Cyclone Global Navigation Satellite System Research Articles

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Enhancing Algal Bloom Level Monitoring with CYGNSS and Sentinel-3 Data

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.

CY GNSS significant wave height inversion model based on multivariate machine learning

The Cyclone Global Navigation Satellite System (CYGNSS) provides high-quality Global Navigation Satellite System Reflectometry (GNSS-R) data, which can be reliably used for the inversion of Significant Wave Height (SWH). Due to the high dynamics of CYGNSS, the received signal is easily affected by environmental factors, and the complexity of the sea conditions makes it difficult for simple models to accurately invert SWH. In order to solve the above problems, this paper proposes a multivariate SWH inversion model based on machine learning. According to the formation mechanism of waves and the correlation analysis between CYGNSS parameters and SWH, relevant parameters are selected, and three training schemes of 5 parameters, 9 parameters and 17 parameters are designed. Subsequently, the inversion model was trained and validated using Random Forest (RF) and Convolutional Neural Network (CNN), and the SWH inversion results were compared with the reference values of the European Centre for Medium-range Weather Forecasts (ECMWF). The best inversion model was the 17-parameter CNN inversion model with an RMSE of = 0.1840 m.

Machine Learning-Based Soil Moisture Retrieval Using CYGNSS and Interpolated SMAP Data

Abstract. Soil moisture is a crucial component of the global terrestrial ecosystem water vapor cycle, and higher spatial-temporal soil moisture product has significant impacts on research in agriculture, hydrology, and ecology. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology which can be used to retrieve soil moisture. Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA in 2016 to observe ocean surface hurricanes, has also collected a substantial amount of data of GNSS signals reflected over the land. This paper focuses on soil moisture retrieval using CYGNSS and SMAP dataset. In this study, three machine learning methods (GSVM, BP and RF) are used for spatial interpolation of SMAP soil moisture data. Result shows RF interpolation is the best with the RMSE of 0.044 cm3/cm3 and the CC of 0.959 compared with SMAP truly SM. Then soil moisture data after RF interpolation and conventional linear interpolation are respectively used as response variables to train the retrieval model by ELM, BP and RF machine learning methods respectively. Results show that, among the six regression models, the RF-RF regression model performs the best with the RMSE of 0.012 cm3/cm3 and the CC of 0.986. Using this model, we get a global soil moisture product and have higher temporal and spatial resolution.

A novel global grid model for soil moisture retrieval considering geographical disparity in spaceborne GNSS-R

Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture (SM) retrieval. However, the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface. Introducing redundant ancillary data may also result in over-reliance problems. Therefore, we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS (CYGNSS) observations and Soil Moisture Active and Passive (SMAP) product. Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP, we establish five models for each grid with different parameters to achieve global SM retrieval. Subsequently, an optimal model, determined by the performance indicator, is used for SM retrieval. The results show that the root mean square error SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} with the improved method is decreased by 9.1% using SMAP SM as reference with the SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} = 0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method. Additionally, using the in-situ SM of International Soil Moisture Network as reference, the overall correlation coefficient R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} and SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} values with the improved method are 0.80 and 0.064 cm3/cm3, respectively. The average R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} of the chosen sites is increased by 22.7%, and the average SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} is decreased by 8.7%. The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.

An improved soil moisture retrieval method considering azimuth angle changes for spaceborne GNSS-R

The soil moisture (SM) retrieval of spaceborne GNSS-R is significantly affected by azimuth angle. In this paper, the K-means algorithm for clustering is used to weaken the effects caused by azimuth angle changes on spaceborne GNSS-R reflectivity. The matched Cyclone Global Navigation Satellite System (CYGNSS) and Soil Moisture Active and Passive (SMAP) observables are categorized differently using the K-means algorithm, and subsequently employed in SM retrieval based on the Reflectivity-Temperature-Vegetation (R-T-V) model. The SM retrievals are verified with the SMAP as well as the International Soil Moisture Network (ISMN). In the verification with SMAP, the results show that, compared with traditional method, the root-mean-square error (RMSE) is 0.040 cm3/cm3 with a reduction of 9.09 % and the correlation coefficient (R) is 0.946. In the verification with ISMN, the results demonstrate improvement in both RMSE and R at the selected sites compared with the traditional method. Specifically, RMSE is decreased from 0.086 cm3/cm3 to 0.054 cm3/cm3, while R is increased from 0.316 to 0.840 at Chalender. Therefore, the improved method can further strengthen the ability of spaceborne GNSS-R for SM retrieval by weakening the effects of azimuth angle changes.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation

