Global Navigation Satellite System Reflectometry (GNSS-R) has long been explored for retrieving sea ice properties, but in-situ validation in the central Arctic during the freezing season is rare, limiting its application. The primary objective of this study is to advance the current understanding of multi-polarization GNSS-R remote sensing for sea ice application. This paper presents observations from the full-polarization GNSS-R(FpolGNSSR) prototype during the MOSAiC expedition. The FpolGNSSR, with four polarization channels and high antenna gain (11.3 dB), aims to assess the impact of sea-ice thickness and permittivity on GNSS-R data, with observations from October 2019 to January 2020, the onset period of ice growth. First, the reflectivity is simulated by a four-layer model, and the sensitivity of multi-polarization GNSS-R to sea ice is qualitatively analyzed. Subsequently, a simplified model reveals a linear relationship between reflectivity and ice thickness, with regression showing a correlation of 0.74 (P<0.01). The retrieval error (RMSE) of sea ice thickness retrieval is 0.13 m for first-year ice (0.3–1.0 m thick). Additionally, the imaginary component of sea ice permittivity is estimated, between 0.018 and 0.039. This study provides valuable insights for the GNSS-R community, which could be summarized as: (1) the “H-polarization advantage”, which is first proposed in sea ice GNSS-R, (2) the validity of a simplified model, reinforcing the feasibility of satellite-based sea ice thickness estimations using Left-hand-circular, V, and H polarizations, and (3) a lower apparent permittivity of GNSS-R than previously reported, corresponding to a deeper penetration depth.

Global navigation satellite system-reflectometry (GNSS-R) is capable of typhoon wind speed (WS) detection, benefiting from its unique feature of L-band forward-scattered signal with frequent temporal revisits. However, the fluctuation and deviation in observations limit the performance. Conversely, the C-band scatterometer can provide stable observations with a larger width, while the backscattered signal tends to be saturated when WSs exceed 25 m/s, accompanying rain attenuation. The complementary advantages of GNSS-R and scatterometer offer a novel strategy for typhoon maximum sustained wind (MSW) estimation. This study first explores the potential of typhoon MSW estimation using data provided by the GNSS occultation sounder-reflectometry (GNOS-R) and Wind Radar (WindRAD) codeployed on China’s polar-orbiting meteorological satellite FengYun-3E (FY-3E). A typhoon MSW estimation method using combined GNSS-reflectometry and scatterometer (CoGREAT) was proposed, considering the distance from the typhoon center, the WS, and the rain attenuation. The experimental results of five typhoon cases in the Western Pacific show that CoGREAT outperforms GNOS-R-only and WindRAD-only methods for various grades of typhoons, achieving an overall absolute bias of 2.77 m/s taking the typhoon best-track (BST) dataset as the reference. Removing GNOS-R or WindRAD observations in CoGREAT reduces accuracy by 0.86 or 3.39 m/s, respectively, but still outperforms single-source methods without weight factors. In addition, using the typhoon center information provided by FengYun-4B (FY-4B) further proves that the method can be extended to near-real-time MSW estimation. This study provides a valuable reference for similar observation modes.

This letter presents a new ocean surface wind speed (OSWS) retrieval algorithm called Combined SCAtterometry ReflectometrY (CSCARY). The algorithm uses the maximum-likelihood estimator (MLE) and geophysical model functions (GMFs) to combine backward and forward observables from the advanced scatterometer (ASCAT) and the cyclone global navigation satellite system (CyGNSS) to achieve more accurate wind speed retrieval. To verify the efficacy of the CSCARY algorithm, a case study is conducted on part of the South China Sea. Results showed that the algorithm improved wind speed accuracy by 28.22% for low-medium wind speeds (2–10 m/s) and 25.33% for medium-high wind speeds (6–16 m/s) compared to the operational ASCAT algorithm. Additionally, the most significant improvement is observed at medium wind speeds, with an increase of 23.6%. These findings suggest that combining scatterometer and global navigation satellite system-reflectometry (GNSS-R) could lead to better OSWS retrieval.

Based on a statistical analysis of the currently available 3-year on-orbit Cyclone Global Navigation Satellite System (CYGNSS) data (2017–2019), this study proposes a two-step calibration method to improve the accuracy of the CYGNSS-derived land surface reflectivity (SR) and the resulting soil moisture (SM) estimates. The method is designed for two purposes: the one is to correct the system errors of the SR estimates induced by the calibration of the CYGNSS Version 2.1 L1B data, and the other is to eliminate vegetation attenuation in the SR of the soil layer. Mean SR corrections of ~ −0.9 and 2.2 dB are achieved through the first and second steps of calibration, respectively. This resulted in better SM estimates compared with the Soil Moisture Active Passive (SMAP) product and thein situmeasurements, i.e., improved correlations between the CYGNSS SR and the SMAP SM (from$R = 0.46$to$R = 0.74$), improved correlations between the CYGNSS SR and thein situSM (from$R = 0.47$to$R = 0.62$), and between the CYGNSS SM and thein situSM with the best$R = 0.64$.

