The sensitivity of Global Navigation Satellite System Reflectometry (GNSS-R) to ocean surface roughness is well-established within the scientific and technological communities. Missions like the CYclone GNSS (CYGNSS) have been specifically designed to detect and monitor hurricanes and other oceanic storms. Additionally, data from the Soil Moisture Active Passive (SMAP) mission’s radar, operating in receive mode, known as SMAP-Reflectometer or SMAP-R, provide opportunities to evaluate the polarimetric sensitivities of GNSS-R measurements to ocean surface roughness. This enables exploration of potential applications that can be achieved or enhanced through the use of polarimetric techniques. This study provides a sensitivity analysis of wind-driven changes in surface roughness using a comprehensive view of full polarization GNSS-R signals over the ocean. The results show an improvement of about 0.6% on ocean wind speed retrievals for dual polarization GNSS-R missions respect to single polarization GNSS-R missions. The results show an improvement of about 4.5% on ocean wind speed retrievals for full polarization GNSS-R missions respect to single polarization GNSS-R missions.

L-band radiometry for retrieving surface soil moisture is challenged by the confounding influence of surface roughness scattering and vegetation attenuation. This study presents a novel retrieval framework that uses GNSS reflectometry (GNSS-R) coherence time metrics from Soil Moisture Active Passive-Reflectometry (SMAP-R) data to characterize global surface roughness and vegetation parameters. By analyzing the decay of signal-to-noise ratio (SNR) across varying coherent integration times, we estimate temporal coherence time (τc) and derive surface roughness (σ) without ancillary topographic data. A polarization mixing parameter (Pmix), derived from the degree of polarization (DoP), is related to the polarization decoupling factor (Q) used in SMAP models. These variables feed into a τ−ω radiative transfer model to retrieve vegetation optical depth (τ) and single-scattering albedo (ω) directly from observations. The resulting global maps of σ,Q,τ, and ω show enhanced spatial structure and biome sensitivity. They can constrain the empirical SMAP static ancillary inputs used now for constraining the inversion of L-band brightness temperatures for soil moisture. To evaluate the impact of the new vegetation parameters, we apply a neural network trained on SMAP data to retrieve soil moisture using both the original and derived VOD inputs. Differences in soil moisture are spatially coherent and physically consistent, with lower values in high-biomass regions and improved retrieval in arid zones. This work demonstrates that GNSS-R coherence metrics can be integrated with L-band radiometry to produce observation-driven land surface parameters. The approach reduces dependence on empirical or static climatological inputs and supports improved soil moisture retrievals from passive microwave missions.

This manuscript explores the use of Global Navigation Satellite System–Reflectometry (GNSS-R) data collected by the Soil Moisture Active Passive (SMAP) mission to estimate antenna view angle offsets. A novel methodology for estimating antenna view angle offsets using GNSS-R data is proposed by comparing the received signal-to-noise ratio (SNR) of the reflected GNSS signal with SMAP’s antenna pattern measured prior to launch. To properly compare them, observation angles are defined with respect to the SMAP position and the specular point position, which are computed from the SMAP telemetry and the Global Positioning System (GPS) ephemeris data using a 1-km resolution digital elevation model (DEM) map. A methodology based on a second-order polynomial fit and a linear fit is proposed to estimate the offset by azimuthal sector and at different times of the year. Results are obtained for a total of six years, showing a consistent dependence on the antenna azimuthal look angle and on the time of the year, with an average antenna offset of 0.2° with a standard deviation of 0.06°, which produces a geolocation error of ~3.6 km. Further analysis proposes a nonlinear model to estimate the antenna offset directly from an azimuthal look angle and the time of the year. Model results show a Pearson correlation coefficient of$R$= 0.85 and$R$= 0.80 for descending and ascending passes, respectively. This model can be used by the SMAP team to further correct the antenna footprint location and pointing angle.

This article presents the mathematical background and modeling for full-polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) receivers for nonnegligible cross-polar component in the scattered signal. A signal model is presented to retrieve the Stokes parameters using the Mironov model to estimate the soil surface’s dielectric constant. This article compares data collected by the SMAP-Reflectometry (SMAP-R) receiver and the proposed signal model, emphasizing the need to consider the cross-polar component ($S_{\mathrm {hv}}$). Simulations obtained without considering the cross-polar component have poor agreement in all Stokes parameters. A model is implemented using a complex cross-polar component resulting in notable improvements in the bias and unbiased root mean square difference (ubRMSD) between the modeled and measured SMAP-R Stokes parameters. Furthermore, a methodology is proposed for estimating the$S_{\mathrm {hv}}$component using SMAP-R and a soil moisture (SM) reference dataset. An analysis of$S_{\mathrm {hv}}$reveals a moderate correlation with vegetation water content (VWC) and surface roughness ($\sigma _{\mathrm {slp}}$). We developed an SM model by estimating$S_{\mathrm {hv}}$using both VWC and$\sigma _{\mathrm {slp}}$, showing the potential of full-polarimetric GNSS-R to provide SM estimates upon proper characterization of the cross-polar component. Results show the ubRMSD of 0.09 m3/m3 with respect to the in situ soil moisture network (ISMN). Finally, this study establishes that an RMSD better than 0.02 when estimating$S_{\mathrm {hv}}$is required for an SM product accuracy better than 0.07 m3/m3 for polarimetric GNSS-R SM retrievals.

