Global navigation satellite system (GNSS) refractometry is a new technique that utilizes two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. In the previous study, a linear model that uses wheat height, air temperature, and the amplitude ratio (AR) as inputs was utilized to estimate the volumetric water content (VWC) of wheat. In this study, a second-order nonlinear function is utilized to describe the relationship among GNSS AR, wheat VWC, wheat height, and air temperature, leading to an improved model for estimating wheat VWC. The function coefficients are determined by exploiting the least-squares method to the field measurements collected from April 5, 2023, to June 5, 2023. Once the function coefficients and inputs are obtained, the model can be easily used to calculate the wheat VWC. The model was validated using an independent dataset of field measurements collected from April 5, 2024, to June 5, 2024. The results show that the improved model performs significantly better than the previous linear model, and the root-mean-square (rms) error of the improved model-based GNSS wheat VWC estimation is 0.224 kg/m3 when the in situ wheat VWC ranges from 1.404 to 6.521 kg/m3. This model can help precisely control the timing and water usage of agricultural irrigation, thereby optimizing water management for crops.

This paper firstly reports the combined technique of GNSS positioning and GNSS-IR (GNSS+IR) for imaging the underground coal mining induced ground subsidence deformation over an area of ~10000m2based on a single GNSS station collected observations. The GNSS positioning is utilized to measure the movements of the GNSS antenna; and phase of the reflected GNSS SNR series is utilized to calculate vertical and horizontal distance from the ground specular reflection point to the antenna. The ground subsidence and plane coordinate of the ground specular reflected point can be obtained based on the GNSS antenna movements and the vertical and horizontal distance. An analytical function is developed to describe ground subsidence around the GNSS station; the function coefficients can be estimated by using the least-squares-method to the estimations of the ground subsidence and plane coordinate at the ground reflection points. After obtaining the coefficients, subsidence deformation around the GNSS station can be imaged based on the analytical function. The preliminary results show that there is a good agreement between the proposed method based results and the reference data sets, with the RMSE less than 5cm when the in-situ ground subsidence is in the range from 0cm to 300cm.

The global navigation satellite system (GNSS) refraction pattern technique makes use of two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. Due to the sensitive response of the refracted signal to variation of the dielectric constant, the technique is suitable for measuring medium dielectric constant-related parameters such as snowpack density, vegetation water content, and soil moisture. In this article, a forward model related to the power ratio of the refracted signal to the direct one is developed to elucidate the mechanism of soil moisture-induced refracted signal strength attenuation. By making use of the simulating results derived from the model, a second-order polynomial function is established to describe the relationship between the power ratio, soil temperature, and soil moisture. Based on the function, an inversion algorithm, which takes the GNSS carrier-to-noise (C/N0) observations under high elevation angles (50°–60°) and soil temperature as the inputs, is proposed for the near-surface soil moisture estimation. The proposed algorithm is validated through a dataset collected in an experimental campaign over two years. The results demonstrate that there exists a good agreement between the proposed method-derived soil moisture estimations and ground-truth ones; and the root-mean-square error (RMSE) of the proposed algorithm-derived soil moisture estimation is 0.009 cm3cm−3 when the ground-truth soil moisture is in the range from 0.150 to 0.550 cm3cm−3. Because the observations were collected by using consumer-grade GNSS chips and antennas, this study also provides a basis for the design and development of the low-cost GNSS soil moisture sensor in the future.

Global Navigation Satellite System interferometric reflectometry is an emerging remote sensing technique that can be used to measure a wide range of geophysical parameters. In this letter, a low-cost navigational GNSS chip and RHCP antenna were used to receive and process the interference GNSS signal in a winter wheat farmland. By simplifying the wheat crop as multi-layer equivalent mediums (EMs), the characteristics of GNSS SNR observations recorded by the instrument were analyzed. The confidence level of the Lomb-Scargle spectral analysis result was used to identify the peak frequencies of the GNSS SNR series. Based on the peak frequencies, the EM heights can be calculated. The height estimations were used to compare with thein situwheat height measurements. The results show that the estimated EM height is inversely proportional to thein situwheat height in the wheat stem extension stage, with a correlation coefficient of −0.9939; and the estimation is very close to thein situones in wheat heading and ripening stages, with a root-mean-square error of 5.8 cm when the wheat height ranges between 40 and 75 cm.

