Global Navigation Satellite System Interferometric Reflectometry Research Articles

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).

Ground surface elevation changes over permafrost areas revealed by multiple GNSS interferometric reflectometry

Ground subsidence and uplift caused by the annual thawing and freezing of the active layer are important variables in permafrost studies. Global positioning system interferometric reflectometry (GPS-IR) has been successfully applied to retrieve the continuous ground surface movements in permafrost areas. However, only GPS signals were used in previous studies. In this study, using multiple global navigation satellite system (GNSS) signal-to-noise ratio (SNR) observations recorded by a GNSS station SG27 in Utqiaġvik, Alaska during the period from 2018 to 2021, we applied multiple GNSS-IR (multi-GNSS-IR) technique to the SNR data and obtained the complete and continuous ground surface elevation changes over the permafrost area at a daily interval in snow-free seasons in 2018 and 2019. The GLONASS-IR and Galileo-IR measurements agreed with the GPS-IR measurements at L1 frequency, which are the most consistent measurements among all multi-GNSS measurements, in terms of the overall subsidence trend but clearly showed periodic noises. We proposed a method to reconstruct the GLONASS- and Galileo-IR elevation changes by specifically grouping and fitting them with a composite model. Compared with GPS L1 results, the unbiased root mean square error (RMSE) of the reconstructed Galileo measurements reduced by 50.0% and 42.2% in 2018 and 2019, respectively, while the unbiased RMSE of the reconstructed GLONASS measurements decreased by 41.8% and 25.8% in 2018 and 2019, respectively. Fitting the composite model to the combined multi-GNSS-IR, we obtained seasonal displacements of − 3.27 ± 0.13 cm (R2 = 0.763) and − 10.56 ± 0.10 cm (R2 = 0.912) in 2018 and 2019, respectively. Moreover, we found that the abnormal summer heave was strongly correlated with rain events, implying hydrological effects on the ground surface elevation changes. Our study shows the feasibility of multi-GNSS-IR in permafrost areas for the first time. Multi-GNSS-IR opens up a great opportunity for us to investigate ground surface movements over permafrost areas with multi-source observations, which are important for our robust analysis and quantitative understanding of frozen ground dynamics under climate change.

Investigating the spatio-temporal variation of vegetation water content in the western United States by blending GNSS-IR, AMSR-E, and AMSR2 observables using machine learning methods

Vegetation water content (VWC) is an important parameter when evaluating vegetation growth and climate change. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has provided us with an effective approach for the monitoring of VWC. The normalized microwave reflection index (NMRI) was defined to reflect the change of VWC, based on the fact that the amplitude of the direct and reflected GNSS interferometric signal is related to the variation of VWC. However, the sparse distribution of the ground-site-based observation stations restricts the application of NMRI. Fortunately, microwave vegetation optical depth (VOD) is a dimensionless parameter that describes the rate of attenuation of microwaves as they pass through the vegetation canopy, which can provide spatially continuous vegetation information. In this study, we integrate the GNSS-IR NMRI dataset with Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) VOD datasets by using three machine learning models: the generalized regression neural network (GRNN) model, the back-propagation neural network (BPNN) and multiple linear regression (MLR) models. This method can overcome the drawbacks of sparse distribution and spatial discontinuity of GNSS-IR site data, and obtains spatio-temporally continuous NMRI products that can more intuitively and accurately reflect the variability of VWC in the study area. The results showed that the GRNN model has the best retrieval accuracy, with model fitting and cross-validation R values of 0.76 and 0.74, and root-mean-square error (RMSE) values of 0.034 and 0.035, respectively. Based on the GRNN model, we obtained a spatio-temporally continuous NMRI product to further analyze the changes in VWC from 2007 to 2018. The results indicated that the effects of ENSO events with different intensities on vegetation and precipitation are different, and the response of VWC with different vegetation types to ENSO are also different. The anomalies of VWC show a lag phenomenon and is more stable, not as sensitive as precipitation to the ENSO events.

Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors

Global navigation satellite system interferometric reflectometry (GNSS-IR) is a new type of microwave remote sensing technology that can measure soil moisture content (SMC). GNSS-IR soil moisture retrieval methods based on the satellite signal-to-noise ratio (SNR) and triple-frequency signal combination have the following shortcomings: SNR does not always exist in the original GNSS file, and the number of triple-frequency signal observation satellites is small, resulting in GNSS-IR soil moisture observation time resolution being low. Based on the above problems, in this study, we constructed a soil moisture inversion method based on multisatellite dual-frequency combined multipath error is proposed: the multipath error calculation model of dual-frequency carrier phase (L4 Ionosphere Free, L4_IF) and dual-frequency pseudorange (DFP) without ionospheric effect is constructed. We selected the data of the five epochs before and after the time point of the effective satellite period to construct the multipath error model and error equation, and we solved the delay phase for soil moisture retrieval. We verified the method using Plate Boundary Observatory (PBO) P041 site data. The results showed that the Pearson correlation coefficients (R) of L4_IF and DFP methods at P041 station are 0.97 and 0.91, respectively. To better verify the results’ reliability and the proposed method’s effectiveness, the soil moisture data of the MFLE station about 210 m away from P041 station are used as the verification data in this paper. The results showed that the delay phase solved by multipath error and soil moisture strongly correlate. Pearson correlation coefficients (R) of L4_IF and DFP methods at MFLE station are 0.93 and 0.86, respectively. In order to improve the inversion accuracy of GNSS-IR soil moisture, this paper constructs the prediction model of soil moisture by using the linear regression (ULR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), and evaluates the accuracy of each model. The results showed that the soil moisture retrieval method based on multisatellite dual-frequency combined multipath error can replace the traditional retrieval method and effectively improve the time resolution of GNSS-IR soil moisture estimation. To perform highly dynamic monitoring of soil moisture, higher retrieval accuracy can only be obtained with a small epoch multipath error.

An improved snow depth retrieval method with adaptive noise reduction for GPS/GLONASS/Galileo/BDS multi-frequency signals

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique is an effective method of monitoring snow depth. The detrended signal-to-noise ratio (dSNR) series is analyzed by a Lomb–Scargle periodogram (LSP) to extract the characteristic frequency, which can be converted to the snow depth. However, the dSNR data are greatly affected by noise in the observation environment, which leads to an abnormal characteristic frequency and low accuracy of snow depth retrieval. In order to reduce the influence of noise and to ensure the correct extraction of the characteristic frequency, we present an improved adaptive retrieval method for the multi-constellation retrieval scenario. First, the dSNR sequences are decomposed adaptively into several singular spectrum components (SSCs) with different frequency scales by singular spectrum decomposition. Then, the corresponding SSCs are selected, according to the empirical scope of snow depth, to reconstruct the ‘pure’ dSNR series. Finally, the reconstructed signals are analyzed by LSP to derive the characteristic frequency, in order to obtain the snow depth. Multi-GNSS observations of site SG27 and site P351 from the plate boundary observation network in a representative period from winter 2019 to spring 2020 were used to validate the proposed method. The snow depths were estimated from individual signals, individual constellations and multi-GNSS combination using both the traditional and improved methods. The experimental results show that compared with the traditional method, the snow depth trend of the improved method is more consistent with the measured snow depth trend, especially in the early stage of snowfall. Furthermore, the proposed method shows a universal applicability to various signals of GPS, GLONASS, Galileo and BDS and the retrieval accuracy of all signals is improved to different degrees. When using multi-GNSS combination signals, the mean bias and root mean square error (RMSE) of multi-GNSS snow depth retrieval at site SG27 are improved from 4.6 and 6.2 cm to 4.2 and 5.4 cm, respectively. The mean bias and RMSE at site P351 are improved from 10.5 and 12.4 cm to 9.5 and 11.5 cm, respectively.

Hourly sea level height forecast based on GNSS-IR by using ARIMA model

ABSTRACT Multi-satellite and Multi-Signal-to-Noise Ratio (SNR) types provide more basic data for the monitoring of sea level height by Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. Few studies predict sea level height with high temporal resolution. This study proposes a theory based on GNSS-IR technology and Auto Regressive Integrated Moving Average (ARIMA) model. Taking the MAYG station as an example, the process of sea level height prediction and the determination of the best prediction scheme are shown. First, the sea level height monitored by the GNSS-IR technology is averaged hour by hour as the sea level height at the instantaneous moment. A linear interpolation method is used to compensate for the missing sea level time series. Then, the number of days to participate in the prediction of GNSS-IR sea level height and its forecast are selected for the experiment of predicting tide level. In the tide level prediction study from DOY125 to DOY153, the results of predicting the future 24 h, 48 h, and 72 h from one tide level period (29 days) illustrate that the accuracy of predicting the sea level height at 24 h is the best (R: 0.967, RMSE: 0.270 m, ME: −0.151 m), and the predicted tide level is highly consistent with the tide level measured at the tide gauge station. In addition, experiments reveal that the prediction accuracy gradually decreases with the increase of prediction time. This study verifies the feasibility of the hour-by-hour sea level prediction theory combined with the GNSS-IR technology and the ARIMA model. It proves that the theory can carry out continuous prediction of sea level, further promoting the breakthrough and transformation of GNSS-IR technology to the application level.

