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Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth's surface. With the rapid development of GNSS-RS techniques and spaceborne missions, many experiments and studies were conducted to assimilate those observational data into numerical weather-prediction models for tropical cyclone (TC) forecasts. GNSS RO data, known for its high precision and all-weather observation capability, is particularly effective in forecasting mid-to-upper atmospheric levels. GNSS-R, on the other hand, plays a significant role in improving TC track and intensity predictions by observing ocean surface winds under high precipitation in the inner core of TCs. Different methods were developed to assimilate these remote sensing data. This review summarizes the results of assimilation studies using GNSS-RS data for TC forecasting. It concludes that assimilating GNSS RO data mainly enhances the prediction of precipitation and humidity, while assimilating GNSS-R data improves forecasts of the TC track and intensity. In the future, it is promising to combine GNSS RO and GNSS-R data for joint retrieval and assimilation, exploring better effects for TC forecasting. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) observations are effective for extensive monitoring of soil moisture (SM) and freeze–thaw (F/T) state. However, there is a lack of long-term, well-defined accuracy, high spatiotemporal resolution SM and F/T datasets for alpine regions. To address these issues, this study first generates five years of daily continuous high spatial resolution GNSS-R F/T and SM datasets for Qinghai-Tibet Plateau based on a proposed multivariate time-fitted observation reconstruction method. Specifically, this study generates spatio-temporally seamless observations based on Cyclone GNSS (CYGNSS) observations for SM and F/T retrieval. The RMSE and R of the SM retrieval are 0.063 cm 3 /cm 3 and 0.52, respectively, which are consistent with the SM retrieval accuracy performed pre-reconstruction. Similarly, the F/T retrieval accuracy was 84.1%, which was also comparable to the F/T retrieval accuracy performed pre-reconstruction. In-situ station evaluation demonstrated that the RMSE and R of the SM retrieval and the F/T retrieval accuracy were 0.063 cm 3 /cm 3 , 0.70, and 85.2%, respectively. This is consistent with the performance of the SM and F/T results from original observations. Notably, the temporal resolution of the CYGNSS reconstructed observations was improved by 278% over the original observations at 9 km. Therefore, we argue that the method proposed in this study addresses the problem of mutual constraints on spatial and temporal resolution in CYGNSS SM and F/T retrievals. Furthermore, this study developed the first high-accuracy and high spatiotemporal resolution SM and F/T dataset for the Qinghai-Tibet Plateau region in the GNSS-R domain. [ABSTRACT FROM AUTHOR]

Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA's average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed. [ABSTRACT FROM AUTHOR]

Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on the wind speed itself is considered. The triple colocation method of validation is used to correct for the quality of the reference wind speed products with which CYGNSS is compared. The dependence of retrieval performance on sea state is also considered, with particular attention being paid to the long wave portion of the surface roughness spectrum that is less closely coupled to the instantaneous local wind speed than the capillary wave portion of the spectrum. The dependence is found to be significant, and the efficacy of the approaches taken to account for it is examined. The dependence of retrieval accuracy on wind speed persistence (the change in wind speed prior to a measurement) is also characterized and is found to be significant when winds have increased markedly in the ~2 h preceding an observation. [ABSTRACT FROM AUTHOR]

This paper presents the mission analysis of 3Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological demonstrations. The satellite incorporates several novel systems, including a deployable Fresnel Zone Plate Antenna (FZPA), an integrated PocketQube deployer, a dual-receiver GNSS board for radio occultation and reflectometry experiments, and a polarimetric multi-spectral imager for auroral emission observations. The mission design, the suite of payloads, and the concept of operations are described in detail. This paper discusses the current development status of 3Cat-8, with several subsystems already developed and others in the final design phase. It is expected that the data gathered by 3Cat-8 will contribute to a better understanding of ionospheric effects on radio wave propagation and demonstrate the feasibility of compact remote sensors in a CubeSat platform. [ABSTRACT FROM AUTHOR]

Algal blooms, resulting from the overgrowth of algal plankton in water bodies, pose significant environmental problems and necessitate effective remote sensing methods for monitoring. In recent years, Global Navigation Satellite System–Reflectometry (GNSS-R) has rapidly advanced and made notable contributions to many surface observation fields, providing new means for identifying algal blooms. Additionally, meteorological parameters such as temperature and wind speed, key factors in the occurrence of algal blooms, can aid in their identification. This paper utilized Cyclone GNSS (CYGNSS) data, Sentinel-3 OLCI data, and ECMWF Re-Analysis-5 meteorological data to retrieve Chlorophyll-a values. Machine learning algorithms were then employed to classify algal blooms for early warning based on Chlorophyll-a concentration. Experiments and validations were conducted from May 2023 to September 2023 in the Hongze Lake region of China. The results indicate that classification and early warning of algal blooms based on CYGNSS data produced reliable results. The ability of CYGNSS data to accurately reflect the severity of algal blooms opens new avenues for environmental monitoring and management. [ABSTRACT FROM AUTHOR]

