Search Results (109)

Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m³/m³, RMSE = 0.14 m³/m³). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m³/m³) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology. Full article

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions. Full article

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. Full article

Flood disasters are among the most severe natural calamities worldwide and typically occur in densely populated areas with abundant lakes and high rainfall. These disasters cause significant damage to the environment and human settlements. Therefore, accurately monitoring and understanding the occurrence and evolution of floods, as well as studying the influencing factors, is of great importance. This study employs CYGNSS satellite data from a constellation of small satellites equipped with reflective radar, which observe the Earth’s surface with high spatial and temporal resolution. Such systems effectively monitor the distribution of water bodies and hydrological processes on land surfaces. By collecting and analyzing CYGNSS data, we can map the distribution of water bodies during flood events to assess the extent and severity of the flooding. Additionally, this study examines various factors influencing flooding, including rainfall, land use, and topography. By compiling relevant meteorological, geographical, and hydrological data, we aim to develop a model that elucidates the impacts of these factors on the initiation and progression of floods. Ultimately, this research offers a comprehensive analysis based on CYGNSS data for monitoring floods and their influencing factors. The goal is to yield significant insights and explore the potential of using CYGNSS data in flood monitoring efforts. In the context of global climate change and the increasing frequency of flood disasters, these findings are expected to provide a crucial scientific basis for improving flood prevention and management strategies, thereby helping to mitigate losses and enhance our warning and disaster response capabilities. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a valuable tool for soil moisture (SM) and biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R for vegetation monitoring, the utilization of this technology to fuse multiple RS parameters to retrieve VWC is not yet mature. For this purpose, this paper constructs a local high-spatiotemporal-resolution spaceborne GNSS-R VWC retrieval model that integrates key information, such as bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, and surface auxiliary parameters based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM)). We extensively tested the performance of different models using SMAP ancillary data as validation data, and the results show that the root mean square errors (RMSEs) of the BT, XGBoost, RF, and LightGBM models in VWC retrieval are better than 0.50 kg/m². Among them, the BT and RF models performed the best in localized VWC retrieval, with RMSE values of 0.50 kg/m². Conversely, the XGBoost model exhibits the worst performance, with an RMSE of 0.85 kg/m². In terms of RMSE, the RF model demonstrates improvements of 70.00%, 52.00%, and 32.00% over the XGBoost, LightGBM, and GBDT models, respectively. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article