In situ mesoscale soil moisture content monitoring based on global navigation satellite system interferometric reflectometry and ensemble modeling

The existing point-scale or large-scale soil moisture content (SMC) monitoring techniques cannot entirely satisfy the requirements of in situ SMC monitoring for agricultural purposes. We proposed a framework for in situ mesoscale SMC monitoring based on global navigation satellite system interferometric reflectometry (GNSS-IR) and ensemble modeling technique. The framework consists of a data collecting and preparing system and an SMC retrieval system. The amplitude, phase, and detection depth, which are the GNSS-IR signal-to-noise ratio parameters, were positively and negatively correlated with the volumetric SMC data, respectively. In the SMC retrieval stage, five kinds of models including linear regression, multilinear regression, k-neighbor regressor, support vector regressor, and random forest (RF) was tested as the first-order model. Two kinds of swarm optimization algorithms including sparrow search algorithm (SSA) and particle swarm optimization (PSO) were examined for models’ hyper-parameter optimization. The results show that the RF performed best and had a coefficient of determination (R2) of 0.798, a root mean square error (RMSE) of 0.043 cm3 / cm3, and a mean absolute error (MAE) of 0.034 cm3 / cm3 for the validation set. Both the SSA and PSO are effective for models’ optimization. After input variable selection, the second-order ensemble RF model outperformed the first-order RF model and had an R2 of 0.819, an RMSE of 0.040 cm3 / cm3, and an MAE of 0.031 cm3 / cm3 for the validation set. The proposed framework is potentially valuable for popularization because of its cost-effectiveness and high accuracy.

GNSS-IR Snow Depth Retrieval Based on the Fusion of Multi-Satellite SNR Data by the BP Neural Network

Compared with previous snow depth monitoring methods, global navigation satellite system-interferometric reflectometry (GNSS-IR) technology has the advantage of obtaining continuous daily observation data, and has great application potential. However, since GNSS satellites are in motion, their position in the sky is constantly varying, and the Fresnel reflection areas about the receiver in different periods alter accordingly. As a result, the retrieving results obtained from different GNSS satellites, and data sets collected in different periods, fluctuate considerably, making the traditional single-satellite-based GNSS-IR retrieving method have limitations in accuracy and reliability. Therefore, this paper proposed a novel GNSS-IR signal-to-noise ratio (SNR) retrieving snow depth method for fusing the available GNSS-IR observations to obtain an accurate and reliable result. We established the retrieval model based on the backpropagation algorithm, which makes full use of the back propagation (BP) neural network’s self-learning and self-adaptive capability to exploit the degree of contribution of different satellites to the final results. Then, the SNR observations of the global positioning system (GPS) L1 carrier from the Plate Boundary Observation (PBO) site P351 were collected to experiment for validation purposes. For all available GPS L1 carrier data, the snow depth values retrieved for each satellite were first obtained by the existing single-satellite-based GNSS-IR retrieval method. Then, four groups of comparison results were acquired, based on the multiple linear regression model, random forest model, mean fusion model, and the proposed BP neural network model, respectively. Taking the snow depth in-situ data provided by snow telemetry (SNOTEL) as a reference, the root mean squared error (RMSE) and mean absolute error (MAE) of the proposed solution are 0.0297 m and 0.0219 m, respectively. Furthermore, the retrieving results are highly consistent with the measured data, and the correlation coefficient is 0.9407.

Measuring Soil Moisture With Refracted GPS Signals

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 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (or, 1.3%) when SM is between 0.272 and 0.489 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . 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.

Toward Terrain Effects on GNSS Interferometric Reflectometry Snow Depth Retrievals: Geometries, Modeling, and Applications

Accurate and high temporal-spatial resolution snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has become a popular topic. The terrain is a significant issue affecting GNSS-IR snow depth retrieval. This study explores detailed information about the problems and new solutions to this issue. The main conclusions include but are not limited to the following aspects: 1) Reflections from Below the Antenna (RBA) is the primary geometrical relationship of the GNSS multipath signal for snow depth retrieval; 2) The proposed GSnow_TERR model considers the terrain effects by incorporating the surface tilt angle (γ) into the derivative of the multipath relative phase (φ) with respect to the satellite elevation angle ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> ). Unlike the previous region-by-region solutions, the new model has a definite physical meaning which considers the γ as an independent non-negligible variable; 3) The new model performs well for repeatable and non-repeatable GNSS tracks. The former agrees with the PBO H2O product with r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.97 and a slightly 1 ~ 2 cm improvement in the RMSE. The latter agrees with the baseline two-step clustering method for GLONASS, Galileo, and BDS, with r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.96 and RMSD = 1.11 cm. The rate of data utilization increases by a mean value of 38.21% for non-repeatable tracks. The performance of the model in practical applications is consistent with theoretical analysis. The findings of this study provide a valuable reference for future GNSS-IR snow depth research and applications.

GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data

Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network

ABSTRACT Soil moisture is a vital surface physical quantity in studying the earth’s ecology. It plays a crucial role in the hydrological cycle, crop yield estimation, and ecological monitoring. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology inversion to obtain high accuracy soil moisture is a hot topic of current research. However, due to the limited available sites, it’s difficult to obtain an extensive and continuous range of soil moisture based on this technique. It is necessary to build algorithms for encryption based on known sites’ data, combined with the corresponding geographic environmental elements. This paper extracted the surface environmental factors affecting soil moisture using high-precision optical remote sensing images. The contribution of each surface environmental element to the soil moisture inversion was analysed using back propagation (BP) neural network optimized by the genetic algorithm (GA). Based on this, ten surface environmental elements (latitude and longitude information, precipitation, temperature, land cover type, normalized difference vegetation index (NDVI), elevation, slope, slope direction, and shading) were identified as critical factors, and a multi-data fusion soil moisture inversion model was constructed. The results showed that the constructed model could better describe the relationship between soil moisture and these elements, and the Pearson correlation coefficient R reached 0.8724, and the RMSE was 0.0346 cm3 cm−3. GNSS-IR technology provides an effective technical means for inversing soil moisture over a large area with high spatial and temporal resolution.

Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach

This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasquère, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios.

High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.

Correction to: Development of a GNSS‑IR instrument based on low‑cost positioning chips and its performance evaluation for estimating the reflector height

Global Navigation Satellite System interferometric reflectometry (GNSS-IR) can be used to monitor a series of geophysical parameters in a cost-effective manner with high temporal resolution. The technique makes use of the simultaneous reception of direct and reflected GNSS signals with a single antenna. Based on the low-cost u-blox M8N chips, a GNSS-IR instrument is developed, which could be used to collect and process GNSS signals automatically and save and transmit generated GNSS data. Details about the instrument are described here for the first time. Then, the recorded SNR observation characteristics are analyzed by comparing three in-situ SNR sequences, which are simultaneously collected by the instrument with a low-cost patch antenna and a geodetic antenna and by a geodetic GNSS receiver with a geodetic antenna. Based on the developed function relating the peak power spectral density to peak frequency estimation error of the low-cost instrument, a weighting method is proposed to fuse multiple estimations of the reflector height to improve the estimation accuracy of the GNSS-IR-based reflector height. The performances of the developed low-cost instrument and the proposed reflector height estimation method are evaluated using a data set collected in Xinxiang City, Henan, China, over 6 days. The results show that there exists good agreement between the instrument-based reflector height estimates and the ground-truth estimates, with root-mean-square errors of 1.1 cm and 0.4 cm for the normal average and the proposed weighted average results, respectively, when the antenna height is in the range of 0.65 m to 2.15 m and the reflecting surface is flat, silty loam soil ground.

A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

Snow depth estimation based on GNSS-IR cluster analysis

As the most influential factor of the cryosphere, snow is the weathervane of climate change. Real-time and accurate snow monitoring data play an important role in climate-change indication, water resource management, disaster prevention, and mitigation. As traditional snow monitoring cannot meet the current requirements, since 2007, the signal-to-noise ratio data has been used for snow-depth inversion. The accuracy of the long time-series global navigation satellite system interferometric reflectometry (GNSS-IR) is not high, although it is significantly better compared to the previous methods. When the ground is covered by shallow snow or there is no snow, the snow-depth inversion is affected by the vegetation and snow layer, lowering the reliability of the Lomb–Scargle spectrum (LSP) analysis, and reducing the snow-depth inversion accuracy. To address the instability of the LSP results, in this study, the dynamic clustering algorithm is used for screening the PSD of the LSP results, and the influence of the signal penetration is eliminated. The average peak of the frequency based on multi-satellite LSP is obtained, and finally, the Grubbs criterion is utilized for improving the reliability of the results. The data of the Altay GNSS snow monitoring station at Altay in Xinjiang and the plate boundary observation SG27 and P351 sites are used as the research data, and a representative time period for the snow depth is selected. The inversion snow depth of the traditional GNSS-IR method are compared with those of the improved GNSS-IR. The experimental results demonstrate that improved GNSS-IR results had better match with the measured snow depth.