Multi-frequency and multi-polarization Signals of Opportunity Reflectometry (SoOp-R) has the potential to monitor global root-zone soil moisture (RZSM) at a high spatiotemporal resolution using a constellation of small satellites. This paper introduces a mission design tool to assist the high-level design of multi-frequency SoOp-R missions and presents an example of tradespace analysis. Hundreds of constellation design alternatives were enumerated and compared using standard scoring functions that aggregate cost and coverage metrics for the multi-frequency SoOp-R mission. This comprehensive tool will be valuable in the development of future science missions using SoOp-R.

Land-reflected signals from a geosynchronous communication satellite broadcasting in P-band (367.5 MHz) were captured in low Earth orbit using a simple dipole antenna. A delay-Doppler map (DDM) was generated through autocorrelation. Estimates of the specular point delay were obtained from the lag of the second peak in the DDM with a bias of 239.4 m and a standard deviation of 44 m (12 m over a frozen lake) with respect to a predicted orbit model. Relative magnitudes of the first and second DDM peaks fell within the range of values predicted using dielectric models for the frozen ground and lake. Lastly, retrievals of surface reflection coefficient were generated using a range of realistic values for the transmitter link budgetG/T, these also fell within the range of possible values for the antenna gain pattern. Given the lack of calibration and the large uncertainties in the receiver orbit and attitude, this agreement is sufficient to conclude a successful demonstration of the fundamental principle of single-antenna reflectometry in P-band. P-band reflectometry may offer a new approach to remote sensing of sub-canopy and root-zone soil moisture.

Signals of opportunity reflectometry (SoOp-R), the re-utilization of non-cooperative satellite transmissions for communication and navigation, is a promising approach to remote sensing of root-zone soil moisture (RZSM). Satellite transmissions in the frequency ranges of 137–138, 240–270, and 360–380 MHz are of interest due to the increased penetration depth. These can be combined with Global Navigation Satellite System Reflectometry (GNSS-R) in L-band (1575.42 MHz) to estimate the subsurface SM profile. The objective is to define requirements (e.g. frequency and polarization combinations, observation error, and temporal coincidence of multi-source observations) for satellite-based remote sensing of RZSM. Our approach is to use synthetic observations generated from multi-year time series ofin-situSM measurements from seven United States Climate Reference Network (USCRN) sites and dynamic vegetation structure based on a simple scaling method. A multi-frequency/polarimetric retrieval algorithm is developed and applied to these synthetic observations and used to predict retrieval errors for a range of changes in system parameters. We found that the use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. Moreover, the retrieval errors were found to increase linearly with the reflectivity error and inter-frequency time delays. A bivariate model derived from this linear relationship will be useful for developing requirements on reflectivity precision based upon science requirements for SM/VWC retrievals. Although orbits of specific transmitter constellations were used to generate realistic distributions of incidence angle combinations, the method and results could be applied more generally.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space validation of P-band (240–380 MHz) SoOp techniques and a prototype science instrument. These techniques have the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in August 2022. This presentation will review the instrument science plans aimed at achieving the validation objectives defined for the mission. Mission planning and data processing approaches are described.

Root-zone soil moisture (RZSM) is one of the least measured hydrological variables despite its critical role in understanding the global carbon cycle and forecasting agricultural drought and food production. Signals of opportunity (SoOp) has great potential to overcome the limitation of conventional microwave methods to utilize lower frequencies in space-borne remote sensing. Multi-frequency SoOp reflectometry (SoOp-R) offers a promising solution to measure RZSM by solving the inverse problem for obtaining multi-layer soil moisture profiles. This paper summarizes ongoing work to develop and validate multi-frequency SoOp- R to retrieve RZSM, including forward and inverse methods, sensitivity analysis for the optimal frequency combination, and tower-based field experiments.

Signals of Opportunity (SoOp) remote sensing utilizes existing non-cooperative microwave transmitters as sources of illumination. Knowledge of the SoOp source's Effective Isotropic Radiated Power (EIRP) is required for accurate calibration of a SoOp instrument. A SoOp instrument using a digital communication signal also assumes temporal consistency of the signal's ambiguity function. The Wideband Year-round AmbuguiTy-function Tracking Effective Isotropic Radiated Power (WYATT EIRP) instrument uses an array of RHCP and LHCP antennas, mounted on a rotating antenna pedestal. A secondary observation antenna is placed to allow time difference of arrival measurements for validating satellite positions and potential use in orbit determination. WYATT EIRP will provide temporal monitoring of P-Band SoOp sources EIRP and self-ambiguity function. It will also be used to validate existing P-Band background noise temperature sky maps as well as orbit elevation verification of the P-Band transmitter. The system design is outlined in this paper as well as the theoretical derivation of the measurement retrieval. WYATT EIRP will be installed in the Spring of 2022 and will be used to provide calibration data to the upcoming spaceborne SigNals Of Opportunity: P-Band Investigation (SNOOPI) instrument.

Following the launch of multiple global navigation satellite system (GNSS) reflectometry (GNSS-R) missions, the Signals of Opportunity (SoOp) method has proven to be a powerful tool for geophysical parameter retrieval for land applications such as soil moisture. Having demonstrated the feasibility of the SoOp techniques at P- and S-band, the development of SoOp measurements beyond the GNSS frequency regime is highly anticipated. The SoOp Coherent Bistatic (SCoBi) model and simulator, developed in 2017 and open-sourced in 2018, has been made available to provide multifrequency, fully polarimetric SoOp simulations for ground-based applications through the joint use of analytical wave theory and distorted Borne approximation to evaluate land contributions from multilayer dielectric profiles composed of soil moisture, vegetation, and surface roughness effects. This paper describes the advancement of SCoBi from a ground-and airborne-based model to a spaceborne model. This extension allows for fully polarimetric, complex delay-Doppler map (DDM) simulations through evaluation of the coherent superposition of electric fields emerging from a grid of oriented facets. The model generates a grid of facets by determining the geometry of contributing elements from digital elevation models, with each element providing its contribution under a flat-earth assumption. This module will enable the analysis of fully polarimetric scattering from frequencies available across the ultra-high frequency (UHF) regime.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240–380 MHz). P-band is needed to penetrate through dense vegetation and into the root zone. The longer wavelength of P-band also increases the unwrapping interval for phase observations. These observations hold the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017–2027 Decadal Survey for Earth Science and Applications from Space. SNOOPI will provide in-space validation of both the P-band SoOp technique and a science instrument prototype. SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A secondary priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in early 2022. Updates on the development of measurement models and mission planning to support SNOOPI are provided. A ground-based station will be deployed to monitor the noncooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position.

