Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R² = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation of SWE, the complete snowfall period over a winter season is split into snow accumulation, transition, and melting period in accordance with the variation characteristics of snow depth and SWE. By extensively using in situ snow depth and SWE observations recorded by snow telemetry network (SNOTEL) and regression analysis, three empirical models are developed to describe the relationship between snow depth and SWE for the three periods, respectively. Based on the snow depth fusion model and the SWE empirical models, an SWE estimation algorithm is proposed. Three data sets recorded in different environments are used to test the proposed method. The results demonstrate that there exists good agreement between the in situ SWE measurements and the SWE estimates produced by the proposed method; the root-mean-square error of SWE estimations is smaller than 6 cm when the SWE is up to 80 cm.

In this paper, GNSS interferometric reflectometry (GNSS-IR) is firstly proposed to estimate ground surface subsidence caused by underground coal mining. Ground subsidence on the main direction of a coal seam is described by using the probability integral model (PIM) with unknown parameters. Based on the laws of reflection in geometric optics, model of GNSS signal-to-noise (SNR) observation for the tilt surface, which results from differential subsidence of ground points, is derived. Semi-cycle SNR observations fitting method is used to determine the phase of the SNR series. Phase variation of the SNR series is used to calculate reflector height of ground specular reflection point. Based on the reflector height and ground tilt angle, an iterative algorithm is proposed to determine coefficients of PIM, and thus subsidence of the ground reflection point. By using the low-cost navigational GNSS receiver and antenna, an experimental campaign was conducted to validate the proposed method. The results show that, when the maximum subsidence is 3076 mm, the maximum relative error of the proposed method-based subsidence estimation is 5.5%. This study also suggests that, based on the proposed method, the navigational GNSS instrument can be treated as a new type of sensor for continuously measuring ground subsidence deformation in a cost-effective way.

As an indispensable surface parameter, soil moisture is of great significance for analyzing global water cycle, establishing the hydrological model and assessing drought. Due to differences in the capability of establishing vegetation water content (VWC) models based on different vegetation indices, the influence of the different VWC models on retrieval accuracy has not been effectively assessed. Therefore, the suitability of vegetation parameters obtained from different VWC models established by different vegetation indices for accurate soil moisture retrieval requires further investigation. In this study, the different VWC models were established by different vegetation indices derived from Sentinel‐2 data and compared. Based on the Sentinel‐1 Synthetic Aperture Radar and Sentinel‐2 data, combined with microwave scattering models, the impact of different VWC models on the accuracy of soil moisture retrieval was investigated using the Look Up Table algorithm. The results indicate that, an exponential relationship between the measured VWC and different vegetation indices was obtained in the growth cycle of winter wheat, except for quadratic relationship of Modified Soil Adjusted Vegetation Index vegetation index. Compared with other VWC models, higher accuracy of VWC could be retrieved using NDWI1, and the short‐wave infrared band located at 1610 nm achieved higher accuracy than the band located at 2190 nm. With respect to all VWC models, the combination of near‐infrared and short‐wave infrared bands was more suitable for VWC retrieval. When the contribution of soil scattering exceeded that of vegetation scattering, the results of soil moisture retrieval with different VWC models established by vegetation indices exhibit no discernible difference, regardless of the differences that occurred in VWC itself.

Spaceborne GNSS-R technology is a new remote sensing method for soil moisture monitoring. Focusing on the significant influence of water on the surface reflectivity of CYGNSS, this paper improved the removal method of water influence according to the spatial resolution of CYGNSS data. Due to the disturbance effect of the incident angle, microwave frequency and soil type on the Fresnel reflection coefficient in surface reflectivity, a normalization method of Fresnel reflection coefficient was proposed after analyzing the data characteristics of variables in the Fresnel reflection coefficient. Finally, combined with the soil moisture retrieval method of linear equation, the accuracy was compared and verified by using measured data, SMAP products and official CYGNSS products. The results indicate that the normalization method of the Fresnel reflection coefficient could effectively reduce the influence of relevant parameters on the Fresnel reflection coefficient, but the normalization effect became worse at large incident angles (greater than 65°). Compared with the official CYGNSS product, the retrieval accuracy of optimized soil moisture was improved by 10%. The method proposed in this paper will play an important reference role in the study of soil moisture retrieval using spaceborne GNSS-R data.

As the probability and intensity of global drought events continue to increase, soil moisture is an important basis for drought monitoring and drought assessment, and the need to accurately obtain soil moisture distribution information with high spatial-temporal resolution is becoming extremely important. CYGNSS data based on spaceborne GNSS-R has the advantage of high temporal resolution, while SAR data can provide information on surface features with high spatial resolution, and the combination of the two provides favourable conditions for obtaining soil moisture with high spatial-temporal resolution. This paper proposes a soil moisture retrieval method with high spatial-temporal resolution by the fusion of spaceborne GNSS-R (CYGNSS) and SAR (Sentinel-1) data. This method constructs a function relationship between surface reflectivity of spaceborne GNSS-R and backscattering coefficient of SAR, with the aim of preparing for fusion of CYGNSS and Sentinel-1. By fusing sentinel-1 data, a two-layer machine learning framework based on CYGNSS data is constructed to retrieve the soil moisture with high spatial-temporal resolution, and the retrieval results are compared with the measured data and soil moisture products of SMAP. The results indicate that, the surface reflectivity of spaceborne GNSS-R shows an approximate linear relationship with the backscattering coefficient of SAR. The constructed first-layer framework is able to supplement CYGNSS surface reflectivity data, and verifies the feasibility of converting backscattering coefficients of SAR to the CYGNSS surface reflectivity. The soil moisture retrieval by the two-layer framework method in this paper is comparable to the soil moisture product of SMAP in terms of retrieval accuracy (average ubRMSE = 0.070cm³/cm³, average R = 0.65) at the same spatial resolution (3 km), and the temporal resolution is improved by 3.9 times on average, which confirms the feasibility of soil moisture retrieval by CYGNSS at 3 km spatial resolution.