Global Navigation Satellite System Reflectometry (GNSS-R) is a relatively new type of radar developed for remote sensing of the Earth surface. It uses GNSS navigation signals such as those transmitted by the Global Positioning System (GPS) constellation of satellites as the radar transmitter. The radar receiver measures alterations in the navigation signal caused by scattering from the Earth surface. The nature of those alterations contains information about the surface, which can be retrieved. The general theory of GNSS-R remote sensing and its historical development is presented, followed by a detailed assessment of the performance and capabilities of the first dedicated spaceborne GNSS-R science mission, NASA’s Cyclone Global Navigation Satellite System (CYGNSS). Numerous follow-on GNSS-R missions are also noted that have either already been launched or are scheduled to be soon. A wide range of science data products and scientific applications of the CYGNSS data are examined. Developments that are currently underway in GNSS-R technology are summarized, and their potential impact on the capabilities of future GNSS-R missions is discussed.

As part of the continued refinement of the Cyclone Global Navigation Satellite System (CYGNSS) mission’s Level-1 calibration practices, this work reports on recent progress to compensate for thermal artifacts associated with reported effective isotropic radiated power (EIRP) tracked by the receivers’ zenith channels in the form of oscillations with a dominant 40–60-day principal component. The use of the uncorrected EIRPs in the calibration of normalized bistatic radar cross section (NBRCS) estimates is shown to lead to commensurate nongeophysical oscillations. A simple scheme for using in-orbit measurements to rederive prelaunch zenith channel LNA gain tables as a function of the relevant temperature is overviewed. These are subsequently used as part of the EIRP calibration process and to implicitly correct reported NBRCS estimates. The corrected NBRCS estimates are markedly more consistent. It is estimated that the revisions introduced as part of this work, and adopted for the mission’s latest v3.2 Level-1 data release, reduce the errors in the temperature dependence of the transmitter power estimates by an average of 88% while also reducing NBRCS relative spectral densities over the relevant frequency components by an average of 36%. The remaining energy is attributed to a combination of smaller residual, unaccounted for, calibration imperfections but more importantly to real geophysical phenomena causing oscillations over similar time scales whose minimization is not of interest.

A method for detecting and geolocating terrestrial GPS L1 band interference sources using Cyclone Global Navigation Satellite System (CYGNSS) mission Level-1 noise floor estimates is described. Evidence of interference sources and their signal properties is first presented using CYGNSS’s special raw I/F acquisition mode. The associated Level-1 data is shown to require additional processing steps to provide information on the RFI contained. A “EIRP correlation” approach combined with a spatial anomaly clustering method is developed and applied to CYGNSS Level-1 noise floor data extending from August 1st, 2019 to April 1st, 2025 to produce a daily radio frequency interference (RFI) product on a ≈10 km grid and to geolocate the sources contained. The results reveal more than 25 unique GPS L1-band interferers over the analysis period, with the majority located within the MENA (Middle East and North Africa) region as well as Myanmar. Use of the method for detecting and geolocating interference sources beyond CYGNSS’s nominal northernmost 38°N latitude coverage is also demonstrated. The results highlight how GNSS-R observations can be instrumental in monitoring and understanding the GNSS RFI environment.

We report a study of radio frequency interference (RFI) in the GPS L1 band (1575.42 ± 2 MHz) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. An examination of the “noise” levels reported by both Spire, Inc. and CYGNSS (Cyclone Global Navigation Satellite System) standard Level 1 products is first presented that shows clear evidence of RFI contributions. Data from 498 acquisitions in CYGNSS’s special raw I/F (Intermediate Frequency) mode is then used to examine RFI source properties in greater detail. Both kurtosis and cross-frequency algorithms are applied with the raw I/F data to detect the presence of RFI. The results suggest that up to 25% of the acquisitions considered contain RFI, with both “narrow” and “wideband” sources of varying amplitudes observed. The results provide insights into RFI source properties in the GPS L1 band as well as methods by which the RFI can be detected.

The Arctic-boreal zone is warming due to climate change. Current spaceborne remote sensing techniques and retrieval methodologies need to be complemented to improve systematic monitoring of the cryosphere. To that end, this article presents a new investigation of the use of the Global Navigation Satellite System Reflectometry (GNSS-R) remote sensing technique by a SmallSat constellation. A new freeze/thaw (F/T) seasonal multi-thresholding algorithm is developed using high-inclination orbit near-Nadir Spire Global GNSS-R data acquired through the National Aeronautics and Space Administration Commercial Smallsat Data Acquisition program. Five different soil surface reflectivity Γ models are proposed to account for the impact of vegetation cover and small-scale surface roughness on Earth-reflected GNSS signals. The sensitivity of the Γ models to F/T surface state transitions is evaluated, and the optimum model is selected to construct a seasonal scale factor. Then, a multi-thresholding matrix is obtained for F/T classification using a specific threshold for every surface grid cell. Results for the annual frozen soil duration (days yr-1) are compared with that by the Soil Moisture Active Passive mission. Additionally freezing and thawing periods are analyzed to determine when the moisture exchange with the atmosphere is locked, which is an important climatic factor. A novel metric is introduced to characterize the freeze intensity moving beyond classical F/T binary classifications. Results are evaluated using air and soil temperature, snow depth and temperature, and soil moisture content provided by the European Centre for Medium-Range Weather Forecasts ERA5-Land reanalysis product.