Root zone soil moisture (RZSM) is a key environmental variable needed for drought and flood forecasts as well as fundamental understanding of the water cycle. Signals of opportunity (SoOp) reflectometry offers a new possibility to directly measure RZSM using multiple microwave frequencies allocated for communications and navigation. This paper develops a multi-frequency SoOp RZSM retrieval algorithm and presents simulated results. We adopted the Principle of Maximum Entropy (POME) model to construct soil moisture profiles from in-situ observations at discrete depths. Synthetic observations, representing SoOp reflectivities at four (4) frequencies spanning I, P and L-bands were generated for each profile. A simulated annealing method was then used to generate soil moisture profile retrievals from these simulated reflectivities. An error assessment was conducted for these simulated retrievals.

Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be an in-space technology demonstration of reflectometry using 240-380 MHz communications transmissions. SNOOPI will both demonstrate essential techniques for root-zone soil moisture (RZSM) and snow water equivalent (SWE) remote sensing as well as provide in-space validation of prototype instrument technology. This paper presents results from studies conducted to define key parameters of the SNOOPI mission, including orbital coverage, signal processing, and the estimated power from the non-cooperative sources.

Global Navigation Satellite System Reflectometry (GNSS-R) observations from the Cyclone Global Navigation Satellite System (CYGNSS) mission are expected to improve numerical weather prediction (NWP) models. Level 1 GNSS-R observables, delay-Doppler maps (DDMs), contain information that is lost in producing the Level 2 wind speed retrievals at the specular point. DDMs could, therefore, prove to be a better observable for data assimilation. In this study, assimilation of GNSS-R DDMs into both global and regional NWP models is demonstrated. In the global case, DDM assimilation shows improvements on the European Centre for Medium-Range Weather Forecasts (ECMWF) background for one month of data. In the regional case, DDM assimilation shows its impact on the hurricane structure and intensity. Results using a two-dimensional variation analysis method (VAM) are presented. A plan for an Observation System Experiment (OSE) experiment is proposed.

Signals of opportunity (SoOp) is a promising technique to measure key geophysical variables at high spatiotemporal scales using multiple microwave frequencies outside bands allocated for science. To better understand spaceborne SoOp observations, an end-to-end mission simulator has been developed. It determines observation geometries of multiple transmitters and receivers and generates SoOp observables for land remote sensing by integrating geophysical data and instrument properties. Using the simulator, the coverage and the instrument data of a hypothetical constellation of spaceborne SoOp receivers were evaluated over different orbital planes. This integrative tool facilitates the spaceborne SoOp mission design by exploring and optimizing the tradespace of a variety of design parameters.

The potentiality of Signals of Opportunity (SoOp) over land can be investigated by advanced forward and inverse modeling and simulation tools to provide viable measurements for Earth science data products over land. This research investigates various inversion techniques that can leverage SoOp sources for land-based Earth science measurements by applying them to simulated soil moisture profiles over bare- and vegetated- soils. Forward modeling is accomplished using Mississippi State University’s Signals of Opportunity Coherent Bistatic Scattering Model (SCoBi), a new, open-source electromagnetic scattering model that can determine coherent received signals at a receiving antenna through application of Maxwell’s equations at discrete scattering soil layer boundaries in conjunction with the distorted Born approximation to describe vegetation propagation and scattering. The results of the forward model are used in various inverse methods to investigate the potentiality of using multiple SoOp sources for Soil Moisture Profile (SMP) retrieval. Multiple SMPs are analyzed by SCoBi to determine the sensitivity of soil moisture variation to SoOp transmitter characteristics such as polarization and elevation angle. Simultaneously, SoOp measurements conducted at Purdue University’s Agronomy Center for Research and Education (ACRE) are used to determine the impact that changes in both physical SMPs and vegetation canopies have on the scattered SoOp. The characteristics of the scattering surfaces, vegetation, and SMPs at the ACRE facility are modeled within SCoBi to observe patterns and relationships captured in reflectivity measurements that are caused by vegetation growth periods as well as rain and drought effects manifested by changing SMPs.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band SoOp has the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017-2027 Decadal Survey for Earth Science and Applications from Space. P-band is needed to penetrate through dense vegetation and into the root zone. SNOOPI will provide inspace validation of both the technique of P-band SoOp and a science instrument prototype. This is a necessary risk-reduction step on the path to a science mission, which will verify important assumptions about reflected signal coherence, robustness to the RFI environment, and our ability to capture and process the reflected signal from orbit. SoOp observations will be used to estimate the complex reflection coefficient over various land surface conditions. These will be used to verify models and show that P-band SoOp can meet working requirements for future RZSM and SWE missions. The SNOOPI instrument design builds upon the heritage of a low noise front end (LNFE), developed from an airborne demonstrator, and a digital back end (DBE) evolved from the Cion, TriG and Blackjack GPS receivers. Success with SNOOPI will retire the critical risks associated with a P-band SoOp satellite instrument and exit at TRL-7. Not only would this instrument enable direct measurements of RZSM and SWE which are not presently possible, it's size, weight, power and cost (SWaP-C) would also be orders of magnitude smaller than comparable monostatic radars due to the re-utilization of existing, powerful, anthropogenic signals.

A proof-of-concept experiment has demonstrated that wideband (400 MHz) signals of opportunity (SoOp) transmitted in K- and Ku-bands from geostationary satellites can be used for coastal altimetry. An essential finding from this experiment is that the full broadcast spectrum consisting of multiple digital channels can be processed as a single wideband signal source. An established error model for Global Navigation Satellite System interferometric altimetry was shown to accurately represent the sea surface height (SSH) retrievals when evaluated using the full bandwidth. This experiment was conducted over a 72-h period at Platform Harvest off the Pacific Coast. Colocated tide gauge and LiDAR measurements were used as in situ data. Two anomalies were observed in the experiment: 1) multiple peaks in the cross correlation waveform from one polarization of Ku-band frequency and 2) decrease in signal-to-noise ratio from loss of a data channel. When the instances of multiple peaks were eliminated and the equivalent bandwidth recomputed using only the active channels, SSH error from these cases agreed well with the model prediction. Application of SoOp wideband altimetry will, therefore, require a monitoring capability to identify changes in the transmission spectrum, total power, and waveform shape, for quality control and setting an appropriate observation error covariance. Measurement precision from a satellite receiver is predicted to be between 4 and 6 cm using the error model. SoOp altimetry with these signals may improve coastal measurements and increase the sampling and revisit rate through the use of a constellation of small satellites.

