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.

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.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) mission generates several ocean surface wind speed data products. Level 2 products are sampled in time and space as the measurements are made by each individual satellite in the constellation. Due to the nature of GNSS-R measurements, the sampling is essentially a single pixel-wide swath along the specular point (SP) track on the Earth surface. However, the large number of satellites and their low orbit inclination angle result in dense spatial coverage and frequent revisit rates in the tropics when measurements are aggregated together. Standard Level 3 wind products combine measurements by the full constellation which are then gridded in space and averaged over one hour. A new Level 3 storm-centric gridded wind speed product is reported on a finer spatial grid every 6 hours to better resolve storm structure throughout its lifecycle. Spatial and temporal sampling properties, as well as wind speed uncertainties, are presented here for each of the CYGNSS ocean surface wind speed products.

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.

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 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.

An improved blackbody calibration procedure is developed, implemented, and tested for the cyclone global navigation satellite system (CYGNSS). Previously, CYGNSS calibrated its receivers once every minute to account for temperature-induced gain fluctuations. The time spent making calibration measurements limited the duty cycle of wind-speed measurements to approximately 90%. The analysis presented here shows that the 1-min cadence was overly conservative and can be increased to once every 10 min with minimal impact to data quality, thereby improving the wind-speed duty cycle to 98%. A permanent change to the blackbody cadence was made for the complete eight-satellite constellation during July 27, 2021–August 3, 2021, and subsequent analysis verifies that the new cadence improves duty cycle without impacting science data quality, as expected.

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 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.