This paper presents the design of a dual-band and dual-polarized microstrip antenna for global navigation satellite systems reflectometry (GNSS-R) for the L1/E1/B1 and L5/E5a/B2a bands. In order to allow advanced GNSS-R signal processing and sensing techniques, the design has been carried out for dual-band and dual-polarization operation with isolated ports to receive both left and right-hand circular polarizations. The design procedure to allow receiving both bands independently and with high isolation is described in detail. Numerical and experimental results show that the antenna presents good performance in terms of impedance matching, circular polarization purity and isolation between the ports. The measured levels of axial ratio are 0.86 dB and 1.89 dB for the L1/E1/B1 and L5/E5a/B2a bands, respectively. The measured isolation levels are larger than 30 dB and 40 dB in the L1/E1/B1 and L5/E5a/B2a bands, hence proving that the proposed antenna concept can be properly used in dual-band dual-polarized GNSS-R applications. [ABSTRACT FROM AUTHOR]

Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research. [ABSTRACT FROM AUTHOR]

The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous water bodies, such as Taihu Lake. Sensors operating in the microwave band can effectively mitigate this issue, particularly the emerging Global Navigation Satellite System Reflectometry (GNSS-R), which offers high temporal resolution and cost-effectiveness. In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. We also examine the effective range of GNSS-R data within this application scenario. Meanwhile, the Weighted Linear Regression-Laplacian Prior Regulation Method (WLR-LPRM) image gap-filling algorithm is used as a benchmark to evaluate recovery accuracy. The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. Compared to the previous work on GNSS-R algal bloom detection with overall accuracy of 0.82, this work shows significant improvement in both accuracy and utility. The recovery of lake surface NDVI provides detailed insights into algal blooms, including quantifiable metrics such as the amount and spatial distribution, which are crucial for effective monitoring and management. Additionally, the recovered image textures exhibit high clarity and closely resemble the reference NDVI images. Experimental evaluation using simulated and actual cloud blocks indicates the model’s robustness to recover NDVI under varying cloud cover conditions. In summary, this study demonstrates the capability of GNSS-R aided by supplementary data for recovering missing NDVI values on lake surfaces when optical observations are absent for the first time.

Soil moisture data with both a fine spatial scale and a short global repeat period would benefit many hydrologic and climatic applications. Since the radar transmitter malfunctioned on NASA's Soil Moisture Active Passive (SMAP) in 2015, SMAP soil moisture has been downscaled using numerous alternative fine-resolution data. In this paper, we describe the creation and validation of a new downscaled 3 km soil moisture dataset, which is the culmination of previous work. We downscaled SMAP enhanced 9 km brightness temperatures by merging them with L-band Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data, using a modified version of the SMAP active–passive brightness temperature algorithm. We then calculated 3 km SMAP/CYGNSS soil moisture using the resulting 3 km SMAP/CYGNSS brightness temperatures and the SMAP single-channel vertically polarized soil moisture algorithm (SCA-V). To remedy the sparse daily coverage of CYGNSS data at a 3 km spatial resolution, we used spatially interpolated CYGNSS data to downscale SMAP soil moisture. 3 km interpolated SMAP/CYGNSS soil moisture matches the SMAP repeat period of ~2–3 days, providing a soil moisture dataset with both a fine spatial scale and a short repeat period. 3 km interpolated SMAP/CYGNSS soil moisture, upscaled to 9 km, has an average correlation of 0.82 and an average unbiased root mean square difference (ubRMSD) of 0.035 cm3/cm3 using all SMAP 9 km core validation sites (CVSs) within ±38° latitude. The observed (not interpolated) SMAP/CYGNSS soil moisture did not perform as well at the SMAP 9 km CVSs, with an average correlation of 0.68 and an average ubRMSD of 0.048 cm3/cm3. A sensitivity analysis shows that CYGNSS reflectivity is likely responsible for most of the uncertainty in downscaled SMAP/CYGNSS soil moisture. The success of 3 km SMAP/CYGNSS soil moisture demonstrates that Global Navigation Satellite System–Reflectometry (GNSS-R) observations are effective for downscaling soil moisture. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm3/cm3), followed by winter values of 0.19 cm3/cm3. Subsequently, the minimum SSM values are observed during summer (0.11 cm3/cm3) and an increase in spring to 0.13 cm3/cm3. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, R = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management. [ABSTRACT FROM AUTHOR]

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In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation. [ABSTRACT FROM AUTHOR]