As a pilot mission for the Chinese global navigation satellite system reflectometry (GNSS-R) constellation, the BuFeng-1 (BF-1) twin satellites A/B were launched on June 5th, 2019. Using three-month of sample data, this study presents the first evaluation of this mission for soil moisture (SM) estimations. The results show that the surface reflectivity (SR) derived from BF-1 bistatic radar cross section (BRCS) correlates well with the SM active passive (SMAP) SM (mean$R =0.85$), and the resulting BF-1 SM shows good correlation with the SMAP SM [$R =0.94$and root mean square deviation (RMSD) = 0.029] and thein situmeasurements ($R =0.77$and RMSD = 0.049). The evaluation provides supportive information for the production and release of the new version of BF-1 Level 1 data and for the development of new algorithms to retrieve SM using data of this mission.

The Global Navigation Satellite System reflectometry (GNSS-R) technique has demonstrated its potential for terrestrial applications. Over inland water bodies, the dominance of coherent components in GNSS-R has been widely recognized. Nevertheless, little attention is given to GNSS-R speckle, which is inherent to coherent imaging systems. In this study, taking the multiplicative speckle into account, we regard GNSS-R coherent scattering as a statistical distribution. First, the expression of the statistical distribution is identified and parameterized using observations from the Cyclone Global Navigation Satellite System (CyGNSS). The results suggest that the power tends to obey a three-degrees-of-freedom distribution model. Second, the multilook statistics of CyGNSS, such as the mean value and the coefficient of variation (CV), are analyzed on different spatial–temporal scales. Finally, we realize surface water mapping using multilook statistics. Comparison with the state-of-the-art algorithms shows that the proposed method can effectively improve the goodness of water mapping, with higher overall accuracies (~0.97) and F1 scores (~0.60). This study provides new insights into future GNSS-R land observations.

Accurate and high temporal-spatial resolution snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has become a popular topic. The terrain is a significant issue affecting GNSS-IR snow depth retrieval. This study explores detailed information about the problems and new solutions to this issue. The main conclusions include but are not limited to the following aspects: 1) Reflections from Below the Antenna (RBA) is the primary geometrical relationship of the GNSS multipath signal for snow depth retrieval; 2) The proposed GSnow_TERR model considers the terrain effects by incorporating the surface tilt angle (γ) into the derivative of the multipath relative phase (φ) with respect to the satellite elevation angle (e). Unlike the previous region-by-region solutions, the new model has a definite physical meaning which considers the γ as an independent non-negligible variable; 3) The new model performs well for repeatable and non-repeatable GNSS tracks. The former agrees with the PBO H2O product with r2= 0.97 and a slightly 1 ~ 2 cm improvement in the RMSE. The latter agrees with the baseline two-step clustering method for GLONASS, Galileo, and BDS, with r2= 0.96 and RMSD = 1.11 cm. The rate of data utilization increases by a mean value of 38.21% for non-repeatable tracks. The performance of the model in practical applications is consistent with theoretical analysis. The findings of this study provide a valuable reference for future GNSS-IR snow depth research and applications.

Remotely sensed soil moisture (SM) with high accuracy and high spatial–temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth ($\tau$) and the surface roughness parameter ($h$). The CYGNSS L1 Version 2.1 data of the years 2017–2018 and 2019–2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM andin situSM data on a global scale ($R =0.679$, RMSE$=0.051\,\,\text{m}^{3}\text{m}^{-3}$, and MAE$=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$against SMAP SM;$R = 0.729$againstin situSM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of$h$or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2% and spatial coverage increased by 31.6%.

Sea surface salinity (SSS) can be measured by L-band (1.4 GHz) radiometry. However, the L-band brightness temperature is sensitive to ocean surface roughness, so that the precise knowledge of sea state can help to improve the accuracy of SSS retrievals. Cyclone Global Navigation Satellite System (CyGNSS) mission measures tropical ocean wind speeds, which can provide further knowledge about sea state. This letter, by investigating the sensitivity of brightness temperature derived from two L-band radiometry satellite missions [i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)] to CyGNSS data, first explores the potential of using spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) to improve the accuracy of SSS measurements. The statistical results from two-year CyGNSS data show that the systematic uncertainties of the brightness temperature up to 0.5 K in the SMOS/SMAP can be minimized at low wind speed, which proves the concept that CyGNSS wind speed data can be used to improve the accuracy of SSS retrievals across the oceans. The findings strongly suggest that the spaceborne GNSS-R instruments could be utilized as cost-effective hosted payloads in designing future ocean remote sensing missions.

Channel network topology plays an important role in hydrological analysis. This letter proposes an innovative method to construct it based only on remote sensing images. The method uses spectral water indexes and mask of large lakes and ocean areas derived from remote sensing data to generate the map of channels. Then, a morphological thinning algorithm is introduced to extract the initial skeleton of channels. Moreover, an iterative pruning process based on a graph algorithm is proposed to simplify the initial skeleton. Finally, according to the simplified skeleton and its adjacency matrix, a new connectivity graph can be constructed to describe the channel network topology. The proposed method can construct complete skeleton structure and the topology of channels with good connectivity in an automatic way. The output will facilitate detailed hydrological modeling and further applications.