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.

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.