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements of ocean winds present a challenge because the surface wind speed cannot be assumed homogeneous over the large glistening zone observable from orbit. The 25-km resolution requirement of the Cyclone GNSS (CYGNSS) mission limits wind speed retrievals to using only a small fraction of the delay-Doppler map (DDM) near the specular point. This paper presents a new method to invert a larger portion of the DDM and estimate the wind field within a swath defined by the maximum delay. An extended Kalman filter (EKF) is applied to combine a series of DDMs and estimates the wind speed on a uniformly gridded ocean surface exploiting the large overlap between sequential looks. Wind retrievals at the specular point using simulated data generated using wind fields from two hurricanes (Danielle and Earl, 2010) were found to meet the CYGNSS measurement requirements (2 m/s for U20 m/s) and perform better than a typical single-point observable. Wind retrievals were also found to meet these requirements below 20 m/s within a 90-km swath and meet them for hurricane-force winds ($17 \times 11$) DDM, demonstrating benefits of sequential processing within a limited data budget. Delay-Doppler ambiguities introduced noticeable artifacts in circumstances where the wind varies asymmetrically within the observed ($90\times 90$km) surface area but did not appear to affect specular point wind retrievals.

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at -5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate.

Root zone soil moisture (RZSM) is an essential variable in meteorology, hydrology, and agriculture. A penetration depth sufficient to sense RZSM requires frequencies below about 500 MHz (I- and P-band). Active or passive microwave sensing in these bands presents substantial technical challenges due to antenna size, radio frequency interference (RFI) and competition for spectrum. Bistatic radar using Signal of Opportunity (SoOp) (e.g. digital satellite transmitters) offers an alternative approach, through reutilizing powerful signals already occupying bands allocated for communications. Airborne experiments using 240-270 MHz sources were conducted in October 2016, followed by a campaign using 360-380 MHz from a fixed tower location in an agricultural research site during the 2017 growing season. A new campaign that will also include I-band (137 MHz) is presently being installed in advance of the 2018 season. This paper will summarize activities to support the reduction of data from these campaigns and development of soil moisture profile retrievals.

The Cyclone Global Navigation Satellite System (CYGNSS) constellation was launched for the purpose of improving tropical cyclone forecasts using GNSS Refiectometry (GNSS-R). CYGNSS wind speed estimates have been based on only a small window of the Delay-Doppler Maps (DDM) due to the resolution requirement. Direct assimilation of DDM data into a forecast model is an alternative approach, that could take advantage of contribution to the DDM from regions on the ocean away from the specular point. This paper will present a generalized forward model for assimilation of DDMs into a weather model. The forward operator and Jacobian matrix are derived and structured for use in data assimilation systems. The model has also been assessed using CYGNSS Level 1 data from the 2017 Hurricane season.

This paper presents results from a proof-of-concept experiment conducted at Platform Harvest to measure Sea Surface Height (SSH) using Ku- and K-band `Signals of Opportunity' (SoOp) from DirecTV Direct Broadcast Satellite (DBS) system. The retrieved SSH was compared with the SSH measurement from a tide gauge located at the platform; the error in retrieval of Ku-band and K-band was found to be 2.78 cm and 2.58 cm, respectively. This matched the model for error with some differences that can be attributed to the difference in spatial and temporal characteristics of the SoOp and tide gauge measurements. Finally, this paper gives an overview of temporal and spatial sampling possible from a constellation of receivers observing Ku-band SoOp to resolve mesoscale eddies in coastal regions.

Dual-polarized reflected signals from GNSS (Global Navigation Satellite System) L-band and SDARS (Satellite Digital Audio Radio Service) S-band transmissions were collected during the 2016 Maine and 2017 Carolina Offshore airborne experiments conducted by Purdue and NRL (Naval Research Lab). Simultaneous airborne measurements were also made of the L-band brightness temperatures, using NRL's STARRS (Salinity, Temperature, And Roughness Remote Scanner) radiometer system, and the aircraft pitch and roll from a gyro. Flight patterns included multiple crossings over NOAA (National Oceanic and Atmospheric Administration) buoys (2016, 2017) at different headings and CYGNSS (Cyclone Global Navigation Satellite System) under-flights (2017). Preliminary MSS (Mean Square Slope) estimates from S-band and GNSS data were obtained. Agreement between wind speeds retrieved from the aircraft, CYGNSS, and nearby buoys was generally close, but several problems were identified. S-band MSS retrievals showed a strong correlation with flight direction. To test our hypothesis that this was a result of antenna patterns anisotropy, the patterns were both estimated from the reflected signal azimuthal dependence and measured experimentally. These patterns were incorporated into the forward model, with improved wind retrieval results.

Initial results from the first airborne campaign to evaluate P-band reflectometry for soil moisture remote sensing are presented. P-band radiation has a penetration depth of 10-20 cm, compared to around 5 cm for L-band. This offers the possibility of measuring Root-Zone Soil Moisture (RZSM), a capability that does not presently exist in spaceborne remote sensing. Signals of Opportunity Airborne Demonstrator (SoOp-AD) is a brassboard P-band reflectometry demonstration instrument, developed under the NASA Instrument Incubator Program (IIP-13). Soil reflectivity is estimated from the cross-correlation of direct and reflected signals from a geostationary communication satellite. SoOp-AD will demonstrate key technological advancements on the roadmap to a spaceborne instrument, including an FPGA-based correlator array and “smart antenna” null-steering in the post-processing stage. The first airborne tests of SoOp-AD were conducted around the ARS Micronet in Little Washita, OK. Initial results confirm the assumption of coherent scattering, show the water-land transition over Lake Ellsworth, and present reasonable values for reflectivity over the instrumented area.