At present, it is speculated that the interannual variability of global CH4 is dominated by wetlands. More accurate CH4 emission models require improved capabilities for surface water extend monitoring by remote sensing sensors under dense vegetation. A new NASA Cyclone Global Navigation Satellite System (CYGNSS) surface water map data product has been produced, which can be used to further understand the water cycle in tropical latitudes. This new data product enables small inland water bodies detection under dense biomass up to ~ 400 tons/ha. Our product improves multi-spectral VisIR imagers aboard satellites such as e.g. Landsat & Sentinel-2, and C-band Synthetic Aperture Radar (SAR) images by e.g. Sentinel-1.The main objective of this work is the application of an improved version of the new CYGNSS retrieval algorithm to all the available Rongowai Global Navigation Satellite Systems Reflectometry (GNSS-R) airborne mission data to investigate the benefits of polarimetric measurements on inland water detection under dense biomass. The expected relevance of this study is high because tens of polarimetric GNSS-R SmallSats will be launched in the near future, including the European Space Agency (ESA) HydroGNSS mission and the Muon Space commercial constellation. On the other hand, SAR & microwave radiometry missions are limited by the required power consumption, instrument size and cost. In summary, we are developing a novel GNSS-R retrieval algorithm, useful for the remote sensing community, and with interest to further understand highly dynamic water surface processes.

Spatial and temporal patterns of landscape Freeze/Thaw (F/T) state transitions within the cryosphere are highly variable with impacts to climate, hydrological, ecological, and biogeochemical processes. F/T state has a strong impact on the seasonal amplitude and partitioning of surface energy exchange, while ecosystem responses to seasonal thaw are rapid, with evapotranspiration, soil respiration, and plant photosynthetic activity accelerating with warmer temperatures and the availability of liquid water. The annual thaw period also influences the vegetation growing season, while variability in F/T timing rules vegetation net primary production and Net Ecosystem CO2 Exchange (NEE) with the atmosphere. More than one third of the Earth’s land surface is covered by seasonal or permanent soil frost. Many agricultural, engineering, and environmental issues and applications are affected by F/T state. A frozen land surface tends to reduce or even to impede water infiltration, which may promote flooding, surface runoff, and soil erosion.In this work, Spire data through the NASA Commercial Smallsat Data Acquisition (CSDA) Program are used to develop novel Global Navigation Satellite Systems Reflectometry (GNSS-R) methods to determine the F/T state over the Arctic-Boreal region. The generated theoretical and experimental capabilities will be applied to analyze the spatial patterns and temporal dynamics with an improved spatio-temporal sampling as compared to Synthetic Aperture Radar (SAR) missions.

There are several established algorithms for processing Global Navigation Satellite System Reflected (GNSS-R) signals. The goal of these techniques is to generate delay Doppler maps (DDMs) of the magnitude distribution of the reflected/scattered signal power over the surface. The resulting DDMs are then used to estimate geophysical surface parameters such as ocean surface wind speed or near surface soil moisture. Each of these techniques have their unique application specific advantages and disadvantages. This presentation will present three of the most common GNSS-R DDM processing algorithms and compare them from the perspective of signal quality and calculation efficiency, and make recommendations for their suitability for various remote sensing applications and instrument design considerations.

Earth’s inland water detection under thick biomass remains unresolved. Optical sensors are limited by night, clouds and upwelling biomass. The SWAMPS product (active + passive microwave sensors) has a coarse spatial resolution ~ 25 km. C-band Sentinel-1 radar is limited by the upwelling biomass and has a high revisit period. In this work, a new retrieval algorithm based on L1 Cyclone Global Navigation Satellite System (CYGNSS) mission data is developed and results show the capability of water detection under thick biomass ~ 450 ton/ha. The use of the recent IEEE Global Navigation Satellite Systems Reflectometry (GNSS-R) Standard would enable the application of this algorithm by all the present and future GNSS-R missions, and thus generating an unbeatable spatio-temporal sampling by this "virtual" constellation of SmallSats. The capability to accurately resolve surface water extend has an important impact on estimating global methane emissions to the atmosphere.

Earth’s inland water monitoring is probably the main promising application of Global Navigation Satellite Systems Reflectometry (GNSS-R) techniques. The ultimate spatial resolution under the coherent scattering regime deserves further investigation. The new Cyclone Global Navigation Satellite System (CYGNSS) raw Intermediate Frequency (IF) data product with a temporal resolution down to 2 ms could help to further understand this. In the framework of climate change the "water" is the "new gold". In-space water monitoring could help final users to make decisions with impact in several topics including geopolitics. The use of GNSS-R techniques by future constellations of SmallSats could overcome several limitations of more classical remote sensing techniques. In this work, a novel real-time inland water detector by a future GNSS-R receiver is presented. This detector, the so-called fast entropy Efast, shows the capability to detect small water bodies under thick biomass ~ 450 ton/ha in the Congo basin.

