The process of soil freezing and thawing refers to the alternating phase change of liquid water and solid water in the soil, accompanied by a large amount of latent heat exchange. It plays a vital role in the land water process and is an important indicator of climate change. The Tibetan Plateau in China is known as the “roof of the world”, and it is one of the most prominent physical characteristics is the freezing and thawing process of the soil. For the first time, this paper utilizes the spaceborne GNSS-R mission, i.e., CYGNSS (Cyclone Global Navigation Satellite System), to study the feasibility of monitoring the soil freeze-thaw (FT) cycles on the Tibetan Plateau. In the theoretical analysis part, model simulations show that there are abrupt changes in soil permittivities and surface reflectivities as the soil FT occurs. The CYGNSS reflectivities from January 2018 to January 2020 are compared with the SMAP FT state. The relationship between CYGNSS reflectivity and SMAP soil moisture within this time series is analyzed and compared. The results show that the effect of soil moisture on reflectivity is very small and can be ignored. The periodic oscillation change of CYGNSS reflectivity is almost the same as the changes in SMAP FT data. Freeze-thaw conversion is the main factor affecting CYGNSS reflectivity. The periodical change of CYGNSS reflectivity in the 2 years indicates that it is mainly caused by soil FT cycles. It is feasible to use CYGNSS to monitor the soil FT cycles in the Tibetan Plateau. This research expands the current application field of CYGNSS and opens a new chapter in the study of cryosphere using spaceborne GNSS-R with high spatial-temporal resolution.

In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of −0.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to −0.09 and 0.49 m, respectively. When the incidence angle ranges from ${35}^{°}$ to ${65}^{°}$ and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the “Block III” satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data.

Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

Based on a developed three-layerair–ice–water reflection model, this article simulates the evolution of reflection coefficient versus elevation angle. Due to the interference between the signal components from the air–ice and ice–water interfaces, the reflection coefficient experiences an oscillating pattern versus elevation angle so that detecting sea ice using the power or amplitude of the reflected global navigation satellite system (GNSS) signal has to choose a suitable satellite to reduce the influence of the oscillating pattern. A sea ice surface is more stable and presents higher correlation than a dynamic ocean surface, this article explores the potential of detecting and measuring sea ice using the coherency of reflected GNSS signal for coastal scenario. Experimental results show that phase coherency can significantly detect sea ice without strictly limiting elevation and azimuth angle. This article also is to is to explore the potential of retrieving sea ice thickness using the oscillating phase pattern versus elevation angle. The phase compensation and the dual-polarization observation are proposed to remove the delay phase between the direct and reflected signal from the estimated phase of the reflected GNSS signal. The results show that the amplitude and frequency of the oscillating phase pattern, respectively, have an inversely proportional and positively linear relationship with sea ice thickness. Simulation shows that, compared to the oscillating amplitude, the oscillating frequency is a better choice to measure sea ice thickness. The frequency of the dual-polarization oscillating pattern could provide the measurement performance with a root-mean-square error of 0.05 m.

A study investigating the feasibility of retrieving wind direction using low-altitude backward geometry of Global Navigation Satellite System-Reflectometry (GNSS-R) is implemented in this paper. This paper first focuses on analyzing the influence of wind direction on the roughness of sea surface and scattering strength. The results show that backward scattered GNSS signals obviously change over wind direction despite of the weaker power level than the forward signals. Then, the power link of backward scattered GNSS signal and the visible number of the satellites composing the bistatic backward observation geometry with the receiver are analyzed. Backward scattered GNSS signals are lower about $10\text{--}20$ dB than forward ones; hence, a higher gain left-hand circularly polarization antenna should be used to receive them. In the same antenna view, theoretically the backward scattered signals from about three satellites could be received. Finally, a retrieving algorithm of wind direction based on matching theory is proposed. The retrieving results using simulated data show that, first, multibeam observation (at least three-beam observation) should be utilized to remove the uncertainty of retrieving wind direction, second, the accuracy of wind speed and the signal-to-noise ratio (SNR) of the delay waveform importantly impact on the retrieving performance of wind direction, therefore, it is needed to improve the measuring accuracy of wind speed and the SNR. In one word, all results mentioned above give the guide information to observe wind direction using low-altitude GNSS-R.