• Automatic selection of embedding dimension in Singular spectrum analysis is achieved. • Singular spectrum analysis yields the most stable and accurate sea level heights among the methods. • Singular spectrum analysis provides a better concentration of spectral power in the periodogram. In order to obtain reliable GNSS-R derived sea level heights (SLHs), it is crucial to extract accurate reflected signals from signal-to-noise ratio (SNR) data. In this study, Quadratic Fitting, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Singular Spectrum Analysis (SSA) are adopted for extracting the reflected signals from SNR data, which are then analyzed with Lomb Scargle Periodogram (LSP) assisted with tidal harmonic analysis for GNSS-R SLHs. Three continuous GNSS stations including Onsala in Sweden, Friday Harbor in the United States, and Brest in France with tidal ranges of approximately 1 m, 3 m, and 7 m, respectively, were used in the study. The derived SLHs were evaluated against the nearby or co-located tide gauge records. The results reveal that the three proposed algorithms effectively cope with the multi-peaks spectrum problems when using the conventional Quadratic Fitting without a priori reflected height constraint, and SSA can provide the solutions with fewer outliers among the proposed methods with the RMSEs of 6.7 cm, 13.4 cm and 39.6 cm at Onsala, Friday Harbor, and Brest stations, respectively. Without implementing tidal harmonic analysis, SSA is the method capable of acquiring reliable SLHs among the stations, as evidenced by comparing with co-located gauge records, particularly at the Brest station. In summary, SSA not only distinguishes signals across different frequencies, but also orders the signal components according to their eigenvalues, demonstrating the potential to extract the reflected signals from SNR and enhance the stability and accuracy of GNSS-R applications. [ABSTRACT FROM AUTHOR]

The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley‐RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single‐satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N–37.4°S). The sole reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS's indication of water existence. Berkeley‐RWAWC provides monthly, low‐latency inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water data sets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo‐Gangetic Plain. The comparisons reveal Berkeley‐RWAWC's enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley‐RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics. Plain Language Summary: The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley‐RWAWC) is a new data product developed to better monitor areas that are hard to observe due to thick vegetation and clouds, such as tropical regions. Using data from the Cyclone Global Navigation Satellite System (CYGNSS), an 8‐satellite constellation, Berkeley‐RWAWC has more frequent data collection compared to single‐satellite systems. This allows mapping of flooding or water accumulation with improved accuracy over time, even in clouds‐prone and overgrown areas. Berkeley‐RWAWC spans from 37.4° North to 37.4° South and consists of monthly inundation maps at approximately 1 km by 1 km resolution since August 2018. The method places the greatest emphasis on CYGNSS data indications of where is the water, making it different from others. In this paper, we explain how we made the maps, and compare them with other data sets in four different areas: the Amazon Basin, the Pantanal, the Sudd, and the Indo‐Gangetic Plain. Our comparisons show that Berkeley‐RWAWC is better at showing how water changes with the seasons, which is useful for understanding tropical wetland water cycles. Berkeley‐RWAWC is publicly available and can become an important new resource for studying our planet, especially in the study of patterns in tropical wetlands. Key Points: Vegetation and clouds can obstruct the view of waterbodies, making accurate, seasonal mapping difficultThis new CYGNSS‐based product combines L‐band microwaves with computer vision to produce quasi‐global monthly maps of waterbodiesThe product shows greater seasonal and interannual variability than other data sets for new insights into tropical hydrological processes [ABSTRACT FROM AUTHOR]

The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique. [ABSTRACT FROM AUTHOR]

