Global Navigation Satellite System Interferometric Reflectometry Research Articles

Improved tide level prediction model combined GA-BP neural networks and GNSS SNR data

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction.

Wheat height and phenology retrieval using GPS/BDS interferometric reflectometry technology

Monitoring vegetation growth utilizing the Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) technology has obtained widespread attention in recent years. GNSS-IR based approaches demonstrate the advantageous characteristics of high temporal resolution while maintaining cost-effectiveness. Most existing studies generally utilize reflected signals to retrieve the plant height and vegetation water content, while few studies focus on vegetation phenology and yield. In this study, both phenology metric dates and plant height of wheat were extracted using reflected signals data from GPS and BeiDou Navigation Satellite System (BDS) in a wheat farmland, located at northeast of Henan, China. The results showed that there was reasonable consistency between the retrieval heights and in-situ measurements, with the correlation coefficient of 0.836 for GPS and 0.868 for BDS. The damping coefficient calculated by signal-to-noise ratio (SNR) presented obvious characteristics during different wheat growth stages and the trends of the damping coefficient were basically consistent with that of the normalized difference vegetation index (NDVI). When taking a lag between the leaf area index (LAI) and the damping coefficient into consideration, the correlation was greatly increased. Moreover, the phenology metrics dates extracted by the damping coefficient were close to those estimated by NDVI with errors less than 8 days. Hence, this research preliminarily proves the feasibility of obtaining the wheat phenological period by using GNSS-IR technology.

A solution to the problem of height overcompensation in GNSS-IR corn height measurements: a canopy reflectance calibration model

ABSTRACT The global navigation satellite system interferometric reflectometry (GNSS-IR) technique for collecting corn height has the limitation of not accounting for changes in the corn canopy reflector. A model that uses the corn canopy reflection coefficient to account for changes in the canopy’s reflector and the power attenuation of the signal reflected by the soil surface is proposed. This letter proposes a method to solve the overcompensation problem of traditional GNSS-IR vegetation height measurement. Considering the decrease in GNSS signal penetration depth and the upward shift of the actual reflecting surface caused by changes in corn leaf water content (LWC), this letter provides a calibration model for the GNSS-IR corn height measurement. The GNSS signals reflected from the corn canopy were separated using empirical mode decomposition (EMD), and the height measurement was then calibrated using the directed and reflected signal ratios. The results of the experiments show that the model proposed in this letter can effectively improve the accuracy of corn height measurement, and the root mean square error (RMSE) is reduced to 0.17 m . Compared with the current study, the model proposed in this study reduces the influence of the actual reflective surface variation of the corn canopy on the height measurement using GNSS-IR.

Error exploration of ground-based GNSS-IR snow depth retrieval caused by geographical location difference

ABSTRACT The emergence of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology has expanded the detection methods of snow depth. However, there are still two problems to be solved: On the one hand, the existence of terrain error. On the other hand, there will be geographical location errors when GNSS stations, meteorological snow depth stations and modeling snow depth stations are not co-located. Based on this, this study takes P360 and P676 stations as examples to conduct research on different satellite systems. It verifies the feasibility of the terrain tilt correction (TTC) algorithm based on different azimuth intervals of reflection area. Then, according to the distance between the meteorological snow depth station (546), modeling snow depth station (KWEY and KWYS) and GNSS station (P360 and P676), it is evaluated whether the snow depth inversion accuracy of different satellite systems is related to the distance. The experiment can lead to the following three inferences. First, a meteorological station may not be a suitable reference station due to the difference in geographical location and surrounding environment. Secondly, the modeling stations are all close to the P676 station. It can be found that the root mean square error (RMSE) of the P676 station and modeling station is small, ranging from 12-15 cm, which shows that the closer the GNSS station is to the modeling station, the higher the accuracy of snow depth retrieval, confirming the conjecture that the error may occur when the stations are not co-located. Last but not least, because of its unique frequency division multiple access (FDMA) modes, the GLONASS system shows a good snow depth inversion effect compared with the meteorological station and modeling station, which further confirms the reliability of modeling station. This study provides an important data source and experimental verification for GNSS-IR technology.

