On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country’s disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating “three-dimensional deformation and soil moisture content” combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters.

The potential of spaceborne global navigation satellite system reflectometry (GNSS-R) to retrieve a variety of geophysical parameters has already been demonstrated in numerous studies. In 2021, China successfully launched the Fengyun-3E (FY-3E) polar orbit satellite. It carries the GNSS occultation sounder-II (GNOS-II) that can simultaneously receive reflected signals from the global positioning system, BeiDou, and Galileo constellations. Multiconstellation measurement significantly reduces the revisit time of the spaceborne GNSS-R data. This offers the possibility to study the sensitivity of the reflected signals from multi-GNSS constellations to surface parameters at global scale. The main objective of this article is to analyze the sensitivity of FY-3E GNOS-II surface reflectivity (SR) to soil moisture (SM), vegetation, and surface roughness for different GNSS constellations, and different incidence angles. FY-3E data from May 2023 to October 2023 were collected along with SM data from the soil moisture active passive (SMAP). The SMAP static auxiliary surface roughness parameter SMAP-h was also collected for subsequent comparisons. Furthermore, the effect of vegetation in the reflected signal was accounted for using the SM and ocean salinity INRA-CESBIO L -band vegetation optical depth. Afterward, for each GNSS constellation, the SR was binned as a function of SM, SMAP-h, and incidence angle. The results indicate that the sensitivity for the different GNSS constellations is consistent, showing similar behavior. Moreover, this study also reports for the first time the experimentally computed sensitivity of FY-3E SR to SM under different SMAP-h values. The sensitivity of GNOS-II SR to SM is in general agreement with the values in the previous studies, demonstrating the feasibility of using single-pass multi-GNSS constellation GNSS-R data to retrieve surface parameters, such as SM, with shorter revisit times. Furthermore, the results of this study re-emphasize the nonlinear sensitivity of SR to SM. As compared with higher SM, SR is more sensitive at lower SM. This highlights the shortcomings of using linear models to retrieve SM. Meanwhile, another important finding is that, for spaceborne GNSS-R data, SMAP-h may have underestimated the effective surface roughness.

Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm³cm⁻³, the unbiased RMSE (ubRMSE) is 0.042 cm³cm⁻³, and the mean absolute error (MAE) is 0.040 cm³cm⁻³ in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm³cm⁻³, the ubRMSE is 0.051 cm³cm⁻³, and the MAE is 0.044 cm³cm⁻³. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm³cm⁻³. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors.

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) has shown potential for terrestrial applications. The feasibility of monitoring and mapping surface inundation using Cyclone Global Navigation Satellite Systems (CYGNSS) has been demonstrated in literature. Nevertheless, most studies have only classified the surface into two states, inundated and non-inundated, which do not meet the requirements of refined hydrological modeling. Here, we propose a new calculation flow to retrieve the surface water fraction (Wf) based on CYGNSS data by considering fractional and dynamic surface water monitoring for the first time. First, a newly proposed physics-based algorithm was utilized for coupling CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature (Tb) to attenuate the effects of surface roughness and vegetation on CYGNSS observations. After removing the effects of surface roughness and vegetation, the SR was assumed to be equal to the sum of inundated surface reflectivity and the non-inundated surface reflectivity multiplied by the corresponding fraction. Thus, Wf was obtained on a per-pixel basis. Next, Wf retrieval results were validated using the case of the catastrophic floods of 2022 in Pakistan. A comparison with the Wf obtained using SMAP Tb showed high spatial and temporal similarity; nevertheless, the CYGNSS results had a higher spatiotemporal resolution. A quantitative pixel-by-pixel comparison with the Global Flood Monitoring (GFM) data derived from Sentinel-1 radar imagery, which had a finer resolution (~10m) showed that the average overall accuracy of Wf retrieval was 64.44% and the average commission error was 17.78%. Finally, we quantified the impact of land use and land cover on the retrieval results while analyzing the advantages and limitations of the method. Thus, this study provides new insights for the future use of spaceborne GNSS-R data for monitoring inland water bodies.