In the last 20 years, the reflected signal of Global Navigation Satellite System (GNSS) has been used for remotely sensing a series of geophysical parameters, resulting in two GNSS based remotely sensing techniques: GNSS reflectometry (GNSS-R) and GNSS interferometric reflectometry (GNSS-IR). In this letter, the refracted GNSS signal is first proposed to estimate near-surface soil moisture (SM). Amplitude of the refracted GNSS signal will attenuate when penetrated into soil due to refraction and propagation of the signal in the soil. Amplitude attenuation degree of the refracted signal is quantified as the amplitude ratio (AR) of the direct GNSS signal to the refracted signal. Two low-cost navigational GNSS chips and right-hand circularly polarized (RHCP) antennas are used to collect the refracted and direct GNSS signal in an experimental campaign, respectively. To simplify the modeling, the AR at elevation angle of 20° is used to develop the model to describe the relationship between SM, AR, and soil temperature (ST) in the letter; and the AR and ST observation can be converted into SM accurately with a 2nd-order polynomial. The modeled SMs are strongly correlated with the sensor-based ones with correlation coefficient of 0.947 and root-mean-square error (RMSE) of 0.013 cm3/cm3(or, 1.3%) when SM is between 0.272 and 0.489 cm3/cm3. The study also suggests that, based on the proposed method, the low-cost GNSS instrument can be treated as a new type of sensor monitoring SM in a cost-effective way.

Soil moisture is fundamental to land surface hydrology, affecting flooding, groundwater recharge, and evapotranspiration. In this paper, a low-cost GNSS receiver is used to estimate soil moisture the first time. A new soil moisture estimation method based on the receiver SNR data is proposed. The relationship between amplitude attenuation factor (AAF) of SNR and soil moisture is investigated by using in-situ observations at first. Then, the retrieval method of SNR AAF is proposed. GPS data collected over a month in Chongqing, China was used to test the proposed method. Based on in-situ SNR and soil moisture observations, 1stand 2ndregression functions converting AAF into soil moisture were established by least squares method. The preliminary results show that there is a good agreement between the insitu soil moisture and the estimated one by the proposed method, with the RMSE smaller than 0.012 when soil moisture is in the range of 0.35 to 0.45.

In this paper we propose a new snow water equivalent (SWE) estimation method using GNSS-R method. The forward model is established to describe the relationship between antenna height (snow depth), snow density and multipath error by using combination of pseudorange and carrier-phase of GNSS dual-frequency signals. As the function of antenna height and snow density, the forward model is used to construct the fitness function based on least squares principle. Then, the problem of inversion of antenna height and snow density is transformed into a conventional multi-variable function optimization problem. The genetic algorithm is used to find the minimum of the fitness function to obtain the optimal snow depth and snow density estimates. The Galileo satellite navigation system data of an experimental campaign conducted in Harbin, China was used to test the proposed method. The preliminary results show that the proposed method can achieve SWE estimation accuracy of about 4cm.

Global navigation satellite system reflectometry (GNSS-R) is a new remote sensing technique, which can be used to measure a wide range of geophysical parameters. GNSS-R makes use of the simultaneous reception of the direct transmission and the coherent surface reflections of the GNSS signal with either a single antenna or multiple separate antennas. This paper presents a new snow depth estimation method using a combination of pseudorange and carrier phase of GNSS dual-frequency signals. The proposed method is geometry-free and is not affected by ionospheric delays. The formulas of the amplitude attenuation factor of reflected signals, multipath-induced carrier-phase error, and pesudorange error for ground-based GNSS receivers are used to describe the combined signals. Using theoretical formulas instead of in situ measurement data, analytical linear models are established in advance to describe the relationship between snow depth and main frequency of combined signal time series. When the main frequency of the combined measurements is obtained by spectrum analysis, the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that there exists good agreement between the proposed method and the ground-truth measurements.

Continuous and reliable monitoring of world-wide snowfall is important for study of climate change and water resource utilization. Both snow depth and snow water equivalent (SWE) are the measure of snowfall, but SWE is a more useful measure, which is defined as the product of snow depth and snow density. Global Navigation Satellite System reflectometry (GNSS-R) is a new remote sensing technology that can enable cost-effective global-scale and continuous monitoring of snowfall. This paper presents a new snow density estimation method based on GNSS-R by matching the envelope of the SNR amplitude with that of the modeled SNR amplitude. Field experimental data are used to evaluate the proposed model based snow density estimation method. The experimental results demonstrate that the RMS of the density estimation error is 0.02gcm-3.

Global Navigation Satellite System reflectometry (GNSS-R) is a new remote sensing technique which can be used to measure a wide range of geophysical parameters. GNSS-R makes use of the simultaneous reception of the direct transmission and the coherent surface reflections of GNSS signal with either a single antenna or two separate antennas. This paper presents a snow depth estimation method using pseudorange and carrier-phase combination of BDS (BeiDou Navigation Satellite System) dual-frequency (B1 and B2) signals. The proposed method is geometry free and is not affected by ionospheric delays. The GNSS receiver of Trimble R9 was used to collect BDS satellite signals in a ground-based experimental campaign recently conducted in Harbin, Heilongjiang Province, China. The GNSS data recorded during the experimental campaign were used to test the proposed method. The results demonstrate that there exists good agreement between the proposed method and the ground-truth measurements.