Analysis of Water Level Monitoring using GNSS Interferometric Reflectometry in River Waters

GNSS Interferometric Reflectometry (GNSS-IR) is one of the newest explorations of Global Navigation Satellite System (GNSS) signals which utilizes multipath signals to calculate the vertical distance from the reflecting surface to the geodetic antenna/receiver. However, scientific-grade or geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor. Hence, in this study, low-cost GNSS receiver devices were used to monitor the surface level of PT Garam River in Pamekasan Regency using the GNSS-IR method. The study location of PT Garam River which is quite close to the sea causes the water level to vary following the tides at the sea. This is because there are needs for advanced methodologies to limit GNSS observations in a much narrower area. The river in this case is a more challenging study location in terms of monitoring the water level. Calculation of river water level height with GNSS-IR observations is based on determining the peak frequency on the periodogram resulting from signal-to-noise ratio (SNR) data extraction. The two-weeks GNSS-IR observation in this study were applied by installing sideway orientation of GNSS antenna towards the river. This will be done to see how effective the strength of the reflected signal received by the low-cost GNSS devices are in narrow footprint area. Conventional tide gauge near the GNSS-IR site was used to evaluate the accuracy of GNSS-IR in river water level monitoring. Tide analysis is carried out to obtain tidal constituents and tide predictions using least square harmonics estimation (LSE). The resulting accuracy of the validation data is at an RMSE of 15.35 cm, with the correlation value of 0.94. The type of river tides in the study location based on the calculation results is mixed tide prevailing semidiurnal. Tide prediction for 20 days gives promising results with an RMSE of 16 cm. These results indicate that low-cost GNSS device has a promising capability for water level monitoring using GNSS-IR method in a narrow reflector area.

GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique provides a new remote sensing method that shows great potential for soil moisture detection and vegetation growth, as well as for climate research, water cycle management, and ecological environment monitoring. Considering that the land surface is always covered by vegetation, it is essential to take into account the impacts of vegetation growth when detecting soil moisture (SM). In this paper, based on the GNSS-IR technique, the SM was retrieved from multi-GNSS and multi-frequency data using a machine learning model, accounting for the impact of the vegetation moisture content (VMC). Both the signal-to-noise ratio (SNR) data that was used to retrieve SM and the multipath data that was used to eliminate the vegetation influence were collected from a standard geodetic GNSS station located in Nanjing, China. The normalized microwave reflectance index (NMRI) calculated by multipath data was mapped to a normalized difference vegetation index (NDVI), which was derived from Sentinel-2 data on the Google Earth Engine platform to estimate and eliminate the influence of VMC. Based on the characteristic parameters of amplitude and phase extracted from detrended SNR signals and NDVI derived from multipath data, three machine learning methods, including random forest (RF), multiple linear regression (MLR), and multivariate adaptive regression spline (MARS), were employed for data fusion. The results show that the vegetation effect can be well eliminated using the NMRI method. Comparing MLR and MARS, RF is more suitable for GNSS-IR SM inversion. Furthermore, the SM reversed from amplitude and phase fusion is better than only those from either amplitude fusion or phase fusion. The results prove the feasibility of the proposed method based on a multipath approach to characterize the vegetation effect, as well as the RF model to fuse multi-GNSS and multi-frequency data to retrieve SM with vegetation error-correcting.

Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers.

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

Preliminary inquiry on the linear relationship between the height of the station and the ground height error retrieved by GNSS-IR with low-cost smart electronic equipment

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) ground height retrieval technology is based on global navigation satellite system (GNSS) signal reflection, which can achieve efficient and high-precision ground retrieval. However, errors cannot be avoided. And whether there is a linear relationship between the height of the station and the error is unknown. This research uses Hi-Target geodetic GNSS receivers, smart phone devices (Honor 60) and smart tablet devices (Huawei MatePad Pro) to collect a total of 5 d data from DOY65 to DOY69 in 2023, with the station heights of 0.8 m, 1.0 m, 1.2 m, 1.4 m and 1.6 m, respectively. The experimental results show that each satellite can effectively establish a linear relationship between the inversion error and the station height, which can be used in the error compensation research of different station heights under the limitation that the height of reflector is between 0.8 m and 1.6 m. Simultaneously, the error is related to the influence of comprehensive factors such as reflector type, satellite number, and data-receiving equipment. Secondly, two clustering methods, k-means and k-media, are introduced to cluster a and b in the linear relationship y = ax + b of each satellite, and it is proved that the linear relationship between inversion error and station height is obviously related to ground reflection surface (plastic track and concrete ground). Finally, it is verified that the height measurement accuracy of low-cost smart electronic equipment (Root Mean Square Error (RMSE): 0.047 m and 0.042 m) is worse than that of GNSS (RMSE: 0.010 m), but it still has good measurement performance. All in all, this study provides an essential technical reference for the error compensation of different station heights and for the application of GNSS-IR with low-cost smart electronic equipment. Due to its low-cost advantage, it has great potential in developing other surface parameter inversion of GNSS-IR technology.