GNSS-Reflectometry, a technique that harnesses the power of microwave remote sensing, is poised to revolutionize our ability to detect and monitor near-surface soil freeze/thaw processes. This technique's theoretical underpinnings are deeply rooted in the comprehensive explanation of the Zhang–Zhao dielectric constant model, which provides crucial insights into the behavior of frozen and thawed soils. The model elucidates how the dielectric properties of soil change as it transitions between frozen and thawed states, offering a scientific basis for understanding reflectivity variations. Furthermore, the theoretical framework includes a set of formulas that are instrumental in calculating reflectivity at Lower Right (LR) polarization and in deriving Dual-Polarization Differential Observables (DDMs). These calculations are pivotal for interpreting the signals captured by GNSS-R sensors, allowing for the detection of subtle changes in the soil's surface conditions. The evolution of GNSS-R as a tool for detecting freeze/thaw phenomena has been substantiated through qualitative analyses involving multiple satellite missions, such as SMAP-R, TDS-1, and CYGNSS. These analyses have provided empirical evidence of the technique's effectiveness, illustrating its capacity to capture the dynamics of soil freezing and thawing processes. In addition to these qualitative assessments, the application of a discriminant retrieval algorithm using data from CYGNSS and F3E GNOS-R has further solidified the technique's potential. This algorithm contributes to refining the accuracy of freeze/thaw detection by distinguishing between frozen and thawed soil states with greater precision. The deployment of space-borne GNSS-R for monitoring near-surface freeze/thaw cycles has yielded commendable results, exhibiting robust consistency and delivering relatively precise retrieval outcomes. These achievements stand as testaments to the technique's viability and its growing significance in the field of remote sensing. However, it is imperative to recognize and actively address certain limitations that have been highlighted in this review. These limitations serve as critical focal points for future research endeavors, directing the efforts toward enhancing the technique's overall performance and applicability. Addressing these challenges will be essential for leveraging the full potential of GNSS-R to advance our understanding and management of near-surface soil freeze/thaw processes. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the design of a dual-band and dual-polarized microstrip antenna for global navigation satellite systems reflectometry (GNSS-R) for the L1/E1/B1 and L5/E5a/B2a bands. In order to allow advanced GNSS-R signal processing and sensing techniques, the design has been carried out for dual-band and dual-polarization operation with isolated ports to receive both left and right-hand circular polarizations. The design procedure to allow receiving both bands independently and with high isolation is described in detail. Numerical and experimental results show that the antenna presents good performance in terms of impedance matching, circular polarization purity and isolation between the ports. The measured levels of axial ratio are 0.86 dB and 1.89 dB for the L1/E1/B1 and L5/E5a/B2a bands, respectively. The measured isolation levels are larger than 30 dB and 40 dB in the L1/E1/B1 and L5/E5a/B2a bands, hence proving that the proposed antenna concept can be properly used in dual-band dual-polarized GNSS-R applications.

For the past 20 years, Global Navigation Satellite System reflectometry (GNSS-R) technology has successfully shown its potential for remote sensing of the Earth's surface, including ocean and land surfaces. It is a multistatic radar that uses the GNSS signals reflected from the Earth's surface to extract land and ocean characteristics. Because of its numerous advantages such as low cost, multiple signal sources, and all-day/weather and high-spatiotemporal-resolution observations, this new technology has attracted the attention of many researchers. One of its most promising applications is GNSS-R ocean altimetry, which can complement existing techniques such as tide gauging and radar satellite altimetry. Since this technology for ocean altimetry was first proposed in 1993, increasing progress has been made including diverse methods for processing reflected signals (such as GNSS interferometric reflectometry, conventional GNSS-R, and interferometric GNSS-R), different instruments (such as an RHCP antenna with one geodetic receiver, a linearly polarized antenna, and a system of simultaneously used RHCP and LHCP antennas with a dedicated receiver), and different platform applications (such as ground-based, air-borne, or space-borne). The development of multi-mode and multi-frequency GNSS, especially for constructing the Chinese BeiDou Global Navigation Satellite System (BDS-3), has enabled more free signals to be used to further promote GNSS-R applications. The GNSS has evolved from its initial use of GPS L1 and L2 signals to include other GNSS bands and multi-GNSS signals. Using more advanced, multi-frequency, and multi-mode signals will bring new opportunities to develop GNSS-R technology. In this paper, studies of GNSS-R altimetry are reviewed from four perspectives: (1) classifications according to different data processing methods, (2) different platforms, (3) development of different receivers, and (4) our work. We overview the current status of GNSS-R altimetry and describe its fundamental principles, experiments, recent applications to ocean altimetry, and future directions. [ABSTRACT FROM AUTHOR]

Feature capture and recognition of sea wave components in radar systems especially in global navigation satellite system reflectometry (GNSS-R) using signal processing approaches or computer simulative methods has become a research hotspot in recent years. At the same time, parameter inversion of marine phenomena from the discovered characteristics plays a significant role in monitoring and forewarning the different components of sea waves. This paper aims to investigate the impact of multiple parameters, such as the wind speed, directionality variable, wave amplitude, wave length, and directions of sea wave components, on the delay waveform of the delay–Doppler map (DDM). Two types of wind waves and the 2-D sinusoidal sea surface are chosen to be analyzed. By comparing and analyzing the discrepancy of delay waveforms under different conditions, it can be concluded that the increased MSS which arises from the increase in the roughness of the sea surface can lead to the difference in the peak value or trial edges exhibited in delay waveforms. The values of delay waveforms at zero chip along the increasing direction of long-crest wind waves exhibit the periodic spikes shape, which is the opposite of the short-crest wind waves, and the fluctuation of the periodic profiles decreases with the increase in the amplitude of waves. The results and conclusions can provide a foundation for the parameter inversion, tracking, and early warning of anomalous formations of waves in bistatic radar configuration. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research. [ABSTRACT FROM AUTHOR]