The shift between frozen and thawed conditions on the Earth’s surface influences climate, hydrology, and ecology. A primary objective of the Soil Moisture Active Passive (SMAP) mission is to estimate the surface binary freeze/thaw (F/T) state for the northern hemisphere above 45° N latitude with a classification accuracy of 80% at 3-km spatial resolution and 2-day intervals. The objective was to be accomplished by the SMAP L-band radar measurements. In July 2015 the radar transmitter suffered an anomaly that prevented it from nominal operation. The mission partially met the goal exceeding the targeted 80% accuracy but at 36 km resolution. After the radar anomaly, the SMAP mission switched the radar receiver band pass filter frequency to GPS L2c, acting as a full-polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R) receiver, known as SMAP-Reflectometer (SMAP-R). This work focusses on using SMAP-R signals to classify the F/T state over the Northern latitudes, within the limitations of the dataset. An algorithm based on the seasonal threshold approach that was originally envisioned for the SMAP radar is applied to the SMAP-R data (i.e., bistatic radar). Then the algorithm is evolved using a Random Forest algorithm to aid threshold selection from the discriminator built in the seasonal threshold approach. This algorithm is applied for years 2016 to 2022 over the Northern Hemisphere terrestrial cryosphere and shows an F/T classification accuracy agreement better than 97% with respect to the classification of the official SMAP F/T Radiometry product, proving the potential of polarimetric GNSSR to derive F/T.

Polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) is the next natural step for land monitoring using GNSS signals. The Soil Moisture Active Passive (SMAP) radar receiver has been retrieving polarimetric GNSS-R data using two orthogonal linearly polarized antennas since 2015, enabling the study of the polarimetric signature of GNSS-R signals on different Earth’s surfaces. Currently, new instruments and missions are using circularly polarized antennas to retrieve polarimetric GNSS-R information. In this manuscript, synthetic right and left-hand circularly polarized signals are reconstructed using the Stokes parameters of the SMAP L2C GNSS-R data. The signal-to-noise ratio (SNR) of the SMAP-R data at the equivalent RHCP, LHCP, H, and V polarization antennas is retrieved, and normalized to an arbitrary receiver with a noise figure of 2 dB. Appling the Albersheim model, we analyze the probability of detecting a reflection in a 0.5° Lat/Lon box. Results are presented for different configurations of coherent and incoherent integration times and antenna gains, for each possible antenna polarization. We present different receiver configurations capable of detecting more than 70% and 90% GNSS reflections over land. Results show that with a 10-dB antenna and a receiver with a coherent integration time of 4 ms, and an incoherent integration time of 1000 ms would suffice to detect 19.4%, 92.3%, 83.5%, and 79.4% for RHCP, LHCP, H-polarized, and V-polarized antenna, respectively. Detectability improves up to 57.4%, 99.3%, 96.3%, and 96.6% using a 14-dB antenna. Results are then generalized to L1 C/A GNSS-R signals.

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

This manuscript provides the first Hybrid Compact Polarimetric (HCP) Global Navigation Satellite System – Reflectometry (GNSS-R) analysis of the cryosphere. By reconstructing the full-Stokes parameters and the child Stokes parameters from the reflectometry signals collected by the SMAP radar receiver, the sensitivity to the geophysical changes of the Earth’s landscapes of these opportunistic signals are analyzed. This analysis is focused over the Artic Sea ice formation zone, the northern latitudes presenting freeze and thawed transitions, and the Greenland ice sheet. We conduct a sensitivity analysis for each landscape. The signals analyzed over the Arctic Sea show correlation with sea ice concentration (SIC) of 0.77 for the second Stokes parameterS1and 0.7 for the child Stokes parameter χ, which describes the ellipticity of the wave. The signals analyzed over the northern latitudes present a sensitivity to the two states (frozen and thawed). The analysis over Alaskan landscape shows a total reflectivity Γ0difference between freeze and thaw states of ~8 dB with a standard deviation of ~1 dB. Greenland presents a challenge since available datasets are not from the same timeframe, despite of this our initial evaluation shows that there is a ~0.5 correlation between ice thickness and the third Stokes parameterS3and the relative phase δ between the two linear electromagnetic components, h-pol and v-pol. This manuscript provides an extensive view of the HCP GNSS-R signals over the cryosphere and finds initial sensitivities to the seasonal geophysical changes of the different areas.