An assessment of the ocean surface Level-1 normalized bistatic radar cross Section (BRCS) products provided by the two batch 1 “LEMUR-2” Global Navigation Satellite Systems Reflectometry (GNSS-R) receivers of Spire, Inc. is reported. The analysis uses datasets extending from DOY 345, 2020 to DOY 329, 2021. Initial assessments indicate a highly consistent overall interchannel response with mean normalized bistatic radar cross Section (NBRCS) differences estimated to be at the 1.00% level over a 7–12-m/s European Centre for Medium-Range Weather Forecasts (ECMWF) reference wind speed range. Efforts to validate the observation systems’ aggregate response relative to$\approx 3$million colocated Cyclone Global Navigation Satellite System (CYGNSS) measurements suggest a highly complementary behavior with an overall NBRCS correlation of 79.03% highlighting the potential utility of “LEMUR-2” measurements for ocean surface wind sensing and related applications. Nonphysical NBRCS dependencies on various Level-1 calibration variables, also observed with GNSS-R previous systems, are nonetheless noted and are explored in detail.

This work reports a case study of the use of synthetic cyclone models for the development, assessment and validation of global navigation satellite system reflectometry (GNSS-R) wind speed remote sensing algorithms using a cyclone global navigation satellite system (CYGNSS) data record extending from 1 August 2018 to 31 December 2022. Synthetic cyclone models are shown to be useful in assessing the high wind speed sensitivity of CYGNSS’s v1.0, v2.1, v3.0, v3.1, and future v3.2 normalized bistatic radar cross Section (NBRCS) products due to the extended matchup dataset of high wind speed information that is obtained. The models are also shown useful in investigating the impacts of specific error corrections terms and in the development of level-2 geophysical model functions (GMFs) for the retrieval of ocean surface winds.

A new Cyclone Global Navigation Satellite System (CYGNSS) data product is described which is generated from the raw Intermediate Frequency (IF) data. The product includes several established signal coherence detectors, including the power-ratioPratio, complex zero-Doppler delay waveform and full entropyEfull, and a novel fast entropy detectorEfast. Both entropy detectors are provided with two temporal resolutions: 2 ms and 50 ms. Coherence performance is characterized using the phase derivative of the reflected signal at the peak of the delay waveform φpeak. Threshold values of the full entropy detector are determined which classify scattering into three regimes: incoherent, partially coherent, and coherent. Several scattered signal strength products are included: Signal-to-Noise RatioSNR, reflected powerPg, reflectivity Γ, and Normalized Bistatic Radar Cross-Section NBRCS. Each of these products is derived using a coherent integration time ofTC= 1 ms and incoherent integration times ofNinc= 1000, 500, 250, 100, 50, and 2 ms. Signal strength time series at the shorter (2 and 50 ms) times provides excellent detection of land-water transitions in heterogeneous scenes. Delay Doppler Maps (DDMs) are also generated with high delay (Δτ = 1/16 chip) and Doppler (Δf = 50 Hz) resolution. The behavior of each signal strength product as a coherence detector is examined using the full entropy method as a reference. Performance is characterized using Receiver Operating Characteristic (ROC) curves. The fast entropy method, which has much lower computational cost, is similarly characterized. This suite of coherence detection methods can be used to detect the presence of small inland water bodies.

This paper will present the first radio occultation (RO) electron density profiles of Earth’s atmosphere generated from a Global Positioning System (GPS) receiver in Geostationary orbit. The GPS receivers on the GOES-16 (R) and GOES-17 (S) satellites track GPS signals propagated through the Earth’s atmosphere and can be used to estimate electron density profiles. Radio occultation profiles from geosynchronous orbit holds the potential to generate unique temporal and spatial atmospheric measurements complementary to those from ground and low Earth orbit space based receivers, including limb observations of the upper atmosphere at altitudes above traditional low Earth orbiting RO satellites. This paper will present details of the GOES satellite GPS receivers and the limitations and challenges in generating RO profiles with its current hardware and software configuration. Following, the temporal and geo-spatial coverage for each of the GOES satellites will be presented, quantifying the frequency and number of GPS signals tracked down to sufficiently low enough altitudes to provide useful atmospheric information. Next, the data processing required to generate excess phase and electron density profiles will be described and demonstrated using two examples of GOES profiles. Subsequently, these two example GOES RO profiles will be compared to and calibrated with an ionospheric model, compared with co-located profiles from the low Earth orbit COSMIC-2 constellation and a ground based ionosonde.

A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher than CYGNSS Science Data Record Version 3.1 by an average of 5 dB and also smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

This article presents a detailed examination of fluctuating input noise power levels on the analog-to-digital convertor (ADC) sampling hardware of the NASA Cyclone Global Navigation Satellite System (CYGNSS) instruments and the associated impacts on the level 1 normalized bistatic radar cross section (NBRCS) estimation performance. The impact of external noise variations on both the CYGNSS science and navigation channels is quantified with respect to how the fluctuating received power impacts the low-level ADC sampling distribution and subsequent NBRCS estimation. This work demonstrates that there are clear quantifiable geospatially dependent noise variations linked to Global Navigation Satellite System (GNSS) space-based augmentation systems [notably Japanese Quasi-Zenith Satellite System (QZSS) and U.S. Wide Area Augmentation System (WAAS)] and that these additional noise sources significantly alter the digital sampling distribution of the CYGNSS instruments, actively degrading the level 1 NBRCS estimation if not corrected. A derivation of the theoretical correction for both the science and navigation channel ADC sampling variations is presented which is later tuned based on empirical performance metrics in an effort to minimize the induced calibration errors. The impacts of the enhancements outlined in this work on CYGNSS level 1 calibration are evaluated using one year of observations before and after the digital sampling corrections, using model European Centre for Medium-Range Weather Forecasts (ECMWF) wind and Wavewatch III mean square slope (MSS) surface validation datasets.