Feasibility and performance evaluation of low-cost GNSS devices for sea level measurement based on GNSS-IR

The low-cost GNSS devices are more convenient, flexible, and can be used as the low-cost Global Navigation Satellite System interferometric reflectometry (GNSS-IR) sensors. In this study, the feasibility and performance of low-cost GNSS devices for the temporary sea level measurement are assessed, by jointly utilizing signal-to-noise ratio (SNR) data from an Android smartphone Redmi Note 9 Pro and a low-cost antenna BT560 connected to a CHCNAV P5 GNSS receiver, while another identical receiver equipped with a standard geodetic-quality antenna and a pressure tide gauge (TG) were placed nearby for comparison. 80-hours of SNR data from multiple GNSS constellations were collected and processed. First, the SNR data from the three devices are compared and analyzed. In most cases, the SNR data of low-cost antennas have longer and clearer oscillations. But they are maybe more vulnerable due to the manufacturing of antennas or positioning modules. Second, quality control and height rate correction are crucial to stable sea level measurement of low-cost GNSS devices. Finally, the performance of sea level measurement of multiple GNSS constellations, dual-frequency, and dual-antenna was also assessed. It is found that the low-cost GNSS devices could yield stable sea level measurements, and have a root-mean-square error (RMSE) of about 16 cm, of which the performance is equivalent to or even slightly better than standard geodetic-quality devices, making it a promising solution for the temporary TGs.

Sea-Level Estimation from GNSS-IR under Loose Constraints Based on Local Mean Decomposition.

The global navigation satellite system-interferometric reflectometry (GNSS-IR) technique has emerged as an effective coastal sea-level monitoring solution. However, the accuracy and stability of GNSS-IR sea-level estimation based on quadratic fitting are limited by the retrieval range of reflector height (RH range) and satellite-elevation range, reducing the flexibility of this technology. This study introduces a new GNSS-IR sea-level estimation model that combines local mean decomposition (LMD) and Lomb-Scargle periodogram (LSP). LMD can decompose the signal-to-noise ratio (SNR) arc into a series of signal components with different frequencies. The signal components containing information from the sea surface are selected to construct the oscillation term, and its frequency is extracted by LSP. To this end, observational data from SC02 sites in the United States are used to evaluate the accuracy level of the model. Then, the performance of LMD and the influence of noise on retrieval results are analyzed from two aspects: RH ranges and satellite-elevation ranges. Finally, the sea-level variation for one consecutive year is estimated to verify the stability of the model in long-term monitoring. The results show that the oscillation term obtained by LMD has a lower noise level than other signal separation methods, effectively improving the accuracy of retrieval results and avoiding abnormal values. Moreover, it still performs well under loose constraints (a wide RH range and a high-elevation range). In one consecutive year of retrieval results, the new model based on LMD has a significant improvement effect over quadratic fitting, and the root mean square error and mean absolute error of retrieval results obtained in each month on average are improved by 8.34% and 8.87%, respectively.

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When rm=0.600 and rn=0.700, the correlation coefficient (r) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology.

A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning

Soil moisture monitoring is widely used in agriculture, water resource management, and disaster prevention, which is of great significance for sustainability. The global navigation satellite system interferometric reflectometry (GNSS-IR) technology provides a supplementary method for soil moisture monitoring. However, due to the quality of the signal-to-noise ratio (SNR) measurements and the complex surface environment, inevitable outliers in multipath interference signal metrics (amplitude, frequency, and phase) were used as modeling variables to inverse GNSS-IR soil moisture. Besides, it is hard to use the univariate model to comprehensively analyze the relationship between the various factors, due to the poor fitting effect and weak generalization ability of the model. In this paper, the minimum covariance determinant (MCD) robust estimation and machine learning algorithms are adopted. The MCD robust estimation can eliminate outliers of the multipath signal metrics and machine learning algorithms, including the back propagation neural network (BPNN), Gaussian process regression (GPR), and random forest (RF), and can comprehensively establish nonlinear GNSS-IR soil moisture inversion models using multipath interference signal metrics. Moreover, the study of the modeling parameter selection for the three machine learning algorithms and the inversion results for single satellite and all satellites are also carried out to make the algorithms more generalizable. The results show that the correlation coefficients (R) and the root mean square error (RMSE) of the machine learning models for all satellite tracks are increased by 4.3~86.6% and reduced by 2.8~30%, respectively, compared with the MCD multiple regression model. The RF model with 80 decision trees and 1 node shows the clearest improvement. The total model using all satellite data has more generalization ability than the single satellite model but causes some loss of accuracy.