The Soil Moisture Active Passive - Reflectometry (SMAPR) is the first polarimetric L2C GNSS-R dataset collected from space. Given that the dataset consists of I/Q raw data, different processing techniques can be applied. In this paper we explore the calculation of the full polarization Stokes derived from the horizontal (H) and vertical (V) polarization measurements of the transmitted right-hand circularly polarized (RHCP) Global Positioning System (GPS) signal. This configuration is known as hybrid compact polarimetry (HCP) radar, in this case HCP of a bistatic radar or a GNSSR instrument. This work describes how to use the collected data to obtain the full Stokes parameters and presents a glimpse to its value in the GNSS-R field, in particular the value of the phase of the cross-correlation signal between the H and V polarization measurements.

Global Navigation Satellite System - Reflectometry has shown promising results to retrieve land-related parameters, such as soil moisture. Polarimetric radar measurements offer additional information about the scattering scenario. SMAP-R is the first polarimetric GNSS-R dataset collected from the space thanks to the reception of the Horizontal and Vertical components of an incident circular polarized signal, allowing the retrieval of the full-Stokes parameter of the reflection. The reflectivity derived from the first Stokes bistatic radar cross section is compared to the SMAP brightness temperature, showing their dependence with vegetated areas by means of a linear equation. Finally, a polarimetry analysis is conducted proposing different polarimetric ratios derived from SMAP-R Stokes parameters, and the MODIS-derived vegetation water content.

SMAP-R is the first polarimetric GNSS-R dataset collected from the space. The reception of the Horizontal and Vertical components of an incident circular polarized signal allows to retrieve the Full-Stokes parameter of this hybrid compact polarimetry radar configuration. In this manuscript, the Stokes parameters of the reflection scenario are defined and calculated. The first Stokes is calibrated to retrieve the normalized bistatic radar cross section as main observable. The SMAP-R dataset is validated by collocating it to CYGNSS measurements. The results show a good agreement with CYGNSS normalized bistatic radar cross section, with an unbiased root-mean-square error of 3.3 dB for a 6 months period, showing also larger sensitivity over vegetated areas.

The Soil Moisture Active Passive (SMAP) is capable of performing Global Navigation Satellite System Reflectometry (GNSS-R) measurements. The SMAP-R global dataset extends from July 2015 to present and covers latitudes up to ∼82°, which benefit multiyear cryosphere studies. In this paper we perform an initial evaluation of the freeze/thaw (F/T) state over Alaska and the sea ice presence over the Beaufort and Chukchi seas area for the periods January to March 2018 and July to September 2018. This paper investigates the sensitivity of the current fully-calibrated SMAP-R dataset to the changes in the cryosphere.

Shortly after its launch, the Soil Moisture Active Passive (SMAP) mission started using its radar receiver to collect Global Positioning System (GPS) bistatic radar measurements by switching the band-pass center frequency of its radar receiver to the GPS L2C band. We refer to this functionality of the SMAP mission as SMAP-Reflectometry or SMAP-R. The collected SMAP-R dataset represents the first polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) dataset from space and brings unique capabilities as compared to other co-existing GNSS-R missions, i.e., CYclone Global Navigation Satellite System (CYGNSS). This paper formally introduces the SMAP-R dataset in its more current status and provides a wide overview of its science capabilities.

Global Navigation Satellite System – Reflectometry (GNSS-R) is a promising field with a diverse set of applications. Polarimetric GNSS-R has been shown to be sensitive to different land parameters, such as freeze/thaw, crop growth, or soil moisture. In this manuscript, the polarimetric scenario of a GNSS reflection is defined, and the theoretical basis and the algorithm to retrieve the Stokes parameters of a GNSS-R signal is proposed. The presented algorithms are validated using data collected by the two linear orthogonal polarization channels of the SMAP radar antenna working as GNSS-R, known as SMAP-Reflectometry (SMAP-R). The Stokes parameter retrieval is presented for the SMAP-R case, and the receiver phase offset and intensity are calibrated. The same calibration procedure proposed by the CYclone GNSS (CYGNSS) mission is applied to the SMAP-R first Stokes parameter, allowing the retrieval of the normalized bistatic radar cross section of such measurement. Finally, the SMAP-R data is compared to the CYGNSS Normalized Bistatic Radar Cross Section (NBRCS) over the ocean, showing an unbiased root-mean-square error of 3.3 dB, and a small bias.

The Goldstone Solar System Radar (GSSR) facility is the largest fully steerable ground-based radar in the world for nonclassified high-resolution ranging and imaging of planetary and small-body targets. Over the years, the use of the GSSR to detect and characterize near-Earth objects (NEOs) has become critical to keep track of potential Earth-impacting hazardous NEO. This article relates the specific modifications made to the GSSR hardware and procedures in the last few years, as well as the new capabilities derived from those upgrades: reduced complexity in remote operations, increased experimental design versatility, and increased performance on bistatic radar experiments from GSSR to other complexes. In addition, we dedicate a section to provide an update on the current GSSR power capabilities as the new klystrons are installed. The work detailed in this article is intended to reach the broader science community in order to communicate how those modifications and the derived new capabilities can make science experiments more successful.