Global Navigation Satellite System (GNSS) Reflectometry uses reflected GNSS signals for Earth remote sensing applications. Absolute calibration of a Delay Doppler Map (DDM) requires an accurate estimate of the effective isotropic radiated power (EIRP) of the GNSS transmitter, e.g., Global Positioning System (GPS). However, variable transmit power by numerous Block II Follow-on (IIF) and II Replenishment-Modernized (IIR-M) GPS space vehicles has been observed due to their flex power mode. Nonuniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to address GPS EIRP variability. It uses measurements by the direct received GPS signal to estimate GPS EIRP in the specular reflected direction and then incorporates it into the calibration of normalized bistatic radar cross section (NBRCS). Error analyses using Monte Carlo simulations and a root sum of squares (RSS) approach show that the resulting error in NBRCS is about 0.32 dB. Dynamic EIRP calibration instantaneously detects and corrects for power fluctuations in the GPS transmitters and significantly reduces errors due to GPS antenna gain azimuthal asymmetry. It allows observations with the most variable Block IIF transmitters (approximately 37% of the GPS constellation) to be included in the standard data products and further improves the calibration quality of NBRCS and geophysical data products.

A comparison of three different electromagnetic scattering models for delay-Doppler maps (DDMs) of global navigation satellite system reflectometry (GNSS-R) from land is performed along a Cyclone Global Navigation Satellite System (CYGNSS) track over a validation site in the San Luis Valley, Colorado, USA. The peak reflectivity profiles of all three models and of the corresponding CYGNSS data are found to be in general agreement and are strongly influenced by topography. An intercomparison of DDM structure for one acquisition is also included. Efforts to refine the model results using a high resolution lidar survey are ongoing.

A new CYclone Global Navigation Satellite System (CYGNSS) data product is generated using all the available raw Intermediate Frequency (IF) tracks. In so doing, calibrated Delay Doppler Maps (DDMs) are produced using the CYGNSS raw IF processor, and the CYGNSS End-to-End Simulator (E2ES) to generate Look-Up- Tables (LUTs) to bin-by-bin correct the whole DDMs. Such calibrated DDMs are delivered with shorter incoherent integration time$(\boldsymbol{N}_{\mathbf{inc}})$, finer delay and Doppler resolution, and a wider range of delay and Doppler values as compared to the L1b CYGNSS data product, currently available in the Physical Oceanography Distributed Active Archive Center (PODAAC). This work aims to foster the use of this new data product, so as to improve the performance of CYGNSS for a wide range of scientific applications over land and ocean.

The v3 CYGNSS Level 1 calibration algorithm uses the direct signal received by the navigation channel to estimate the GPS EIRP for calibration of the NBRCS. Three sets of antenna patterns play an important role in this calibration algorithm, including the GPS transmit antenna and the CYGNSS zenith and nadir receive antennas. In this paper, we examine how the three antenna patterns are characterized on-orbit and how they are used by the CYGNSS Level 1b calibration algorithm. The impact of antenna pattern knowledge on calibration uncertainty is evaluated and discussed.

This work demonstrates the creation of dynamic inland water body masks at spatial resolutions ranging from 1 to 3 km through the use of a recently developed coherence detector for the delay-Doppler maps produced by the cyclone global navigation satellite system (CYGNSS) constellation. The use of the coherence of the observed measurements reduces many of the uncertainties associated with previous signal-to-noise ratio-based water body detection approaches for CYGNSS. Using data from January 2018 to February 2020 and producing maps representing time intervals ranging from 3 months to 2 years, the water body masks created are found to be associated with a probability of detection that exceeds 80% as compared to the Pekel water mask developed from Landsat observations. The analysis presented in this work highlights the potential of using spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) systems for dynamic inland water body mapping.

An algorithm for detecting coherence in Cyclone Global Navigation Satellite System (CYGNSS) mission delay-Doppler maps (DDMs) is presented. Because CYGNSS DDMs report only the observed power without phase information, the algorithm uses estimates of power “spread” within the DDM to flag coherency. Since the estimate used is a ratio of the powers in differing portions of the DDM, it is less sensitive to absolute power calibration and to the GPS C/A code type observed, and is applied to CYGNSS Level-1 uncalibrated DDMs. The basic detector formulation is described along with modifications to improve performance in lower signal-to-noise ratio (SNR) situations. The required detection thresholds are determined using matchups with CYGNSS “Raw I/F” mode measurements for which the DDM phase can be computed and used to identify coherence more precisely. Application of the final detector over a large CYGNSS data set suggests that approximately 8.9% of all inland returns are coherent. Inland regions persistently identified as coherent were found largely to be associated with the presence of water bodies. A smaller set of desert locations apparently having very low surface roughness were also found to be associated with persistent coherence. The detector was also applied to a set of ocean measurements, with the results showing that persistent coherence is limited to areas with sheltered waters. Ocean tests avoiding such regions indicate that the detector's false-alarm rate is approximately 0.0012% for the detection threshold used.