Simultaneous Retrieval of Corn Growth Status and Soil Water Content Based on One GNSS Antenna

The retrieval of crop growth status using Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has become a major area of interest within the field of vegetation remote sensing in recent years. Using only a single GNSS antenna, it is difficult to determine the crop growth status and soil water content (SWC) in vegetation-covered regions due to plenty of multi-path signals. Based on the empirical mode decomposition and the spectrum difference, this study presents an algorithm that can decompose and separate signals reflected by the soil surface or corn canopy. Because the low-roughness soil surface is isotropic while the corn canopy is anisotropic, the signals reflected by the soil surface have a higher proportion of coherent components than those reflected by the corn canopy. The moduli between the retrieved heights and the actual heights (for the same interval from different satellites) have the least variance. In this study, the signals reflected by the soil surface and the corn canopy are separated using the variance of retrieved heights. When the corn grows taller than the GNSS antenna, the vegetation water content (VWC) of the corn leaves becomes the primary factor affecting the direct signal’s intensity, as the leaves obstruct the signal. Hence, the VWC of corn leaves can be calculated through the power attenuation of signals. An experiment performed on a plot of land covered with corn shows that, after multi-GPS-satellite fusion, the correlations between the retrieved corn canopy height, leaf VWC, soil water content (SWC), and in situ data reach 0.94, 0.92, and 0.88, respectively. The corresponding root mean square errors are 0.195 m, 0.0055 kg/cm2, and 0.0484 cm3/cm3, respectively.

Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry.

The global navigation satellite system-interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal's "notch" and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil's attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92.

A New Algorithm for Measuring Vegetation Growth Using GNSS Interferometric Reflectometry

The use of global navigation satellite system interferometric reflectometry (GNSS-IR) to measure vegetation growth status has become a rapidly growing technique in remote sensing. GNSS signals reflected by the soil surface affect the accuracy of vegetation growth status (vegetation cover density) measurement, and the influence of soil moisture (SM) varies. This study establishes a calibration model that can reduce the influence of SM and snow layer on reflectivity. We used a direct-reflected signal amplitude ratio and GNSS-IR altimeter based on the Lomb–Scargle Periodogram to calculate the reflectivity of vegetation and snow layer depth. GNSS data from plate boundary observation were used to verify the validity of our model. The results show that reflectivity correlates better with vegetation growth status after calibrating the influence of the SM and snow layer. Moreover, the correlation increased by nearly 0.14. This study analyzed the influence of the snow layer and found that it had a noticeable effect on vegetation growth status measurement when the snow depth was over 30 cm. Furthermore, a fusion method is proposed to improve the accuracy of vegetation growth status measurement by combining the reflectivity and normalized microwave reflection index (NMRI). The experimental results show that better performance can be obtained compared to the single observation of the reflectivity and NMRI, and the best correlation between the measured and in situ normalized difference vegetation index is over 0.91, and the root mean square error decreases to 0.1893.

Improvement of Snow Depth Inversion Derived From Terrain Tilt Correction and Multi-GNSS Measurements Using the Helmert Variance Component Estimation

