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Using delay‐Doppler maps of Global Navigation Satellite Systems Reflectometry (GNSS‐R) from the TechDemoSat‐1 satellite and considering sea ice and ocean interaction, an innovative method for retrieval of Arctic sea ice concentration (SIC) based on a deep neural network is proposed. This retrieval method shows the potential of future GNSS‐R applications for Arctic missions. Compared with SIC products from Hamburg University, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS‐R SIC data are added into the assimilation as supplementary passive microwave remote‐sensing data, it has a positive influence on improving the accuracy of the Arctic SIC forecast. Especially in some edge regions of sea ice, when compared to only assimilating the remote‐sensing data, the regional RMSE of joint assimilation has a maximum decrease of approximately 17% in the 24‐hr forecast time, and over 5% in 72‐hr. Plain Language Summary Accurate sea ice forecasting is critical to understanding the risks of Arctic maritime activity and to improving climate forecasting in the mid‐high latitudes of the Northern Hemisphere. Data assimilation of sea ice observations is an effective way to improve the numerical model forecast results, and its effect is related to both the quality and the quantity of observations. As a new remote sensing technology, Global Navigation Satellite Systems Reflectometry (GNSS‐R) shows great potential in sea ice remote sensing. Based on GNSS‐R data from TechDemoSat‐1, we combined Delay‐doppler number eigenvalue method and deep neural network to propose an innovative method for estimating sea ice concentration (SIC) at GNSS‐R subsatellite points. The estimated SIC has reasonable accuracy and provides more information at subsatellite points near the grid points of the passive microwave remote sensing product. When it is joint assimilated with passive microwave remote sensing SIC, it has a positive influence on the forecast effect of SIC in the region with rapid change of sea ice. This is the first time that GNSS‐R data has been applied to the prediction of Arctic SIC, which is of great value in promoting the application and development of GNSS‐R in Arctic sea ice forecast. Key Points An innovative method for retrieving sea ice concentration (SIC) at Global Navigation Satellite Systems Reflectometry (GNSS‐R) subsatellite points is presented, considering the marine factors The joint assimilation of retrieved GNSS‐R SIC and passive microwave remote sensing SIC is a useful means to improve the forecast accuracy More GNSS‐R SIC data in areas with large SIC gradient will bring more useful information to the sea ice forecast

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been the research focus of Earth observation because of its unique advantages; however, there are still many challenges to be resolved. The reduction of the impact of the satellite motion on the GNSS-R waveform is the one of key technologies for spaceborne GNSS-R. The proposed delay retracking methods in existing literatures require too many instrument resources and too much priori information to refresh correlation window on each coherent integration time period. This paper aims to propose an on-ground alternative in which less frequency tracking refresh on board is needed. The model of dynamic delay waveform, which is expressed as the convolution of the pure waveform and the point spread function, are described. Based on this, the new methodology, which utilizes the least squares fitting to make the residual error between the dynamic model and measured waveform minimum, is employed to reconstruct the pure waveform. The validity of proposed method is verified using UK-DMC, UK-TDS-1 and simulated data. Moreover, the performances of sea surface height and wind speed retrieval using retracked and non-retracked waveforms are compared. The results show that (1) the MSEs between aligned and retracked waveform reduce to 0.026 and 0.044 from 0.110 and 0.156 between aligned and non-retracked waveform with the TRP of 1 s and 3 s for UK-DMC data, and for UK-TDS-1 data, the MSEs decrease from 161.02 and 227.34 to 70.10 and 61.80; (2) the standard deviation of sea surface height using retracked waveform is lower 5 times than the one using non-retracked waveform; (3) the retracked waveform could lead to a better measurement performance in wind speed retrieval. Finally, the relationship between the performance of retracking and Signal-to-Noise Ratio (SNR) is analyzed. The results show that when the SNR of the waveform is lower than 3 dB, the retrieval accuracies rapidly become worse.

As an important component of the cryosphere, lake ice plays an important role in regulating regional climate and maintaining the balance of lake ecosystems. Lake ice thickness is one of the core parameters to study the dynamic processes of lake ice. However, there are relatively few studies on lake ice thickness estimation based on spaceborne GNSS reflected signals. Therefore, this paper presents a method for global lake ice thickness estimation using Multi-GNSS reflected signals from the Tianmu-1 constellation. By combining multi-system GNSS-R data (BDS-R/GPS-R/GLONASS-R/Galileo-R) with the ERA5 dataset, we adopt a hybrid deep learning framework combining convolutional neural network (CNN) and Bi-directional long short-term memory network (BiLSTM) for retrieving lake ice thickness. The experimental results show that the method has high accuracy, with the best performance of the BDS system for retrieving lake ice thickness (RMSE: 0.137m, CC: 0.95). Index Terms —Global Navigation Satellite System-Reflectometry (GNSS-R); Tianmu-1; Lake ice thickness.

Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.

Knowledge of sea ice is critical for offshore oil and gas exploration, global shipping industries, and climate change studies. During recent decades, Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an efficient tool for sea ice remote sensing. In particular, thanks to the availability of the TechDemoSat-1 (TDS-1) data over high-latitude regions, remote sensing of sea ice based on spaceborne GNSS-R has been rapidly growing. The goal of this paper is to provide a review of the state-of-the-art methods for sea ice remote sensing offered by the GNSS-R technique. In this review, the fundamentals of these applications are described, and their performances are evaluated. Specifically, recent progress in sea ice sensing using TDS-1 data is highlighted including sea ice detection, sea ice concentration estimation, sea ice type classification, sea ice thickness retrieval, and sea ice altimetry. In addition, studies of sea ice sensing using airborne and ground-based data are also noted. Lastly, applications based on various platforms along with remaining challenges are summarized and possible future trends are explored. In this review, concepts, research methods, and experimental techniques of GNSS-R-based sea ice sensing are delivered, and this can benefit the scientific community by providing insights into this topic to further advance this field or transfer the relevant knowledge and practice to other studies.

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.

Fengyun-3G (FY-3G) is one of the satellites in the Fengyun series, equipped with a Global Navigation Satellite System-Reflectometry (GNSS-R) payload. FY-3G has been operating successfully for several months and has accumulated sufficient observations. To explore the land remote sensing potential of FY-3G GNSS-R observations, this study conducted surface soil moisture (SM) and freeze-thaw (F/T) state retrievals using FY-3G GNSS-R observations. The results show that the overall root mean square error (RMSE) and correlation (R) of the FY-3G SM retrievals compared to the contemporaneous Soil Moisture Active Passive (SMAP) SM are 0.056 [Formula: see text]/[Formula: see text] and 0.91, respectively. The accuracy of the FY-3G F/T retrievals is 84.3%. In addition, independent stations similarly demonstrated the good performance of FY-3G observations for SM and F/T retrievals and showed that FY-3G and SMAP have comparable monitoring capabilities. This study successfully demonstrates the capability of FY-3G in SM and F/T monitoring. This will enlarge the range of the application of the Fengyun satellite for monitoring land parameters in the future.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data.

The continuous evolution of Global Navigation Satellite System (GNSS) constellation presents exciting opportunities for GNSS Reflectometry (GNSS-R) with more available signal sources. This study evaluates the disparities in Cyclone GNSS (CYGNSS) observations (signal-to-noise ratio [SNR] and calibrated reflectivity) and soil moisture retrieval performances from Global Positioning System (GPS), BeiDou-3 (BDS-3) and Galileo reflected signals. To conduct the analysis, the complex waveform products from the Institute of Space Sciences (ICE-CSIC/IEEC), which are processed from the GNSS-R raw Intermediate Frequency (IF) data, are employed and post-processed to generate power waveforms with different coherent and incoherent integration times. Our findings indicate that GPS reflected signals exhibit a notable advantage in terms of noise floor, with noise power peak measuring 1.5–1.8 dB lower than that of BDS-3 and Galileo. In the comparison with reference Soil Moisture Active Passive (SMAP) soil moisture, GPS retrievals achieve the best performance, yielding a root-mean-square error (RMSE) of 0.081 cm 3 cm −3 , while the accuracies of BDS-3 and Galileo retrievals are close with an RMSE of 0.086 cm 3 cm −3 and 0.085 cm 3 cm −3 . Furthermore, increasing the coherent integration time can effectively reduce the noise floor and improving the SNR. When the incoherent integration time increases from 500 to 4000 ms, the soil moisture retrievals exhibit a decrease in RMSE of 9.52%, 10.11% and 13.64% for GPS, BDS-3 and Galileo retrievals, respectively. These results offer valuable insights for future applications of multi-constellation GNSS-R, shedding light on the potential benefits and performance characteristics of different GNSS in soil moisture retrieval.