The science payload on each spacecraft in the CYGNSS constellation is a GNSS-R receiver and antennas designed to receive GPS L1 signals scattered from the Earth surface. The constellation was launched on 15 Dec 2016 and the GNSS-R instruments continue to operate successfully. A next generation version of the receivers and antennas is in development which adds significant measurement capabilities that are expected to enhance the resolution, precision and coverage of current CYGNSS science data products as well as enable some new ones.

The effective isotropic radiated power (EIRP) of the Global Positioning System (GPS) determines the power incident on the Earth surface. It is critical to the CYGNSS mission's Level 1 calibration of normalized bistatic radar cross section (NBRCS). This paper reports a dynamic EIRP calibration algorithm that uses the CYGNSS direct signal to correct the GPS EIRP in the direction to the specular reflection point. This approach can instantaneously detect any transmit power fluctuation in all GPS transmitters and any change of the receiver system gain, and then automatically applied to correct the science measurement. It also helps mitigate the error caused by the azimuthal asymmetry of the GPS antenna gain pattern without knowing the exact yaw attitude of the GPS satellite. The dynamic EIRP calibration algorithm brings back flagged observations from the Block IIF (~37% of the entire dataset) to the CYGNSS standard science data products. It will also help improve the accuracy of Level 2 wind speed retrieval.

The Cyclone Global Navigation Satellite System (CYGNSS) measures GPS signals specularly reflected from Earth's surface to remotely sense ocean surface roughness and wind speed. The mean square slope (mss) is a key physical parameter that relates the ocean surface properties (wave spectra) with the CYGNSS measurement of the normalized bistatic radar cross section (NBRCS). An approach to model the mss for validation with CYGNSS mss data was developed by adding the contribution of a high frequency tail to the IFREMER WAVEWATCH III (WW3) mss. It is demonstrated that the ratio of CYGNSS mss and the modified WW3 mss can be used to diagnose potential calibration errors that exist in the Level 1 calibration algorithm. This approach can help to improve CYGNSS data quality, including the Level 1 NBRCS and Level 2 ocean surface wind speed and roughness.

This letter analyzes the background signals and thermal noise received over ocean scenes in spaceborne global navigation satellite system (GNSS)-reflectometry (GNSS-R) remote sensing using observations from the cyclone GNSS (CYGNSS) constellation. The measured noise floor contains a stable and predictable radiometric thermal noise component and a more variable background signal component from unintended specular reflections within the CYGNSS receive antenna pattern from all GNSS satellite constellations (GPS, Galileo, GLONASS, Beidou, and all SBAS). The presence of SBAS reflections, in particular, is evident in the noise floor measurements, especially the U.S. wide area augmentation system (WAAS) satellites. The results show that the impact is negligible, since background signal contributions by other GNSS transmitters affect both the “signal” and “noise” portions of CYGNSS's delay Doppler map measurements, and therefore cancel when the measured noise floor is subtracted from the desired signal. The potential increase in measurement uncertainty introduced by any residual background signal to the CYGNSS calibration is analyzed.

The eight smallsat CYGNSS constellation was launched into low Earth orbit on 15 Dec 2016. Each satellite carries a four channel bistatic radar receiver which measures GPS signals scattered from the Earth surface, from which surface roughness and wind speed are determined over ocean and soil moisture and inland flooding are determined over land. The mission architecture and satellite design are presented and related to the resulting science data products and their applications. Examples are presented of early on orbit engineering commissioning, calibration and validation of the science data products, and some recent scientific results and applications.

Level 1 calibration of the Cyclone Global Navigation Satellite System (CYGNSS) measurements requires an accurate estimate of the effective isotropic radiated power (EIRP) of the GPS transmitter in the direction of specular reflection. Variable transmit power by numerous Block IIF and IIR-M GPS space vehicles was observed due to their flex power mode. GPS antenna gain patterns feature azimuthal asymmetry, further complicating EIRP estimation. As a result, all Block IIF observations by CYGNSS are flagged and not used to measure ocean winds. A new (version 3.0) Level 1 calibration algorithm is developed which uses measurements by the direct (zenith) antenna to estimate the specular GPS EIRP used to calculate the bistatic radar cross section (BRCS) of the ocean surface. Direct signal measurements are used to estimate GPS transmitter EIRP in the direction of the CYGNSS spacecraft. By applying corrections to the direct signal EIRP, it is possible to estimate the GPS EIRP in the direction of the specular reflection point. This real-time EIRP calibration algorithm instantaneously detects and corrects power fluctuations in all GPS block transmitters and significantly reduces errors due to the GPS antenna gain azimuthal asymmetry. It also allows observations with Block IIF transmitters (approximately 37% of the entire dataset) to be included in the standard data products.

The processing procedures used for the calibration and validation of Level 2 ocean surface wind speed data product for the CYGNSS mission will be presented in this work. The validation process is planned against a series of ground truth matchups which include buoy measurements, other existing satellite counterparts such as scatterometers, radiometers, altimeters and data from global forecast models.