Low frequency observations are desired for soil moisture and biomass remote sensing. Long wavelengths are needed to penetrate vegetation and Earth's land surface. In addition to the technical challenges of developing Earth observing spaceflight instruments operating at low frequencies, the radio frequency spectrum allocated to remote sensing is limited. Signal-of-opportunity remote sensing offers the chance to use existing signals exploiting their allocated spectrum to make Earth science measurements. We have made observations of the radio frequency environment around 240-270 MHz and will discuss properties of desired and undesired signals.

Coastal altimetry plays a prominent role in measuring the total water-level envelope directly, and is one of the key measurements required by storm surge applications and services. It can also provide important information about the wave field, leading to development of more realistic wave models and therefore improving forecasts of wave setup and overtopping processes. Satellite altimeters have a long history of mapping the variability of the Earth's open ocean. However, this is not the case for coastal areas because of the limitations of technology and difficulties in processing and interpretation of data near coastal surface (due land contamination and rapid variations due to tides and atmospheric effects). There is, therefore, a need for more accurate Sea Surface Height (SSH) near coastal areas. Bistatic altimetry using signals of opportunity (SoOp) (e.g. digital communication signals) may provide additional measurements in coastal areas through oblique incidence angles and high bandwidth (400 MHz). In this study, we investigate the capabilities of SoOp technique for coastal altimetry from spaceborne platforms.

This letter provides a proof-of-concept experiment and validation of an error model for bistatic altimetry using signals of opportunity (SoOps). Coastal sea surface height plays a prominent role in measuring the total water-level envelope directly and is one of the key quantities required by storm surge applications and services. Nadir satellite altimeters have a long history of mapping the variability of the earth's open ocean. However, they exhibit problems operating in coastal areas due to the effects, such as land contamination, rapid variations due to tides, and atmospheric effects. One technique for filling this gap is bistatic altimetry using SoOp (e.g., digital communication signal reflections). In this letter, we investigate capabilities of this technique. Twenty three days of data were collected at platform harvest from a single channel of the Ku-Band direct broadcast satellite. The wind speed observed during the experiment was between 4 and 14 m/s and significant wave height was between 0.7 and 4 m as measured by buoy 46 218 located 8 km away. The standard deviation in the estimation of height was found to be 7.2 cm (the same as predicted from theory). Using a least-squares approach improved the precision reducing the standard deviation to 6.8 cm. It is shown that the error in the estimation of height can be reduced to 3.5 cm by utilizing the full bandwidth (all the channels) of the SoOp. Extrapolating these results, we predict a precision of 5.3 cm from a typical (e.g., Jason) orbit of 1380 km.

The instrument is currently under development with science flights planned aboard a Beechcraft Super King Air B200 aircraft. The flights will include NASA's SLAP L-Band radar and radiometer to provide coincident measurements. The instrument comprises two dual-polarization antennas (one each for sky and Earth views), a four-channel RF receiver with internal calibration network, and a digital receiver to correlated signal pairs. Brass boards of the P-band receivers have been fabricated and have been used to monitor P-Band satellite transmissions to develop spectrum population statistics and survey unwanted RFI. These results have led to requirements for channel processing and RFI mitigation in both the RF and digital portions of the system. The instrument will store complex correlation coefficients for all pairs of elements formed by the two dual-polarization antennas. These coefficients will be averaged in ground processing to reduce noise prior to estimating reflectivity and retrieving soil moisture. A “Smart Antenna” approach will be used in ground processing to steer an antenna pattern null towards the unwanted reflected signal as seen by the sky-view antenna. The background and status of the SoOp-AD instrument will be discussed along with sources of error and mitigation strategies.

Reflected S-band digital communication satellite signals were collected over 12 days of flights around and into developing hurricanes. Cross correlation with the direct signal was used to generate a power vs. delay waveform which was then fit to a forward scattering model to estimate the mean square slope (MSS) of the ocean surface. The existing L-band (GNSS) empirical model function was applied to produce estimates of wind speed. We present a preliminary look at these results, showing the sensitivity of S-band reflectometry measurements to surface winds up to 40-45 m/s. Flight level winds (FLW) were used for the comparison.

Global Navigation Satellite Systems Reflectometry (GNSS-R) methods sense ocean roughness by cross correlating scattered GNSS signals with a locally generated replica of the transmitted signal. The resulting delay-Doppler map (DDM) is related to surface slope statistics through established scattering models. DDM samples are correlated in time and between delay and Doppler coordinates, limiting the number of independent samples available to reduce measurement error. Performance predictions for future GNSS-R missions depend on a model with sufficient fidelity to represent these statistics. A previously developed model for the correlation in time and a new model for the correlation between delays are used to create a GNSS-R signal simulator. A change of variables reduces these models to the numerically efficient form of a single integral and a convolution. Independent normally distributed white noise is passed through a filter bank implementing these models to generate an ensemble of synthetic noisy measurements having realistic correlation in time and between delay bins. Correlation between Doppler bins, however, is not represented by this model. The output of this simulator is compared to 1-D (delay-only) DDMs collected during a 2009 airborne experiment in the North Atlantic, with winds from 5 to 25 m/s. Good agreement is found in the variance, time correlation, and covariance matrix. The probability density functions show reasonable agreement. A bias between the synthetic and observed data was found to result from a bias in the wind/roughness retrieval. Agreement was worse for the low-wind (5.8 m/s) example, perhaps due to a component of specular reflection. One application of this simulator is in generating synthetic DDMs, maintaining accurate representation of statistics following nonlinear processing (e.g., incoherent averaging). The simulator presents a numerically efficient method for generating large statistically significant ensembles of DDMs under identical conditions.

The Cyclone Global Navigation Satellite System (CYGNSS) mission will use Global Navigation Satellite System-Reflectometry to measure ocean surface winds in and near the hurricane inner core, including regions beneath the eye wall, which could not previously be measured from space due to intense precipitation. The CYGNSS constellation will consist of eight small satellite observatories in 500-km circular orbits at an inclination of ~35°. CYGNSS observatories will receive both direct and reflected signals from Global Positioning System satellites. Direct signals will be used both for conventional navigation of the observatory positions and for calibration of the incident signal power. Reflected signals respond to ocean surface roughness, from which wind speed is retrieved. This is observed as changes in a delay-Doppler map (DDM). A generalized observable, which is defined as a linear combination of the DDM samples, is optimized using three different methods: maximum signal-to-noise ratio, minimum variance of the wind speed, and principal component analysis (PCA). Performance of each of these three approaches is compared with each other and with that of the baseline Level-2 retrievals defined for CYGNSS: DDM average, leading edge slope, and the minimum variance combination of both. PCA is found to have the best performance.