GNSS Reflectometry (GNSS-R) is an emerging technique for the remote sensing of the environment. Traditional GNSS-R bio-geophysical parameter retrieval algorithms and deep learning models utilize observables derived from only the peak power of the delay-Doppler maps (DDMs), discarding the rest. This reduces the data available, which potentially hinders estimation accuracy. In addition, reflections from water bodies dominate the signal amplitude, and using only the peak power in those areas is challenging. Motivated by all the above, we propose a novel deep learning retrieval model for biomass estimation that uses the full DDM of surface reflectivity. Experiments using CYGNSS data have illustrated the improvements achieved when using the full DDM of surface reflectivity. Our proposed model was able to estimate biomass, trained using the ESA Climate Change Initiative (CCI) biomass map, outperforming the model that used peak reflectivity. Global and regional analysis is provided along with an illustration of how biomass estimation is achieved when using the full DDM around water bodies. GNSS-R could become an efficient method for biomass monitoring with fast revisit times. However, an elaborate calibration is necessary for the retrieval models, to associate GNSS-R data with bio-geophysical parameters on the ground. To achieve this, further developments with improved training data are required, as well as work using in situ validation data. Nevertheless, using GNSS-R and deep learning retrieval models has the potential to enable fast and persistent biomass monitoring and help us better understand our changing climate. [ABSTRACT FROM AUTHOR]

Deep soil moisture data have wide applications in fields such as engineering construction and agricultural production. Therefore, achieving the real-time monitoring of deep soil moisture is of significant importance. Current soil monitoring methods face challenges in conducting the large-scale, real-time monitoring of deep soil moisture. This paper innovatively proposes a real-time prediction approach to deep soil moisture combining GNSS-R data and a water movement model in unsaturated soil. This approach, built upon surface soil moisture data retrieved from GNSS-R signal inversion, integrates soil–water characteristics and soil moisture values at a depth of 1 m. By employing a deep soil moisture content prediction model, it provides predictions of soil moisture at depths from 0 to 1 m, thus realizing the large-scale, real-time dynamic monitoring of deep soil moisture. The proposed approach was validated in a study area in Goodwell, Texas County, Oklahoma, USA. Predicted values of soil moisture at a randomly selected location in the study area at depths of 0.1 m, 0.2 m, 0.5 m, and 1 m were compared with ground truth values for the period from 25 October to 19 November 2023. The results indicated that the relative error (δ) was controlled within the range of ±14%. The mean square error (MSE) ranged from 2.90 × 10 − 5 to 1.88 × 10 − 4 , and the coefficient of determination ( R 2 ) ranged from 82.45% to 89.88%, indicating an overall high level of fitting between the predicted values and ground truth data. This validates the feasibility of the proposed approach, which has the potential to play a crucial role in agricultural production, geological disaster management, engineering construction, and heritage site preservation. [ABSTRACT FROM AUTHOR]

The persistence of inland water bodies affects climate, biodiversity, and human societies. Among the multiple remote sensing methods, Global Navigation Satellite System Reflectometry (GNSS-R) technology has shown great potential for inland water bodies mapping at very high temporal resolutions. For large inland water bodies with dimensions larger than 350 km, long fetches would roughen water surfaces, thereby causing low-power GNSS returns, posing challenges for mapping them using GNSS-R data, particularly in the case of ultra-large lakes with complicated shorelines, such as Lake Victoria. In this study, we propose an algorithm for mapping Lake Victoria at a 0.01° × 0.01° spatial resolution using GNSS-R data from the Cyclone Global Navigation Satellite System (CYGNSS). By mainly leveraging the surface reflectivity signal, our algorithm extracts lake boundaries and fills the interior to map Lake Victoria. The probability of detection (POD) of the resultant water mask was approximately 90%. This simple and robust algorithm could enhance the capability of monitoring global fast-changing inland water bodies using GNSS-R data, especially in the pan-tropical areas. [ABSTRACT FROM AUTHOR]

A parametric error model is developed to represent the uncertainty in retrieval of hurricane force wind speed by a spaceborne GNSS-R instrument. The functional form of the model is constructed based on a bottom–up consideration of the primary contributing sources of uncertainty. Scaling parameters in the model are tuned in a top–down manner using a large population of wind speed retrievals by the CYGNSS satellite, which are colocated in space and time with HWRF reanalysis hurricane winds in the North Atlantic during 2018–2022. The root-mean-squared difference between CYGNSS and HWRF winds is found to depend on a number of variables, two of which are wind speed and receive antenna gain. The parametrized error model represents these dependencies. The model can be used as a design tool to predict expected performance as a function of instrument design. In particular, the model predicts the antenna gain required to achieve a particular level of wind speed uncertainty at a particular wind speed. A case study is considered in which a receive antenna gain of at least 20 dBi is found to be required to reliably distinguish between a Category 4 and Category 5 hurricane. This has implications for the optimal design of a future GNSS-R instrument intended for hurricane observations.