An overview of the wind speed retrieval algorithm used to generate the first CYGNSS Level 2 wind speed products is presented. The algorithm uses two observables derived from Level 1b calibrated Delay/Doppler Maps, and constructs a geophysical model function which maps the observable value and its associated incidence angle into a wind speed value. The wind estimates from the two observables are also combined to form a best-weighted estimator, which represents our final retrieved wind speed. Here the major steps needed to implement the algorithm are reviewed, with a focus on some new aspects, such as the characterization of the dependence of the observables on incidence angle based on real data. An analysis of the algorithm performance is also presented.

This presentation will include an overview of the recently launched NASA CYGNSS mission Level 1 calibration algorithms and their on-orbit validation [1], [2]. The validation of the Level 1 calibration will be performed in several steps, including a) a detailed noise floor analysis to assess the observed on-orbit noise power levels over the open ocean, b) multiple consistency checks using a forward model and co-located ocean wind and wave truth reference data and c) a term by term error analysis of all the non-ocean corrections applied to the final sigma0 estimates. An outline of the Level 1a (calibration from raw Level 0 instrument counts to units of watts for the received power) and the Level 1b (calibration from watts to bistatic scattering cross section) algorithms are each shown below. Three key components of the Level 1a calibration will be presented, namely, an analysis of the instrument (alone) and antenna noise characteristics over the ocean, a study of the range of received power levels from the surface, and comparisons with a forward model. The key components of the Level 1b calibration presented here will include validation of the main corrections applied to arrive at a surface sigma0 estimate, including receiver antenna gain, GPS transmitter and scattering area corrections.

This paper analyzes and compares multiple techniques for estimating the mean square slope (MSS) of surface waves during Hurricane Ike in the Gulf of Mexico and studies the correlation of these estimates with co-located independent surface wind measurements. The measurements used in this analysis include a GPS reflections instrument, a Scanning Frequency Microwave Radiometer (SFMR), a Wide Swath Radar Altimeter (WSRA), and HWinds modeled wind fields. These data sets were used to study the correlation between the near-surface wind and waves during Hurricane Ike in 2008. The GPS-R, SFMR and WSRA instruments recorded temporally and spatially coincident data during two passes over the hurricane eye. This paper estimates the ocean surface waves mean square slope using GPS-R for two eye transects using: a) a least squares model fitting technique; b) a model-based waveform width method; and c) a model-based integration of the area around the reflection peak. Subsequently, the correlation between the GPS-R MSS estimates and the SFMR wind speed estimates are compared to reveal regions of high and low wind/wave correlation. Subsequently, the correlation of MSS estimates from the higher frequency WSRA instrument and SFMR wind estimates are calculated. It has been found that (generally) the wind and wave correlation is significantly better on the western side of the storm during both hurricane eye transects. A relationship between wind and waves was then derived from the GPS-R and SFMR data and is compared to existing wind/wave models, including the results obtained by Katzberg et al and with the Elfouhaily et al theoretical model.

The NASA Earth Venture Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of eight microsatellite observatories that was launched into a low (35°) inclination, low Earth orbit on 15 December 2016. Each observatory carries a 4-channel GNSS-R bistatic radar receiver. The radars are tuned to receive the L1 signals transmitted by GPS satellites, from which near-surface ocean wind speed is estimated. The mission architecture is designed to improve the temporal sampling of winds in tropical cyclones (TCs). The 32 receive channels of the complete CYGNSS constellation, combined with the ~30 GPS satellite transmitters, results in a revisit time for sampling of the wind of 2.8 hr (median) and 7.2 hr (mean) at all locations between 38° North and 38° South latitude. Operation at the GPS L1 frequency of 1575 MHz allows for wind measurements in the TC inner core that are often obscured from other spaceborne remote sensing instruments by intense precipitation in the eye wall and inner rain bands. An overview of the CYGNSS mission is presented, followed by early on-orbit status and results.

The Cyclone Global Navigation Satellite System (CYGNSS) uses the Global Positioning System (GPS) constellation (32 satellites) as the active source in a bi-static radar configuration, with CYGNSS acting as the passive radar receiver. A knowledge of Equivalent Isotropically Radiated Power (EIRP), based on transmit power and antenna pattern of GPS satellites, is of great importance in the accurate calibration of L1B data (bistatic radar cross section, BRCS) of the CYGNSS mission. However, the current knowledge of the EIRP of GPS satellites is limited. There exists an uncertainty of transmit power, and only 20 laboratory-measured antenna patterns have been published. Due to the azimuthal asymmetry of the patterns, the yaw attitude of GPS satellites may affect the EIRP. Therefore, a ground-based GPS constellation power monitor system has been built to accurately and precisely measure GPS signals in watts and, from that, estimate the transmit powers and antenna patterns of all GPS satellites. Measurement data without absolute calibration demonstrates that the GPS yaw attitude does affect the received power. A low noise amplifier (LNA) and calibration subsystem implemented on a PID controlled thermal plate is calibrated with a liquid nitrogen source, showing stable and reasonable results. With the absolute calibration of GPS signals. the retrieved GPS parameters will serve as inputs to the CYGNSS L1B calibration algorithm to improve the data accuracy.