This paper compares the retrieval of significant wave height (SWH) from reflected signals of opportunity in the L-band, S-band, and Ku-band. The fundamental observation is the time series of the interferometric complex field (ICF) of the reflected signal. A known relationship between the coherence time of the ICF time series (width of the ICF autocorrelation function) and SWH of the ocean is used for this retrieval. This relationship is applied to data recorded at three frequencies in the S-band, L-band, and Ku-band. The accuracy obtained for S-band and L-band were on the order of 0.4 m, but this model saturated around the SWH value of 3 m. A secondary algorithm used spectral bandwidth to estimate SWH. This model showed linear dependence, with the error also around 0.4 m. The ICF coherence time for Ku-band signals showed little sensitivity to SWH. Furthermore, the effect of fully developed sea, wind, and wave directions on SWH retrieval is analyzed.

Global Positioning System (GPS) radio-occultation (RO) is an atmospheric sounding technique utilizing the received GPS signal through the stratified atmosphere to measure refractivity, which provides information on temperature and humidity. The GPS-RO technique is now operational on several Low Earth Orbiting (LEO) satellites, which cannot provide high temporal and spatial resolution soundings necessary to observe localized transient events, such as tropical storms. An airborne RO (ARO) system has thus been developed for localized GPS-RO campaigns. RO signals in the lower troposphere are adversely affected by rapid phase accelerations and severe signal power fading. These signal dynamics often cause the phase-locked loop in conventional GPS survey receivers to lose lock in the lower troposphere, and the open-loop (OL) tracking in postprocessing is used to overcome this problem. OL tracking also allows robust processing of rising GPS signals, approximately doubling the number of observed occultations. An approach for “backward” OL tracking was developed, in which the correlations are computed sequentially in reverse time so that the signal can be acquired and tracked at high elevations for rising occultations. Ultimately, the signal-to-noise ratio (SNR) limits the depth of tracking in the atmosphere. We have developed a model relating the SNR to the variance in the residual phase of the observed signal produced from OL tracking. In this paper, we demonstrate the applicability of the phase variance model to airborne data. We then apply this model to set a threshold on refractivity retrieval based upon the cumulative unwrapping error bias to determine the altitude limit for reliable signal tracking. We also show consistency between the ARO SNR and collocated COSMIC satellite observations and use these results to evaluate the antenna requirements for an improved ARO system.

Global Navigation Satellite System (GNSS) reflectometry leverages signals of opportunity to remotely sense the Earth's surface for a variety of science investigations. However, ionospheric refraction affects GNSS reflections detected at low Earth orbit (LEO). While multifrequency GNSS enables the elimination of most of the ionospheric error, single-frequency missions are still susceptible to this ranging delay. Motivated by the planned launch of Cyclone GNSS (CYGNSS) in 2016, a single-frequency reflectometry mission, this letter presents a simulation of the relative ionospheric delay that will shift the Delay-Doppler Map (DDM) data product. A mathematical model is presented that defines and characterizes signal propagation delay in the DDM. The model differentiates direct and reflected signals as a sum of path lengths, atmospheric refraction effects, and noise. We simulate representative ionospheric delays from the model associated with the direct and reflected ray paths as a function of satellite elevation angle, latitude, and solar activity. Simulation using the International Reference Ionosphere 2007 shows that differential ionospheric content is inversely proportional to satellite elevation angle and that low latitudes present larger ionospheric impacts on the reflected signals. Finally, high solar activity conditions lift the ionospheric density profile to and possibly above the CYGNSS orbit altitude of 500 km. The ionospheric delay will not generally affect the estimation of wind speed but may affect the CYGNSS signal acquisition and open loop tracking process. Implications of the ionospheric delay in other GNSS reflectometry applications are also discussed.

This paper will present a generalized linear observable which can be optimized under different cost functions for the retrieval of wind speed over ocean surfaces Global Navigation Satellite System-Reflectometry (GNSS-R). The Cyclone Global Navigation Satellite System (CYGNSS) will measure the surface winds in and near the hurricane inner core, including regions beneath the eye wall and intense inner rain bands that could not previously be measured from space. CYGNSS observatories will receive both direct and reflected signals from Global Positioning System (GPS) satellites. The direct signals pinpoint observatory positions, while the reflected signals respond to ocean surface roughness, from which wind speed is retrieved. Based on simulated Delay-Doppler Maps (DDM) over a tropical cyclone, the observable is optimized according to three different methods (maximum signal to noise ratio, minimum variance of the wind speed and principal component analysis). The performances obtained with each of these three approaches are compared to each other and that of the baseline Level 2 retrievals defined for CYGNSS.

Traveling ionospheric disturbances (TIDs) induced by acoustic-gravity waves (AGWs) in the neutral atmosphere are observable in trans-ionospheric radio signals such as Global Navigation Satellite System (GNSS) signals. In this research, we designed a wavelet-based method to enhance the detection and estimation for sounding the presence of atmosphere-wave-induced TIDs using measurements collected from GNSS networks near the epicenter of the March 11, 2011 Tohoku-Oki earthquake and the resulting tsunami. A comparison of GNSS/GPS based observations, global ionosphere-thermosphere model (GITM simulations, JPL's tsunami model simulations and ground motions are analyzed to understand the atmosphere-ionosphere responses in this tsunami/earthquake event. Through use of the wavelet coherence analysis, we are able to identify major wave trains presented in the data collected from networks (such as Japan GEONET and U.S. PBO), with different dominant frequency bands and characteristics. They are mostly due to the infrasound signals and gravity waves originated from the epicenter, Rayleigh waves from the ground surface motions, and tsunami propagation.

