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.

Soil moisture is the most active part of the terrestrial water cycle, and it is a key variable that affects hydrological, bio-ecological, and bio-geochemical processes. Microwave remote sensing is an effective means of monitoring soil moisture, but the existing conventional radiometers and single-station radars cannot meet the scientific needs in terms of temporal and spatial resolution. The emergence of GNSS-R (Global Navigation Satellite Systems Reflectometry) technology provides an alternative method with high temporal and spatial resolution. An important application field of GNSS-R is soil moisture monitoring, but it is still in the initial stage of research, and there are many uncertainties and open issues. Based on a review of the current state-of-the-art of soil moisture retrieval using GNSS-R, this paper points out the limitations of existing research in observation geometry, polarization, and coherent and non-coherent scattering. The smooth surface reflectivity model, the random rough surface scattering model, and the first-order radiation transfer equation model of the vegetation, which are in the form of bistatic and full polarization, are employed. Simulations and analyses of polarization, observation geometry (scattering zenith angle and scattering azimuth angle), Brewster angle, coherent and non-coherent component, surface roughness, and vegetation effects are carried out. The influence of the EIRP (Effective Isotropic Radiated Power) and the RFI (Radio Frequency Interference) on soil moisture retrieval is briefly discussed. Several important development directions for space-borne GNSS-R soil moisture retrieval are pointed out in detail based on the microwave scattering model.

Oil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this paper, the original shipborne radar image collected by the teaching-practice ship Yukun of Dalian Maritime University during the oil spill accident of Dalian on 16 July 2010 was taken as the research data, and an oil spill detection method was proposed by using LBP texture feature and K-means algorithm. First, Laplacian operator, Otsu algorithm, and mean filter were used to suppress the co-frequency interference noises and high brightness pixels. Then the gray intensity correction matrix was used to reduce image nonuniformity. Next, using LBP texture feature and K-means clustering algorithm, the effective oil spill regions were extracted. Finally, the adaptive threshold was applied to identify the oil films. This method can automatically detect oil spills in shipborne radar image. It can provide a guarantee for real-time monitoring of oil spill accidents.

Vegetation is an important part of the terrestrial ecosystem and plays a vital role in the global carbon cycle. Traditional remote sensing methods have certain limitations in vegetation monitoring, and the development of GNSS-R (Global Navigation Satellite System-Reflectometry) technology provides a new and complimentary method. With the CYGNSS (Cyclone Global Navigation Satellite System) launch and the increased data acquisition, the use of spaceborne GNSS-R for vegetation monitoring has become a research hotspot. However, due to the complex characteristics of vegetation, its application in this field is still in the exploratory research stage. On the basis of reviewing the current research status, this paper points out the weak links of this technology in terms of polarization and observation geometry. Combined with the microwave vegetation scattering model, this paper analyzes the full polarization bistatic scattering characteristics of vegetation and points out the influence of vegetation parameters (density, water content, and vegetation diameters). The potential feasibility of polarization GNSS-R and future development trends of GNSS-R technology in quantitative retrieval (such as vegetation water content and biomass) are also discussed.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm’s development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm³/cm³. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

In this article, we present the first demonstration of the FengYun-3E (FY3E) Global Navigation Satellite System Occultation Sounder II-Reflectometry (GNOS-R) payload's capacity to detect near-surface soil freeze/thaw (F/T) states. This study offers an initial analysis of the F/T retrieval algorithm applied to data collected from the Arctic Circle, underscoring the GNOS-R's potential to deliver long-term near-surface soil F/T products. Data for the period extending from the launch day of GNOS-R (Day of Year (DOY) 179, 2021) to DOY 270 in 2022 were analyzed using the surface reflectivity (SR) ratio factor to discriminate F/T variations. Comparisons were made with soil moisture active passive (SMAP) F/T products, serving as an auxiliary analysis. We found a strong consistency between SR ratio factor and SMAP F/T values, with the accuracy of the F/T retrieval algorithm exceeding 60%. These findings corroborate the efficacy of the GNOS-R payload aboard FY3E in monitoring F/T patterns at higher latitudes, specifically, the Arctic Circle. The outcomes of this study will be beneficial for future F/T detection efforts using spaceborne Global Navigation Satellite System-Reflectometry payloads.