This paper develops and characterizes the algorithms used to generate the Level 1 (L1) science data products of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The L1 calibration consists of two parts: the Level 1a (L1a) calibration converts the raw Level 0 delay-Doppler maps (DDMs) of processed counts into received power in units of watts. The L1a DDMs are then converted to Level 1b DDMs of bistatic radar cross section values by unwrapping the forward scattering model and generating two additional DDMs: one of unnormalized bistatic radar cross section values (in units of square meters) and a second of bin-by-bin effective scattering areas. The L1 data products are generated in such a way as to allow for flexible processing of variable areas of the DDM (which correspond to different regions on the surface). The application of the L1 data products to the generation of input observables for the CYGNSS Level 2 (L2) wind retrievals is also presented. This includes a demonstration of using only near-specular DDM bins to calculate a normalized bistatic radar cross section (unitless, i.e., m2/m2) over a subset of DDM pixels, or DDM area. Additionally, an extensive term-by-term error analysis has been performed using this example extent of the DDM to help quantify the sensitivity of the L1 calibration as a function of key internal instrument and external parameters in the near-specular region.

This paper presents a method and experimental results for near-surface wind sensing using reflected Global Navigation Satellite Systems (GNSS) signals received on a spacecraft. The estimation method proposed involves four steps. First, the bistatic radar cross section (BRCS) of the received signal is estimated from the measurements. Second, the BRCS measurements are calibrated to agree with existing theoretical and empirical wind-wave models. Next, a geometric optics-based scattering model is used to estimate the sea surface slopes, based on the reflection geometry and the measured BRCS. Finally, the surface winds are estimated using an empirically derived function relating the surface mean square slopes to near-surface wind speed. The accuracy of the proposed inversion technique is then tested using a set of 25 space-based GNSS reflection measurements over a range of wind speeds. These measurements were all taken in the proximity of ocean buoys which provided in situ ocean wind speed information. The wind estimates from the buoys were then compared with the wind retrievals made from the measurements and found to be accurate to a root-mean-square error of 1.84 m/s. Additionally, the potential error sources in the measurements are analyzed, including a simulation of the effects of wind direction on the BRCS measurements. This first demonstration of space-based GNSS scatterometry using a small set of sample measurements will hopefully provide a benchmark and example for future experiments.

Bistatic remote sensing using L-band GPS signals has been proposed as an alternative to using microwave radiometers and monostatic radar scatterometers for spaceborne ocean surface windspeed measurements. L-band scattered signals are sensitive to waves with longer wavelengths than are the signals sensed by conventional radiometers and scatterometers, which typically operate at higher frequencies. It is known that longer surface waves take more time to respond to surface winds, propagate further before decaying, and are generally less directly coupled to the local wind field. These factors could affect the ability of scattered GPS L-band signals to retrieve local wind fields. In this work, we attempt to quantify the relationship between the longwave spectrum and local winds by examining windspeed and surface slope measurements by buoys. Specifically, by applying a lag-correlator, it is observed that the average lag time decreases monotonically as the ocean surface wavelength decreases. It is found that 1 hour serves as a conservative bound on the average response time of L-band waves to local wind forcing.

The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne mission concept focused on tropical cyclone (TC) inner core process studies. CYGNSS attempts to resolve the principle deficiencies with current TC intensity forecasts, which lies in inadequate observations and modeling of the inner core. CYGNSS consists of 8 GPS bistatic radar receivers deployed on separate nanosatellites. The primary science driver is rapid sampling of ocean surface winds in the inner core of tropical cyclones.

In this paper we analyse the GPS signals reflected by the surface of the ocean to retrieve information about the sea surface roughness, expressed in statistical terms by means of the sea surface Mean Square Slopes (MSS). Particularly, we perform Delay-Doppler mapping of real scattered GPS signals from the Surrey Satellite Technolody Ltd UK-DMC mission, and we simulate Delay-Doppler Maps (DDMs) using the Zavorotny-Voronovich model of the GPS power scattered from the ocean surface, as a function of the geometrical properties of the transmitter and receiver, as well as statistical properties of the scattering surface. Subsequently, we fit simulated DDMs to the measured ones, to retrieve the optimal MSS of the scattering surface, and we compare GPS-derived MSS with theoretical and in situ MSS, calculated using the Elfouhaily et al. wave spectrum and co-located buoy spectra of the National Data Buoy Center (NDBC).

Signals from the Global Positioning System (GPS) are constantly being scattered off the entire Earth's surface. These signals can be detected using a low Earth orbiting receiver and have the potential to be used to remotely sense the Earth's ocean, land and ice surface conditions. It is known that the received signals are linked to the ocean surface roughness and height, land surface water conditions and sea ice concentration. However, in order to better understand the potential and limitations of this new technique, the scattered signals fading (or speckle) noise must be properly understood. As consecutive observations are captured from the scattering surface, the fading noise on the signal is gradually reduced to reveal the true signals power profile across the surface. The ultimate accuracy of measurements obtainable using bistatically reflected GPS signals will depend on how accurately this true signal can be estimated in the presence of fading noise. For the case of bi-statically reflected GPS signals the fading noise is of interest over different ranges of delays on the surface. From a space-based platform the signal power is normally detected over a very large surface area, covering a wide range of delays and frequencies, each with unique fading noise statistics. Using selected signals from the UK-DMC satellites bistatic GPS experiment it has been possible to examine several reflected signals from a range of surfaces. This paper will present the observed fading statistics of four signals collected under different conditions; calm ocean, rough ocean, ocean with swell present and a reflection from land. The measurement fluctuations will be presented as a function of the number of averaged looks and time delay across the surface.