This paper presents a semi-empirical relationship between the coherence time of ocean-reflected digital satellite radio signals and significant wave height (SWH). An experiment was conducted on Platform Harvest, a petrochemical exploration platform located in the Pacific Ocean off the coast of Southern California. Raw data were recorded from both directly-received and ocean-reflected signals from the XMradio (S-bandwith center frequency 2.342 GHz), and DirecTV (Ku-Band with center frequency 12.239 GHz) from antennas located at an approximate altitude of 27 meters above the ocean surface. Using a technique first developed for Global Navigation Satellite Systems (GNSS) signals, the coherence time of the Interferometric Complex Field (ICF) is computed by cross-correlating the direct and reflected signals. A relationship is developed between the ICF and SWH. This is done by first developing a model showing a relationship between ICF, correlation time of the sea surface and SWH. A Monte Carlo simulation of the ocean surface is used to show that empirical relationship between correlation time of the sea surface and SWH takes a quadratic form. The coefficients of this empirical model are finally determined from fitting experimental data of S-band signals and a limited amount of Ku-band signals, from the XM radio and DirecTV transmission, respectively. The retrievals of SWH from experimental data through inverting this relationship are then compared to the in-situ recordings from the nearest buoy (8 km away). S-band measurements are found to have a standard deviation of 0.51 meters over a range of SWH from 1 to 5.6 meters.

Global Navigation Satellite System (GNSS) radio occultation (RO) is an atmospheric sounding technique based upon the change in propagation direction of low-elevation GNSS signals through the stratified atmosphere. Atmospheric water vapor profiles can be retrieved from inverting the bending-angle measurements. Open-loop (OL) tracking, in which the received RO signal is cross-correlated with a model signal that does not include refraction effects, is applied to estimate the small excess phase due to atmospheric bending. OL tracking is demonstrated on rising as well as setting satellites observed from an airborne receiver. Setting signals are tracked by processing the sampled signal in reverse, allowing the initialization of tracking after the satellite has reached a higher elevation. A model has been developed to relate the signal to noise ratio (SNR) of the complex correlation to the variance in the residual phase estimate and is used to set a threshold on the minimum SNR for OL tracking. This model is shown to agree well with experimental measurements.

Reflected signals from Global Navigation Satellite Systems (GNSS-R) can be used as a source of illumination for bistatic radar remote sensing of the ocean surface. When cross-correlated with a replica of the transmitted signal, the resulting waveform will have a shape dependent upon the slope distribution of the random rough surface. Substantial data from airborne experiments with GPS exist and have been used to demonstrate the feasibility of GPS reflectometry for ocean remote sensing. The available data from satellite receivers, or any experiments using signals with other modulations, such as Galileo, however, is much scarcer. In order to predict the performance of future spaceborne GNSS-R experiments, and to evaluate the utility of including BOC-modulated signals from Galileo and the modernized GPS in reflectometry measurements, a capability for generating synthetic GNSS-R waveforms has been under development. Individual waveforms are produced by cross-correlating a short block of reflected signal with a model signal over many delay-Doppler bins. As the receiver moves relative to the scattering surface, this cross-correlation is repeated in time, over subsequent blocks of reflected data. Noisy waveforms are thus correlated in time. Samples of the waveform at a given instant in time are also correlated with other samples at same time, but computed at different delays. A realistic simulator, producing synthetic data which accurately represents the statistics of observed signals, must properly account for these correlations. In this presentation, a model for the second effect, the correlation between waveform samples at different delays (or between ”bins”) is modeled and implemented into a simulator.

In this paper, we present a study of relevant correlation properties of signals transmitted from commercial DirecTV satellite signals with the purpose of evaluating their potential use as “signals of opportunity” for bistatic remote sensing. The ambiguity function of the DirecTV satellite signal is computed analytically from published information on the modulation schemes and bandwidth, under the assumption that the data modulation is random. This model is experimentally tested by recording the received signals from the satellites. Also, the signal to noise ratio (SNR) is computed from the published information. The coherence time of the DirecTV signal is computed using the experimental data from two direct signals that is recorded with two different clock sources. An experiment is done to record some reflected data from a river and the coherence time of the Interferometric Complex Field (ICF) from this reflected data is computed for the purposes of measuring the sea state.

Remote sensing of ocean roughness using reflected signals from digital communication satellites is demonstrated in an airborne experiment. Transmitted data are approximated as an infinitely long sequence of random bits, which is experimentally a hypothesis confirmed for the S-band XM radio signal. On July 2, 2010, a signal recorder was flown at an altitude of 3.17 km off the coast of Virginia, collecting ocean-reflected signals from both geostationary satellites identified as “Rhythm” and “Blues,” which were broadcasting the XM radio signal. Direct and reflected signals from the same channel were cross-correlated, producing a waveform that agreed well with a model generated at the 7.5-m/s wind speed reported from the Chesapeake Lighthouse. Adjusting this model to fit the experimental data produced an optimal estimate of 6 m/s. A Monte Carlo approach predicted errors of 0.5% from the simulated reflected XM radio signals and 2%-10% from simulated reflected Global Navigation Satellite System (GNSS-R) signals. This improvement was attributed to the higher ( ~ 30 dB) power in the XM radio signal. The availability of communication satellite transmissions, in all frequency bands used for remote sensing, opens the possibility of using signals of opportunity as low-cost alternatives to radiometry or scatterometry.

This paper present experimental results demonstrating the use of "signals of opportunity" from digital communications satellites (XM radio) as bistatic radar for ocean remote sensing. This builds upon the previous work which demonstrated that the shape of the cross-correlation "waveform" of reflected XM radio signals is sensitive to the roughness of the ocean surface. In these new results, we compare this sensitivity between the waveforms produced from the two XM radio satellites, viewed simultaneously at different azimuths, and show that a small discrepancy exists in the mean square slope (MSS) retrievals obtained from each of them. We then investigate the hypothesis that this discrepancy is the result of neglecting anisotropy in the model for the probability density function (PDF) of surface slopes and that this discrepancy might be useful for sensing the wind direction. In order to do so, a two-stage estimation process was applied to data collected on an airborne experiment that recorded the direct line of sight and reflected XM radio signals. In the first step, an isotropic normal distribution was assumed for the PDF and the mean square slope (MSS) was fit to the measured waveform data from each satellite independently. Since the two satellites are located at different azimuths, a difference between the two MSS estimates were observed. The second step involved using a bidirectional normal PDF with MSS constrained to that obtained from the first step, and a value was assumed for the ratio of upwind and crosswind slopes. The direction of the principal axes was varied to minimize the total residuals for both satellites. The results were compared with Chesapeake Lighthouse recordings of the local wind direction.