Global navigation satellite system reflectometry (GNSS-R) has emerged as a new technique to provide L-band bistatic measurements for ocean wind speed retrieval, in which traditional geophysical model functions (GMFs) or shallow neural networks (NNs) are normally used. However, it is still challenging to identify and consider all relevant parameters in the GMF. Meanwhile, NN models face limitations due to the degradation problem, which restricts their depth and consequently their performance. Furthermore, the interpretation of NN models for GNSS-R wind retrieval is another issue. To this end, we propose a residual fully connected network (RFCN) fusing auxiliary information such as geometry, receiver gain, significant wave height, and current speed with track-wise corrected σ0. Referred to the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5 wind product, the root mean square error (RMSE) and bias of RFCN winds are 1.031 m/s and -0.0003 m/s, respectively, with a 6% improvement in RMSE compared to debiased NOAA Cyclone Global Navigation Satellite System (CYGNSS) Version 1.2 (V1.2) wind speed retrieval. Moreover, in an intertropical convergence zone (ITCZ) area with large current speeds, the RMSE and bias are 1.006 m/s and -0.022 m/s: an improvement of 11.6% and 87.9% compared to debiased NOAA CYGNSS V1.2 winds. The bias ‘strips’ in these areas are nearly eliminated. Daily averaged error analyses also demonstrate that RFCN winds are more robust and consistent with ECMWF winds. For wind speeds larger than 20 m/s, referred to Soil Moisture Active Passive (SMAP) Level 3 final wind products, the RMSE and bias of fine-tuning RFCN (FT_RFCN) winds are reduced by 25.7% and 91.5% compared to NOAA winds. Finally, the RMSE and bias of retrievals in tropical cyclones, measured by Stepped Frequency Microwave Radiometer (SFMR) during 2021-2022, reveal an improvement of 3.5% and 21.2% compared to NOAA winds. Through SHapley Additive exPlanations (SHAP) models developed for RFCN and FT_RFCN, the contribution of each feature is quantitatively evaluated, while providing insights into their interactions within the ‘black-box’ NN models with clear physical meanings.

Bistatic radar cross section estimation by using DDM for better spatial resolution of ocean surface wind speed

The delay-Doppler map (DDM) is the signal power distribution of the Coarse/Acquisition code (C/A code) of the received Global Navigation Satellite System (GNSS) signal in different code phase delay and Doppler frequency. When the received signal is reflected from ocean surface, the DDM can be used to retrieve the ocean surface wind speed, which is the GNSS-reflectometry (GNSS-R) technique. Due to the signal power distribution in DDM is the correlation results of received and artificial C/A code from receiver in different code phase delay and Doppler frequency, the Woodward ambiguity function (WAF) occurs in the DDM. In the case of DDM, the WAF is the correlation results of two square waves in different code phase delay and Doppler frequency, and is approximated a triangular function and a sinc function in code phase delay and Doppler frequency axes, respectively. It means the correlation results not only show the code phase delay and Doppler frequency of the received signal but also influence the surrounding code phase delay and Doppler frequency values and cause the structure of DDM more complex. Using more bins in DDM in the wind speed retrieval process can reduce the influence of WAF but cause the spatial resolution to worsen. In order to use as less bins as possible in the retrieval process and not reduce the retrieval efficiency too much, a simple method to estimate bistatic radar cross section (BRCS) from DDM is developed in this study. Furthermore, a retrieval process is also developed for ocean surface wind speed retrieving by using less bins in the DDM from Cyclone Global Navigation Satellite System (CYGNSS) mission.

Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination

Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

ABSTRACT The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Berkeley‐RWAWC: A New CYGNSS‐Based Watermask Unveils Unique Observations of Seasonal Dynamics in the Tropics

AbstractThe 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.

Advancing Sea Surface Height Retrieval through Global Navigation Satellite System Reflectometry: A Model Interaction Approach with Cyclone Global Navigation Satellite System and FengYun-3E Measurements

The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion.

Polarized Bidirectional Reflectance Distribution Function Matrix Derived from Two-Scale Roughness Theory and Its Applications in Active Remote Sensing

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals.

Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error.

High temporal resolution quasi-global landscape soil freeze–thaw map from spaceborne GNSS-R technology and SMAP radiometer measurements

Landscape freeze–thaw (F/T) state parameters are an integral part of research on terrestrial hydrological processes, vegetation growth dynamics, and terrestrial–atmospheric trace gas exchange. Therefore, the development of large-scale, continuous, and rapid F/T observation records is essential. This study proposes a scheme to effectively retrieve quasi-global daily soil F/T states from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations. This method takes into account the GNSS-R surface reflectivity and its influence factor. Specifically, the method employed surface reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) data from 2018 to 2020, along with vegetation opacity data from Soil Moisture Active Passive (SMAP) as input layers in random forests for F/T retrieval. This approach provides F/T results on a quasi-global scale, does not rely on continuous F/T observations, and employs the least ancillary data. To validate the retrieval results, SMAP F/T data over the same period and temperature data from more than 100 ground stations were also used as reference. Results showed that the F/T retrieval accuracy of CYGNSS was 95.5% and 82.0% with respect to the SMAP data and data from independent in situ stations, respectively. Given the high temporal coverage of the CYGNSS F/T results, the temporal coverage improvement is 23.3% compared with the actual SMAP observations on a daily scale. Furthermore, as gap-filling data complemented SMAP, CYGNSS data can effectively improve the SMAP F/T sample interval (accuracy improvement of 26.0%). More importantly, the F/T accuracy of the CYGNSS and SMAP combination based on monthly performance is 85.1%, i.e., 20.5% better than that of the existing SMAP daily F/T product. Overall, the quasi-global daily F/T results obtained from CYGNSS and SMAP measurements can complement existing databases with well-defined accuracy.

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.

Multiresolution soil moisture products based on a spatially adaptive estimation model and CYGNSS data

ABSTRACT The estimation of soil moisture (SM) utilizing the data from the Cyclone Global Navigation Satellite System (CYGNSS) has attracted significant interest in recent times. However, CYGNSS’ inherent capability of variable resolution has not been fully exploited, often resulting in a loss of detailed spatial information in the raw data. In this paper, a novel downscaling scheme tailored for CYGNSS data is introduced to yield a “self-adjusting adaptive resolution” SM product, which dynamically varies the resolution of SM estimates based on the available CYGNSS data resolution at different geographic locations. Initially, a direct quantitative relationship is established between the key CYGNSS parameters reflecting SM variations and the reference SM from the Soil Moisture Active Passive (SMAP) mission with a coarse resolution of 36 km. This model is then applied to CYGNSS observations with resolutions down to 3 km to generate high-resolution, self-adjusting SM estimates that better conserve the fine-scale information linked to the original CYGNSS data. Extensive experimental results with error ratio diagrams show that the advanced geographically weighted regression (GWR)-based SM estimation method outperforms other competing estimation models and better retains localized spatial relationships and patterns. This study underscores the potential of CYGNSS as a novel and robust independent data source capable of delivering fine-resolution SM estimations by harnessing its unique multiresolution observational capability.

An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry

All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS reflectometry (GNSS-R) using observations from NASA’s Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites and double-difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalog for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.

A new algorithm for mapping large inland water bodies using CYGNSS

ABSTRACT 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.