Global navigation satellite system-reflectometry (GNSS-R) is a kind of promising remote sensing technique, which utilizes the reflected signals of GNSS for geophysical parameters detection. Its applications on ocean surface are earlier and more mature. However, in recent years, with the development of spaceborne satellite exploration programs such as TechDemoSat-1, cyclone GNSS (CYGNSS) and Fengyun 3E,its advantages and potential in land remote sensing research have gradually emerged. The research work of the existing mechanism model is summarized. Meanwhile, this paper reviews the research status of spaceborne GNSS-R and other signal of opportunity reflection remote sensing (SoOP-R). GNSS-R's applications on soil moisture, vegetation, soil freeze-thaw monitoring are focused on, while the research status of the latest SoOP-R technology in root zone soil moisture and snow water equivalent is also summarized. In this way, we hope to promote the development of this technology in the detection of major climatic and meteorological parameters for the hydrological cycle to a certain extent. [ABSTRACT FROM AUTHOR]

A parametric error model is developed to represent the uncertainty in retrieval of hurricane force wind speed by a spaceborne GNSS-R instrument. The functional form of the model is constructed based on a bottom–up consideration of the primary contributing sources of uncertainty. Scaling parameters in the model are tuned in a top–down manner using a large population of wind speed retrievals by the CYGNSS satellite, which are colocated in space and time with HWRF reanalysis hurricane winds in the North Atlantic during 2018–2022. The root-mean-squared difference between CYGNSS and HWRF winds is found to depend on a number of variables, two of which are wind speed and receive antenna gain. The parametrized error model represents these dependencies. The model can be used as a design tool to predict expected performance as a function of instrument design. In particular, the model predicts the antenna gain required to achieve a particular level of wind speed uncertainty at a particular wind speed. A case study is considered in which a receive antenna gain of at least 20 dBi is found to be required to reliably distinguish between a Category 4 and Category 5 hurricane. This has implications for the optimal design of a future GNSS-R instrument intended for hurricane observations. [ABSTRACT FROM AUTHOR]

This paper studies a new concept of using global navigation satellite system (GNSS) signals coherently reflected over relatively smooth ocean and ice surfaces from very low elevation angles (below ∼8°) and received by low Earth orbit (LEO) satellites to retrieve the tropospheric information. This approach can provide horizontal profiles of tropospheric zenith delay and total column water vapor (TCWV) with centimeter‐level high precision and spatial resolutions of tens of km by ∼1 km, depending on the elevation angle, with a sampling spacing of ∼100 m. This approach can potentially be applied to most sea ice and calm ocean areas and provide tropospheric sensing data, which can complement and augment existing observation systems. A few case studies are conducted in this paper using the Spire grazing‐angle GNSS‐R data. The retrieved TCWV is compared to ERA5 products and the Sentinel‐3 Ocean and Land Color Instrument measurements and shows promising performances. The errors associated with the GNSS‐R tropospheric measurements are also discussed. Plain Language Summary: The atmospheric water vapor is an important component for the weather and climate systems and is difficult to measure, especially over ocean and ice surfaces. This paper studies a new approach to measuring atmospheric water vapor using global navigation satellite system (GNSS) signals reflected off ocean and ice surfaces. If the reflection is from a low elevation angle (below ∼8°) and the reflected signal is coherent (all signal rays are reflected in the same direction), this approach can provide very high precision observation of the horizontal gradients of the tropospheric delay and the vertically integrated atmospheric water vapor with good spatial resolutions. This paper presents the methodology of the proposed approach and a few case studies to demonstrate the feasibility and performance by comparing the GNSS‐R retrieved water vapor measurements with models and the Sentinel‐3 satellite radiometry measurements. The errors associated with the GNSS Reflectometry (GNSS‐R) tropospheric measurements are also discussed. Key Points: A new tropospheric sensing concept is studied that relies on coherent‐reflection global navigation satellite system (GNSS) signals off ocean and ice surfacesAlgorithms are developed and demonstrated using Spire grazing‐angle GNSS‐R data to retrieve tropospheric delay and water vaporThe presented approach provides high‐precision tropospheric delay and total column water vapor horizontal profiles, as validated using the Sentinel‐3 Ocean and Land Color Instrument data [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the Chinese Fengyun-3E (FY-3E) satellite is the world's first operational spaceborne mission that can utilize reflected signals from multiple navigation systems for Earth remote sensing. The satellite was launched into an 836-km early-morning polar orbit on 5 July 2021. Different GNSS signals show different characteristics in the observations and thus require different calibration methods. With an average data latency of less than 3 h, many near real-time applications are possible. This article first introduces the FY-3E/GNOS-II mission and instrument design, then describes the extensive calibration methods for the multi-GNSS measurements, and finally presents application results in the remote sensing of ocean surface winds, land soil moisture and sea ice extent. Especially, the ocean surface wind product has been used in operational applications such as assimilation in the numerical weather prediction model and monitoring of tropical cyclones. Currently, GNOS-II has been carried by FY-3E, FY-3F (launched in August 2023) and FY-3G (launched in April 2023). It will be also carried by future follow-on FY series and a more complete multi-GNSS reflectometry constellation will be established. [ABSTRACT FROM AUTHOR]