Traveling ionospheric disturbances (TIDs) induced by acoustic-gravity waves(AGWs) in the neutral atmosphere are subsequently observable in trans-ionospheric Global Navigation Satellite System (GNSS) measurements. Disruptive events on the Earth's surface, such as earthquakes, tsunamis and large explosions are one source of these disturbances. In this study, we apply a wavelet method to enhance the cross-correlation technique for detecting the presence of TIDs in dual frequency IEC time series collected from GNSS networks. Data were collected after the March 11, 2011 earthquake in Japan and the February 27, 2010 earthquake in Chile. Both earthquakes produced large tsunamis. Through use of the wavelet coherence analysis, we are able to find major a wave train, present in the data collected from these networks, with two dominant frequency bands.

In February-March 2009, an airborne field campaign was conducted using the Passive Active L- and S-band (PALS) microwave sensor and the Ku-band Polarimetric Scatterometer to collect measurements of brightness temperature and near-surface wind speeds. Flights were conducted over a region of expected high-speed winds in the Atlantic Ocean, for the purposes of algorithm development for sea surface salinity (SSS) retrievals. Wind speeds encountered during the March 2, 2009, flight ranged from 5 to 25 m/s. The Global Positioning System (GPS) delay mapping receiver from the National Aeronautics and Space Administration (NASA) Langley Research Center was also flown to collect GPS signals reflected from the ocean surface and generate postcorrelation power-versus-delay measurements. These data were used to estimate ocean surface roughness. These estimates were found to be strongly correlated with PALS-measured brightness temperature. Initial results suggest that reflected GPS measurements made using small low-power instruments can be used to correct the roughness effects in radiometer brightness temperature measurements to retrieve accurate SSS.

This paper presents a study of relevant correlation properties of signal transmitted from commercial communication satellites in order to evaluate their potential use as “signal of opportunity” for bistatic remote sensing. The ambiguity function for the XM radio satellites was computed analytically from published information on the modulation schemes and bandwidth, under the assumption that the data modulation is random. The model was then experimentally tested by recording the received signals from these satellites. Next, a cross-correlation waveform for digital signal reflected from random rough surface was simulated. Scattering model that were originally developed for Global Navigation Satellite System (GNSS-R) signals was applied to the modified simulator to incorporate the derived ambiguity function. The simulator was then used to generate synthetic waveform with a realistic signal to noise ratio (SNR). Retrieval algorithms for ocean surface roughness and reflectivity that were derived originally for GNSS-R, were applied to these simulated signals. Non-linear least square methods were applied to invert a scattering model and estimate the slope variances of the probability density function (PDF), which best fits the measurements of the reflected XM signal waveform. The SNR for the experimental data was found to be within 0.5dB of the theoretically calculated SNR.

Acoustic-Gravity Waves (AGWs) in the neutral atmosphere can induce disturbances in the ionosphere that are subsequently observable in trans-ionospheric Global Navigation Satellite System (GNSS) measurements. Disruptive events on the Earths surface, such as earthquakes, tsunamis and large explosions are one source of these disturbances. In this presentation, we applied wavelet decomposition to detect the presence of ionospheric disturbances in dual frequency GNSS time series collected from the GEONET (Japan) and SCIGN (Southern California) networks during the Peru tsunami on June 23 2001 and the Chile Tsunami on February 27 2010. Through use of the wavelet decomposition, we are able to find two wave trains, present in the data collected from both networks, with frequencies of approximately 0.0005-0.0011 Hz and 0.0016-0.0167 Hz. The speed and direction of arrival of the disturbances are found through cross-correlating every pair of stations in several sub-areas of each network. These were found to be compatible with the arrival direction and speed of a disturbance created from the Tsunami, with propagation speeds of 101-265 m/s and 559-1195 m/s, respectively.

The GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS) is a new remote sensing system in development for the HIAPER Gulfstream V (GV) aircraft. This system is designed to use occulted and reflected Global Navigation Satellite System (GNSS) signals to retrieve tropospheric water vapor, ocean surface roughness and soil moisture during long duration, high altitude, flights. This paper summarizes the design and ground testing of GISMOS as well as the current flight test schedule.

A "waveform" is generated by cross-correlating local copies of a global positioning system (GPS) signal with an ocean-reflected GPS signal, over a range of carrier frequencies and code delays. The shape of this waveform can be inverted to obtain estimates of the ocean surface roughness. To assess the accuracy of these retrievals, a stochastic model for the waveform time series measurements was developed in a previous publication. In this letter, this model is validated by comparing the predicted autocorrelation function of the waveform against the autocorrelation computed from experimental waveforms collected from an airborne receiver. A 1-ms coherent integration time was used at first. Then, blocks of these measurements were concatenated to produce equivalent integration times of up to 5 ms to compare the dependence of model predictions on integration time. Correlation time was estimated by fitting a model Gaussian function to the magnitude or the real part of the autocorrelation function. The magnitude and phase of the complex autocorrelation function from the model were also studied to show the location of the first null, and better explain cases in which the Gaussian function did not fit well. The autocorrelation is found to be weakly dependent upon the surface roughness, over a range of moderate wind speeds.

This paper is concerned with the properties of bistatic microwave scattering from a randomly rough surface, and specifically its application to the study of global positioning system (GPS) satellite signals reflected from the ocean. We present a discussion of some recent refinements of Kirchhoff-type models based on second-order iterations of the surface-current integral equation, and the relationship between these models and their high-frequency (geometric optics) limit. In particular, we show that use of these refinements can extend the domain of applicability of the standard geometrical optics (GO) model. It is found that GO can be nearly as accurate as a Kirchhoff-based model provided that the wavenumber cutoff, at which the surface wave spectrum must be filtered in computing the required slope moments, depends on the roughness of the ocean surface (i.e., wind speed) as well as the incident angle and frequency of the radiation. We use a GO model refined in this way to analyze GPS surface reflection data collected from an aircraft equipped with two down-looking antennas for receiving both left- and right-hand circularly polarized reflections. Concurrent measurements of the local wind and wave conditions were collected from a nearby research vessel. Measured waveforms and mean Doppler widths at both polarizations are compared with predictions from our refined GO model, and discussion is given concerning the sensitivity of the reflected radiation to various geophysical parameters and the utility of GPS reflections for remote sensing applications.