As an essential part of the global water cycle, the effective detection of snow depth is of great significance to the study of climate change. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology emerged as a new ground-based snow depth monitoring method. The principle is to invert the height of the reflection surface according to the SNR at a low elevation angle. However, the topographic relief in the reflection area of GNSS stations often dramatically impacts the accuracy of snow depth inversion. This study proposes a Terrain Tilt Correction (TTC) algorithm based on different azimuth intervals to address it. Taking the P351 and P676 stations in the United States as examples, the snow depth inversion research of different systems are carried out. In the P351 station with gentle terrain, the TTC algorithm can maintain stable accuracy. In the P676 station with steep terrain, the TTC algorithm can effectively compensate for the error caused by the landscape and significantly improve the accuracy of snow depth inversion. The experimental results of the two stations show that the snow depth inversion results of different systems after TTC are consistent with the in situ snow depth provided by the respective meteorological stations. Subsequently, a Helmert Variance Component Estimation (HVCE) weighting algorithm is introduced for multi-system combinations based on different systems with different snow depth inversion accuracies, and the feasibility and stability of the combination strategy are investigated. Compared with the in situ snow depth, the correlation coefficient (R) of the combined snow depth at the P351 station is 0.91, the Root Mean Square Error (RMSE) is 19.7 cm, and the Mean Error (ME) is -12.2 cm. The snow depth obtained by the combination of the multi-system at the P676 station has an R of 0.95, an RMSE of 9.9 cm, and an ME of -4.9 cm. Compared with HVCE weighting before TTC, the RMSE is reduced by 53.3%, and the absolute value of ME is reduced by 74.2%. This study is not only of great significance for high-precision snow depth monitoring, but also provides a necessary technical reference for further research on multi-system combined.

Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology provides a new means of snow depth detection. Multi-satellite and multi-Signal-to-Noise Ratio (SNR) provide more data for daily high-precision snow depth retrieval, but also face the problem of data fusion and effective utilization. Therefore, this study proposes a robust estimation algorithm based on multi-satellite and multi-SNR fusion applied to the observations of a GNSS station in Alaska. This study uses four solutions (Savg, Smed, SRE_avg and SRE_med) to carry out multi-system fusion snow depth inversion and precision comparison research. The Savg has more obvious disadvantages, which is not suitable for snow depth assessment. The SRE_avg and SRE_med have better snow depth retrieval effects in the snowy periods. The correlation coefficient (R), root mean square error (RMSE) and mean error (ME) of the calculated snow depth using the robust estimation algorithm with respect to the nearest in-situ measurements reached 0.759, 3.7 cm and −1.4 cm, respectively. Compared with the Smed, the R is increased by 2.0 %, the RMSE corresponds to an improvement of 2.6 %. Moreover, the ME of the snow depth retrievals, as an indicator of the measurement bias, has significantly decreased by 6.7 %. The result also shows that the snow depth inversion by the robust estimation algorithm is more consistent with the in-situ measurements, further extending and advancing the optimal algorithm for snow depth retrieval.

Monitoring of Wheat Height Based on Multi-GNSS Reflected Signals

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), a new and inexpensive technique, has become available to the broader scientific community for detecting surface environmental information, such as soil moisture, snow depth and vegetation growth. However, there have been limited experiments focusing on the potential of crop height retrieval, especially the performance evaluation of BeiDou Navigation Satellite System (BDS) with other GNSS. Accuracy and reliability are challenging to achieve with traditional methods utilizing a single GNSS, and few measured verification data. In this study, an improved method that includes segmentation processing and multi-GNSS fusion is proposed based on GPS/GLONASS/Galileo/BDS multi-frequency data. Furthermore, experiments were carried out on a farmland in Fengqiu County, Henan Province, China. The results show that the height retrievals from four GNSS were in good agreement with the in situ observations during the whole growth cycle of the wheat after overwintering. Meanwhile, the retrievals based on the proposed method exhibited greater correspondence than the single frequency results, the correlation coefficient was increased and the root-mean-square error (RMSE) was reduced, respectively. Therefore, this study illustrates the feasibility of the proposed method to precisely estimate wheat height and its potential for use in the early warning of wheat lodging based on GNSS-IR.

Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China

Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring.

A new snow depth data set over northern China derived using GNSS interferometric reflectometry from a continuously operating network (GSnow-CHINA v1.0, 2013–2022)

Abstract. The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate models, hydrological models, and regional snow disaster monitoring. Benefitting from its unique advantages of cost-effective and high spatiotemporal resolution (∼ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are (1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and control its quality automatically; and (2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS constellations (mean r=0.98, RMSD = 0.99 cm, and nRMSD (snow depth > 5 cm) = 0.11), different frequency bands (mean r = 0.97, RMSD = 1.46 cm, and nRMSD (snow depth > 5 cm) = 0.16), and different GNSS receivers (mean r = 0.62). The data set also has high external consistency with the in situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. Additionally, the result show the potential of GNSS to derive hourly snow depth observations for better monitoring of snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and data files will be maintained and updated as more data become available in the future. The GSnow-CHINA v1.0 data set is available at the National Tibetan Plateau/Third Pole Environment Data Center via https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al., 2021).