With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals' waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a pivotal tool with different land applications, prominently encompassing soil moisture estimation. In contrast to conventional radiometer satellites commonly used for this purpose, GNSS-R offers higher spatiotemporal coverage while maintaining cost-effectiveness. The potential of using Global Positioning System (GPS) reflections measured by the Cyclone Global Navigation Satellite System (CYGNSS) mission to retrieve soil moisture has been previously demonstrated. In 2019, Spire Global Inc. launched their first GNSS-R satellites, which now comprise a constellation of four CubeSats. These satellites track reflections from multi-constellation, encompassing GPS, Galileo, BeiDou Navigation Satellite System (BDS), and Quasi-Zenith Satellite System (QZSS). In this study, an analysis and validation of Spire GNSS-R L1B surface reflectivity for soil moisture retrieval within east Australia during an eight-month period in 2021 is presented. A comparison of the estimated Spire surface reflectivity to that of CYGNSS is performed, unveiling analogous behavioural patterns and biases across both missions. Soil moisture is estimated using observations from Spire GPS-only, Spire multi-constellation, and CYGNSS. The Soil Moisture Active and Passive (SMAP) retrievals are used as the reference, presuming a linear relationship between changes in soil moisture and changes in reflectivity. Our results indicate that the Spire GNSS-R mission can detect variations in soil moisture with a performance comparable to that of CYGNSS. A median unbiased root-mean-square difference (ubRMSD) of 0.062 m³.m-3 is found for both Spire GPS and multi-constellation when using 9-km products and SMAP as the reference. [ABSTRACT FROM AUTHOR]

The key to retrieving soil moisture (SM) with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data is to correct the effects of soil surface temperature (SST), roughness, water, and vegetation. In this study, Cyclone Global Navigation Satellite System (CYGNSS) data are used to calculate surface reflectivity and surface roughness is characterized based on the statistical moments of Delay-Doppler Maps (DDM). After removing open water, a multiple linear regression model is created to retrieve SM by combining the SST and vegetation optical depth (VOD) parameters provided by Soil Moisture Active Passive (SMAP) data. With a correlation coefficient of 0.815 and a root-mean-square error of 0.066 cm3cm−3, an experimental analysis reveals decent consistency between the CYGNSS SM and the referenced SMAP SM. In addition, a time-series analysis between the CYGNSS SM and the International Soil Moisture Network (ISMN) referenced SM data shows a good correlation. Since surface reflectivity is significantly affected by water and because there is a coupling relationship between SST and SM, the differences of CYGNSS SM when the two factors are simultaneously considered or ignored are also analyzed. The experimental results show that after removing water and incorporating SST into the linear regression model, the accuracy of CYGNSS SM has been improved significantly, with the root mean square error, mean absolute error, and Bias increasing by 6%, 7%, and 11.3%, respectively. This study demonstrates the necessity of considering water and SST in SM retrieval and provides a novel approach for SM retrieval using high accuracy and high spatial and temporal resolution data. [ABSTRACT FROM AUTHOR]

In order to improve the problem of poor signal separation and large deviation in the process of sea level height inversion, this paper proposes a new method of sea level height inversion based on Gaussian multipeak fitting + sliding window, which can obtain high-quality signal-to-noise ratio sequence. The SNR (signal to noise ratio) of BDS (Beidou Navigation Satellite System) satellite B2, B6 and B7 bands at MAYG station was selected to invert the sea level height by Gaussian multi-peak fitting + sliding window method, and the inversion results of polynomial and wavelet filtering methods and tide station data were compared and verified. The results show that the accuracy of Gaussian fitting inversion results is 29 % and 19 % higher than that of polynomial and wavelet filtering methods, respectively, and the correlation is better. After combining the sliding window, the effective point of the inversion result is increased to 4.5 times before the windowing, and the time resolution is greatly improved. Compared with the single-frequency data inversion results, the effective point of the joint multi-frequency data inversion results is increased by more than 3 times, and the resolution is further improved while ensuring the accuracy. The results show that the GNSS-R(global navigation satellite system reflectometry) sea level height inversion method combining Gaussian fitting and sliding window can better reflect the sea level height. [ABSTRACT FROM AUTHOR]