The ocean-scattered Global Positioning System (GPS) signal can be used, in a bistatic radar configuration, for estimating the ocean surface roughness. This requires fitting a scattering model to the distribution of reflected power in delay and Doppler. The delay-Doppler map, or waveform, is generated through the cross-correlation of a local copy of the pseudorandom noise (PRN) code assigned to each GPS satellite with the reflected signal over increments in Doppler frequency equal to the pre-detection bandwidth. The correlation time of the reflected signal voltage sets an upper limit on the pre-detection integration time. Correlation time was estimated from experimental data through fitting a model Gaussian function to the magnitude of the complex autocorrelation of the time series of the waveform. Range bins were taken 1.1 chips prior to the averaged waveform peak in the leading edge and 2.5 chips after the averaged waveform peak in the trailing edge. Doppler bins were set at -500, 0, and +500 Hz relative to the Doppler frequency of the corresponding direct GPS signal. The direct GPS signal was tracked using a frequency locked loop (FLL). Results showed that maximum correlation time occurs approximately 0.72 code chips prior to the averaged waveform peak. The correlation time was found to vary with the elevation angle of the satellite, with lower elevation satellites showing longer correlation time. Some experimental results were compared with model predictions. A good comparison was usually found for delay bins near the specular point and both the model and experiment showed a decrease in the correlation time for longer delays. In the higher range bins the model tended to predict longer correlation times than those observed in the experimental data

Information about the roughness of the ocean surface and related geophysical parameters, such as wind speed, is present in the shape of the code-correlation waveform of forward-scattered Global Positioning System (GPS) signals. A model is developed for the statistics of this waveform to be used in designing retrieval algorithms and predicting their accuracy in the estimation of geophysical parameters. One potential application of this model is to assess the feasibility of bistatic GPS measurements from satellite orbits. Time and frequency domain models for the complex "voltage" correlation waveform are developed and compared against experimental results. The voltage model can be applied to determine the upper limit for predetection integration time. The resulting temporal and spatial correlation function has a form similar to the van Cittert-Zernike theorem in that it can be expressed in terms of two-dimensional Fourier transform. The fast Fourier transform is, thus, used for efficient computation. Waveforms were generated from measurements of reflected GPS signals recorded in 1999 from an airborne receiver at an altitude of 3200 m during a flight near Puerto Rico. Complex voltage correlations were produced using the coarse-acquisition code with a 1-ms integration time over a range of code delay "bins". The Doppler compensation frequency was set equal to the Doppler frequency obtained by tracking the direct line-of-sight GPS signal. The resulting spectra and derived correlation times of the voltage signal time series in each delay bin were compared with the predictions of the model. The model agreed well with the experimental data, near the specular point, showing correlation times between 4-6 ms.

The probability density function (PDF) of the ocean surface slope can be estimated from the code-correlation waveform of reflected GPS signals. Anisotropy in this PDF is found to correspond to the local near surface wind direction, suggesting the ability to resolve this direction using scattered signals from two or more GPS satellites. A two-stage estimation process was applied to sets of waveform data collected from an airborne delay-mapping GPS receiver. First, an isotropic normal distribution was assumed for the PDF. The mean square slope (MSS) was fit to the measured waveform data for each satellite independently. Differences between the MSS estimates from two satellites at different azimuths were observed. In the second step, a bidirectional normal PDF was used with MSS constrained to that obtained in the first step, and an assumed value was given for the ratio of upwind to crosswind slopes. The direction of the principal axes was then varied to minimize the total residuals for both satellites. The results were compared with buoy recordings of the local wind direction.

Information about the roughness of the ocean, and related geophysical parameters, including wind speed, is present in the shape of the code-correlation waveform of forward scattered GPS signals. A model is developed for the statistics of this waveform, to be used in the prediction of retrieval accuracy and the design of optimal estimators for the retrieval of meaningful geophysical data. Time and frequency domain models for complex "voltage" correlation waveform are developed and compared against experimental results. The analytical form of a model for the autocorrelation of the "power" (real) waveform is also presented. These two models can be applied to determine the upper limit for predetection integration time, and the time between independent waveform samples, respectively. C/A code waveforms were produced, with a 1 msec integration time, from measurements of reflected GPS signals recorded in 1999 near Puerto Rico. Time and frequency domain measurements of the correlation time of these waveforms were estimated through fitting a Gaussian function to the autocovariance and through measuring the width of the power spectral density, respectively. Typical experimental values obtained for voltage model, at aircraft altitude and speed, were 4.2 msec at the peak of the waveform, decaying to 1 msec at a code lag of 3.5 chips. For power model, they were 3.5 msec at the peak of the waveform, decaying to 1 msec at a code lag of 1.8 chips.

Empirical model functions are derived for the retrieval of surface winds from the post-correlation waveform of scattered GPS signals. A geometric optics model is applied to generate model waveforms from the the surface slope probability density function (PDF). Batches of experimentally recorded waveforms are then processed to estimate parameters defining the PDF (ie., up-wind and cross-wind slope variances and the direction of the principal axes). A nonlinear least squares method is used to perform this estimation. Experimentally measured PDFs from this method are then compared with independent measurements of surface wind conditions obtained from the TOPEX altimeter. Linear and a logarithmic forms are assumed for the functional dependence of apparent slope moments on wind speed. The coefficients of each of these functions are determined by weighted linear least squares. The limitations and uncertainty in these models are discussed.

Instrumentation and retrieval algorithms are described which use the forward scattered range-coded signals from the global positioning system (GPS) radio navigation system for the measurement of sea surface roughness. This roughness has long been known to be dependent upon the surface wind speed. Experiments were conducted from aircraft along the TOPEX ground track and over experimental surface truth buoys. These flights used a receiver capable of recording the cross-correlation power in the reflected signal. The shape of this power distribution was then compared against analytical models, which employ a geometric optics approach. Two techniques for matching these functions were studied. The first recognized the most significant information content in the reflected signal is contained in the trailing edge slope of the waveform. The second attempted to match the complete shape of the waveform by approximating it as a series expansion and obtaining the nonlinear least squares estimate. Discussion is also presented on anomalies in the receiver operation and their identification and correction.