Signals from the Global Positioning System are constantly being scattered off the entire Earth's surface. These signals can be detected from airplanes, surface platforms and in Low Earth orbit and are known to contain information on the oceans. Recently, a new opportunity to study GPS reflections in space has been realized with the launch in October 2003 of the United Kingdom's Disaster Monitoring Constellation (UK-DMC) satellite. The UK- DMC carries a dedicated GPS reflections receiver and a custom designed downward facing antenna. Using this GPS experiment it is possible to process surface scattered signals and estimate the ocean wind and waves. This paper will present a method for calculating a bistatic radar cross section (BRCS) for ocean scattered GPS signals. This method will then be applied over a range of signals detected by the UK-DMC experiment. For purposes of comparison, numerous data collections have been collocated with National Data Buoy Center (NDBC) buoys. These buoys provide an independent measurement of the ocean wind, waves and often a wave frequency spectrum from which the surface mean square slopes, an indication of ocean roughness, can be determined. BRCS values have been estimated over a range of ocean conditions (from less than 2 m/s winds to greater than 14 m/s winds) and vary generally as expected when compared to buoy measurements of the ocean wind and waves. An empirical relationship between the estimated BRCS and the ocean wind speeds and surface roughness will be presented for a limited set of data gathered by the UK-DMC experiment.

the paper explores the accuracy in estimating wind using the Global Navigation Satellite System Reflectometry concept. It is based on modeling the measured signal by considering the potential observable as parameters of interest and deriving the Cramer-Rao Lower Bound.The Cramer-Rao Lower Bound, being the minimum achievable covariance of an estimator, is a useful tool to evaluate the accuracy of the wind estimate from a spaceborne Global Positioning System reflectometry experiment. An attempt to derive this bound in the case of the UK Disaster Monitoring Constellation Low Earth Orbit experiment is presented.

Recent results from the GNSS bi-static radar experiment on-board the low Earth orbiting UK Disaster Monitoring Constellation satellite have shown that GPS signals scattered off the ocean surface can be detected under a wide range of ocean conditions and these signals are known to contain valuable and varied information on the Earth's environment. Several data sets have been down loaded since the experiment was successfully commissioned in March of 2004, a number of these data sets have been collocated with what have been deemed to be reliable in-situ measurements. Sea wind and wave estimates have been obtained from the National Data Buoy Center (NDBC) Buoy network, which can now be compared to the signals retrieved in the down linked raw data sets.This paper will present experimental attempts to sense ocean wind at L-band using simple empirical inversion formulas based on post-processed correlation levels of the ocean-scattered signals. Additionally, the possibility of using the Doppler spread of the detected signals to measure the sea surface wave heights will also be explored.

We will show that ocean-reflected signals from the global positioning system (GPS) navigation satellite constellation can be detected from a low-earth orbiting satellite and that these signals show rough correlation with independent measurements of the sea winds. We will present waveforms of ocean-reflected GPS signals that have been detected using the experiment onboard the United Kingdom's Disaster Monitoring Constellation satellite and describe the processing methods used to obtain their delay and Doppler power distributions. The GPS bistatic radar experiment has made several raw data collections, and reflected GPS signals have been found on all attempts. The down linked data from an experiment has undergone extensive processing, and ocean-scattered signals have been mapped across a wide range of delay and Doppler space revealing characteristics which are known to be related to geophysical parameters such as surface roughness and wind speed. Here we will discuss the effects of integration time, reflection incidence angle and examine several delay-Doppler signal maps. The signals detected have been found to be in general agreement with an existing model (based on geometric optics) and with limited independent measurements of sea winds; a brief comparison is presented here. These results demonstrate that the concept of using bistatically reflected global navigation satellite systems signals from low earth orbit is a viable means of ocean remote sensing.

A study has been undertaken by Surrey Satellite Technology Limited (SSTL) with support from the British National Space Centre (BNSC) to upgrade SSTL's Space GPS Receiver (SGR) into an ocean remote sensing instrument. Software algorithms were added to the SGR to permit on-board characterisation of GPS reflection opportunities. Subsequently, a hardware interface was added to allow for raw data sampling and in-depth data analysis. The upgraded SGR has undergone several levels of testing to date. The real time specular point calculations and an on board slewing capability were demonstrated using a Low Earth Orbit (LEO) spacecraft carrying an SGR for navigation purposes. The ability to map a reflected GPS signal was then accomplished during ground testing from an observatory on a hill above Barcelona, Spain. In preparing the instrument for an upcoming flight experiment, the raw data analysis capability has been undergoing substantial validation and optimisation. The ultimate goal of these experiments is to recover parameters relating to sea-state that may be useful for both scientific and commercial marine users. This paper is intended to detail the initial phases of development and testing of a future low cost ocean remote sensing instrument.