Extreme hydrological events such as tsunamis, high tides, or storm surges seriously threaten coastal communities. These events result in flooding, property damage, loss of life, and long-term economic and social impacts. Therefore, monitoring and detecting extreme hydrological events significantly affect coastal areas in disaster response efforts. However, the cost of installing and maintaining these stations can be a significant challenge for developing countries. The objective of this study is to use a low-cost GNSS receiver to monitor tides and detect extreme coastal hydrological phenomena by analyzing changes in water level, using analysis of the signal-to-noise ratio (SNR) data. Data used in this study were collected from a GNSS station located in the Tam Giang Lagoon area, Thua Thien Hue, Vietnam, from September to October 2022. The water level based on GNSS-R is compared with the sensor's measured water level, with the Pearson correlation coefficient reaching 0.96 and RMSE of 0.08m. Continuous Wavelet Transform analysis demonstrated the relationship between water levels and extreme hydrological events. The results show that distinct signatures in the data correspond to the Noru typhoon from September 27-29, 2022, and the inundation from October 14-19, 2022, in Thua Thien Hue. This information is the basis for forecasting and early warning of extreme events and informing disaster response and management efforts. [ABSTRACT FROM AUTHOR]

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique. [ABSTRACT FROM AUTHOR]

The soil moisture from the South-to-North Water Diversion Middle Route Project is assessed in this study. Complex and variable geological conditions complicate the prediction of soil moisture in the study area. To achieve this aim, we carried out research on soil moisture inversion methods for channel slopes in the study area using massive monitoring data from multiple GNSS observatories on channel slopes, incorporating GNSS-R techniques and deep learning algorithms. To address the issue of low accuracy in linear inversion when using a single satellite, this study proposes a multi-satellite and multi-frequency data fusion technique. Furthermore, three soil moisture inversion models, namely, the linear model, BP neural network model, and GA-BP neural network model, are established by incorporating deep learning techniques. In comparison with single-satellite data inversion, with the data fusion technique proposed in this study, the correlation is improved by 12.7%, the root mean square error is reduced by 0.217, the mean square error is decreased by 0.884, and the mean absolute error is decreased by 0.243 with the linear model. With the BP neural network model, the correlation is increased by 15.4%, the root mean square error is decreased by 0.395, the mean square error is decreased by 0.465, and the mean absolute error is reduced by 0.353. Moreover, with the GA-BP neural network model, the correlation is improved by 6.3%, the root mean square error is decreased by 1.207, the mean square error is decreased by 0.196, and the mean absolute error is reduced by 0.155. The results indicate that performing data fusion by using multiple satellites and multi-frequency bands is a feasible approach for improving the accuracy of soil moisture inversion. These research findings provide new technical means for the risk analysis of deformation disasters in the expansive soil channel slopes of the South-to-North Water Diversion Middle Route Project. [ABSTRACT FROM AUTHOR]

ABSTRACTWith population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

During the past three decades, Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) has become a promising remote sensing technique. In developing GNSS-R, several spaceborne missions have been launched, e.g., the UK's TDS-1, NASA's CYGNSS, and China's BuFeng-1A/B and FY-3E GNOS-R satellites.The application of GNSS-R to vegetation monitoring has attracted attention because GNSS-R signals have less saturation effect than those of a traditional monostatic radar. However, most previous related work has concentrated on the analysis of experimental data, with little attention given to the physical models or polarization. We present a spaceborne vegetation simulator for full polarization GNSS-R, i.e., the LAGRS-Veg (Land surface GNSS-R simulator-Vegetation) model. This model is based on the vegetation radiative transfer equation model and integrated with the GNSS scattering model. Thus, it provides a comprehensive end-to-end simulator for spaceborne GNSS-R study of vegetation. Using this simulator, the impact of vegetation water content, biomass, soil moisture, and surface roughness on delay Doppler map measurements can be thoroughly analyzed because they are all based on physical scattering mechanisms. The effects of observation geometry and different polarizations can all be included and analyzed using LAGRS-Veg. The presented work will benefit spaceborne data simulation, vegetation parameter retrievals, and future spaceborne sensor design. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze global navigation satellite system reflectometry (GNSS-R) data variations as a function of land cover using airborne measurements obtained with the global navigation satellite system reflectometry instrument (GLORI), which is a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil and vegetation properties were then obtained simultaneously with flight measurements. The behavior of the observable copolarization [right–right (RR)] reflectivity $\boldsymbol {\Gamma }_{ \boldsymbol {RR}}$ and the cross-polarization [right–left (RL)] reflectivity $\boldsymbol {\Gamma }_{ \boldsymbol {RL}}$ as a function of land use is discussed. The distribution of coherent and incoherent components in the reflected power is estimated for different types of land cover. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]