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.

An analysis of the potential and limitations of interferometry using near-specular signals of opportunity (SoOp) remote sensing systems for surface height measurement is presented. Results from an attempt at “repeat pass” interferometry with NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission conducted on September 30, 2019, are reviewed, along with models for the sensitivity of the interferometric phase to surface height in “single-pass” multiple-antenna observations. An analysis of 58 CYGNSS “raw I/F” tracks is reported for which single-pass interferometry is feasible given a specular point’s location in the overlap region of CYGNSS’s two receive antennas on a selected satellite. The observations and models developed highlight the impact of parameters such as the baseline distance separating the two antennas, the size of the scattering footprint, the incidence angle, the measurement bandwidth, and the measurement SNR. The results describe the potential of near-specular SoOp interferometry but also demonstrate factors that can limit the sensitivity achievable to a fraction of that obtained by interferometric synthetic aperture radars (SARs).

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.

Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

The development of soil moisture retrieval algorithms for the upcoming NASA/ISRO SAR mission motivates investigations into soil surface scattering effects and their influence on soil moisture retrievals. To better understand the conditions that impact retrieval performance, an investigation is performed into the second-order solution small slope approximation (SSA2) for rough surface backscattering. Scattering amplitudes are calculated for varying soil moisture and surface roughness conditions and compared to the first-order solution. The results show the ability of the second-order solution to model cross-pol scattering and help pinpoint physical conditions that may influence soil moisture retrievals. Overall, the results suggest that the first-order SSA solution should be applicable under the physical conditions where NISAR soil moisture retrievals are expected to be performed.

The use of a “time-series ratio” soil moisture retrieval approach is under consideration for the upcoming NISAR mission’s soil moisture product. As such, it is of interest to characterize the algorithm’s anticipated error budget as part of pre-launch activities. This paper develops an error model to estimate retrieval errors for the proposed algorithm. The model accounts for error contributions from speckle and thermal noise as well as uncertainties that arise as part of the retrieval process. Spatial and temporal behaviors of the retrieval errors are determined using a SMAP-based soil moisture climatology. The results show that soil moisture retrieval errors from the time-series ratio method meet the 0.06 m3/m3unbiased root mean square error (URMSE) performance metric established for the NISAR soil moisture product.

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.

The physical process of GPS signal reflection from Earth's land surface is of interest in order to support the development of land applications using GNSS-Reflectometry (GNSS-R) data. A key question is understanding when GNSS-R land returns should be examined in terms of their reflectivity or normalized radar cross section (NRCS). An airborne lidar DEM obtained near White Sands, NM is used to compute surface roughness properties. The roughness map is then correlated with the recurrence of coherency derived from three years of CYGNSS observations. The results show that “medium scale” rms heights within the first Fresnel zone of less than < ~ 10 cm are associated with coherent reflections. Initial analysis of a raw IF data track further demonstrates the decrease of coherence with increased surface roughness.

A previously reported time-series ratio method for retrieving soil moisture information from Cyclone Global Navigation Satellite System (CYGNSS) measurements is updated to improve the algorithm used for excluding CYGNSS measurements from the time series, thereby improving the algorithm’s computational efficiency while eliminating the requirement for contemporary soil moisture information from Soil Moisture Active Passive (SMAP). Daily CYGNSS soil moisture estimates are retrieved and presented on a 36-km grid over the 27-month period January 2018–May 2020 and show an overall root mean square error$\approx 4.46$%, an overall correlation for all attempted retrievals of$\approx 80.53$%, and an average correlation for individual pixels of 48.61% relative to SMAP using the updated time-series algorithm. An analysis of the errors of the updated algorithm is also provided as a function of surface properties.

This study investigates a means through which commercially available computational electromagnetic modeling software can be used to predict radar cross sections (RCSs) of airborne organisms of interest as a preliminary step toward enabling detection and tracking of these organisms. This work aims to analyze this framework for the specialized case of the honey bee (Apis mellifera), given its critical role in food security as a major pollinator of agricultural crops. A Method-of-Moment (MoM) solver made available by Altair’s FEKO is used to conduct the analysis over varying frequencies, illumination angles, and polarizations. A high degree of correlation between measured and modeled cross sections is noted. Maximum RCS root-mean-square errors (RMSEs) between the two are approximately 4 and 5 dB relative to 1$\text{m}^{2}$(dBsm) for Horizontal polarization (H-pol) and Vertical polarization (V-pol) X-band measurements, respectively. Findings of this study also highlight the sensitivity of both modeled and measured RCS estimates to the dielectric properties of honey bees and the corrupting effects that this may have if not accounted for accurately, where errors are shown to increase from 2 to 5 dBsm, but without significantly corrupting the overall RCS azimuth profile.

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.

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.

NASA's CYclone Global Navigation Satellite System (CYGNSS) mission operates a constellation of 8 small satellites that provide ocean wind speed measurements with dense spatial coverage and a median revisit time of about 4 hours. This paper investigates the possibility of using the “rapid revisit” characteristics of CYGNSS measurements to create a detector of convective activity by examining the variability of CYGNSS wind speeds over small intervals in space and time.

Time-series retrievals of soil moisture obtained from the Cyclone Global Navigation Satellite System (CYGNSS) constellation are presented. The retrieval approach assumes that vegetation and roughness changes occur on timescales longer than those associated with soil moisture changes to allow soil moisture sensing in the presence of vegetation and surface roughness contributions as well as the varying incidence angles associated with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) systems. The approach is focused on incoherent scattering from land surfaces due to the expectation that coherent land surface returns arise primarily from inland water body contributions that are not directly representative of soil moisture. An approach for discarding coherent CYGNSS measurements is therefore developed and described. Because the approach requires the retrieval of N temporal soil moisture samples at a given location but uses only N-1 ratios of CYGNSS measured quantities, ancillary information is incorporated in the retrieval through the use of maximum and minimum monthly soil moisture maps obtained from the Soil Moisture Active Passive (SMAP) mission. Retrieved soil moistures are presented for the 6-month period December 2017-May 2018 and are compared against values reported by the SMAP mission. The comparisons suggest that there exists the potential for using spaceborne GNSS-R systems for global soil moisture retrievals with an rms error on the order of 0.04 cm3/cm3over varied terrain.

An analysis of the coherency properties of specular scattering from ocean and land surfaces as observed in global navigation satellite system-reflectometry (GNSS-R) and signals of opportunity systems is presented. This analysis applies existing approximate models for the coherent and incoherent contributions. Approximate expressions are developed for when one component of the return is likely to dominate as a function of surface and observing system properties. The model developed is then applied for sea surface returns, and the relative contribution of the coherent term is expressed as a function of the receiver height, frequency, incidence angle, and wind speed. For L-band spaceborne measurements, it is shown that coherence is expected only for wind speeds less than 2-3 m/s, while for P-band spaceborne measurements, coherence can dominate returns for wind speeds up to 5-7 m/s. For land surface measurements from space, it is shown that the surface rms height needs to be sufficiently low for coherent components to dominate returns. Coherence dominates for roughness values not exceeding a range of 5-7 cm for the L-band and 15-30 cm for the P-band. For the L-band, these conditions over land are likely to be created primarily by inland water bodies. A model for the specular scattering from a water body, including earth curvature effects, is then developed to highlight the strong dependence of the resulting coherent field on the shape of the water body and any offset in its location from the specular point. These results further clarify the significant variability that should be expected in coherent scattering from inland water bodies.