the 2010 Deepwater Horizon oil spill and in a study to quantify the azimuth angle anisotropy of backscatter in East Antarctica. Near-coastal ocean wind retrieval is also explored in this dissertation. Because near-coastal ocean measurements of backscatter may be “contaminated” from nearby land and introduce errors to wind retrieval, they must be discarded. The modeled SRF is used to quantify the land contamination, enabling enhanced-resolution wind retrieval much closer to the coasts. The near-coastal winds are validated against buoy measurements.

Sea ice is of great interest due to its effect on the global climate, the Earth's ecosystem, and human activities. Microwave remote sensing has proven to be an effective way to measure many of the characteristics of sea ice. In particular, several algorithms map the daily sea ice extent using a variety of instruments. Enhanced resolution images generated from the Scatterometer Image Reconstruction (SIR) algorithm can be used to generate a high resolution ice extent map. Previous algorithms using SIR images were developed for scatterometers which are no longer operational. The Advanced Scatterometer (ASCAT) is a newer scatterometer which has different characteristics from the earlier scatterometers. The previous algorithms do not perform as well when applied to ASCAT. This thesis presents a new algorithm for ASCAT developed to discriminate sea ice from the open ocean and create daily maps of the ice extent. It is developed from previous algorithms used on earlier scatterometers. The algorithm uses an iterative Bayes decision rule to classify pixels as sea ice or ocean. Digital image processing techniques are used to reduce misclassifications. The ice maps from the new algorithm are compared with the NASA Team sea ice concentration maps from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The comparisons include: difference in area, distance between ice edges, number of missed and false detections. The new ice maps are also compared with the Remund-Long algorithm for the QuikSCAT satellite using the same metrics. The ice edge is verified with high resolution Synthetic Aperture Radar (SAR) data. The new ice maps perform similarly to previous ice mapping algorithms for scatterometers.

The Advanced Scatterometer (ASCAT) is a C-band scatterometer designed to be less sensitive to rain contamination than other higher frequency scatterometers. However, the radar backscatter is still affected by rain which increases error during wind estimation. The error can be reduced in rainy conditions by combining a rain backscatter model with the existing wind only (WO) backscatter model to perform simultaneous wind and rain (SWR) estimation. I derive and test several 2.5 km resolution rain backscatter models for ASCAT data which are used with the WO model to estimate the near surface winds. Various rain models optimal for different purposes are discussed. The best rain model for estimating wind speed lowers the root mean square error (RMSE) in the presence of rain by 13.6% when compared to using the WO model alone. The rain model which best predicts rain rates has a RMSE of 7.9 mm/h. A neural network (NN) is designed to discriminate the presence of rain using ASCAT's backscatter measurements. Such a NN enables the SWR algorithm to be used only on rainy samples and thus improves estimation. By removing all samples identified by the NN as rain, the WO algorithm's speed estimate improved by 2.83%.

]The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150-250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New SouthWales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics.