Interferometric Reflectometry Research Articles

GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

Global assessment of linear trend and seasonal variations of GNSS-IR sea level retrievals with nearby tide gauges

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) sea level retrievals and tide gauges at 40 globally distributed stations spanning from about 4.5 to 18 years are compared on a site-by-site basis, in terms of noise background, rate and seasonal variations by using the weighted least squares estimation (LSE) along with the Maximum likelihood estimation (MLE). The result shows that monthly GNSS-IR data agree well with tide gauges for most stations except the site Tuktoyaktuk, Canada. The mean correlation is 0.95 and the mean root mean square difference is 2.9 cm, respectively. The discrepancies of rate and seasonal amplitude estimates are within ± 1 cm for most stations. We confirm both the two sea level data exhibit temporal correlation, which has a great effect on the rate uncertainty estimates. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) favor Matérn and first-order autoregressive (AR1) the preferred stochastic model for the daily and monthly mean sea level time series, respectively. Owing to the data span dependence for the rate uncertainty estimates, to get an accuracy of sub-mm/yr in linear rate using the weighted LSE, at least 30 years of data (depends on data quality) is required. We recommend using long time series and a proper stochastic model for the rate estimation of sea level.

Gnssrefl: an open source software package in python for GNSS interferometric reflectometry applications

An open source software package has been developed for Global Navigation Satellite Systems (GNSS) interferometric reflectometry. The gnssrefl package is written in python; it can be installed from the source code, the python packaging index website, or via a docker. It includes modules that download GNSS data and orbit data from global archives. A periodogram is used to retrieve the height of the GNSS antenna over the reflecting surface using signal to noise ratio data. Signals from the Global Positioning System, Glonass, Galileo, and Beidou constellations are supported. Modules are provided to estimate volumetric water content of soil, snow depth/accumulation, and water level. Utilities for mapping and assessing reflection zones and determining the maximum resolvable height are available.

Quality control and improvement of GNSS-IR soil moisture robust inversion model

Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) technology can realize continuous and dynamic monitoring of soil moisture at areas. The GNSS signal is highly susceptible to the influence of external factors, causing anomalous terms in the process of characteristic parameters extraction. In this study, an integrated outlier detection method is proposed. This method first detects outlier based on Inter-Quartile Range, Grubbs test and Hampel filter outlier detection methods for the characteristic parameters data, and derives the outlier location by complementing the detection results with each other and corrected by rainfall data. Then, four kinds of outlier repair methods, namely, moving average, locally weighted primary linear regression, locally weighted quadratic linear regression, and robust locally weighted quadratic linear regression, were used to repair and filter out the optimal data according to the location of the outlier. Finally, three machine learning methods, namely, Bagging Tree, Support Vector Machine (SVM), and Gaussian Process Regression (GPR), were combined to build a soil water content inversion model and evaluate the accuracy. The results show that the proposed comprehensive outlier detection method in this study can accurately detect the location of outlier. The correlation between the characteristic parameters and in-situ soil moisture can be effectively improved after remediation treatment. The outlier repair helped to improve the inversion accuracy of the soil moisture inversion model, and the R2 of the soil moisture inversion model increased by 13.21 % to 27.08 % (mean 18.25 %), the RMSE decreased by 12.97 % to 23.61 % (mean 18.16 %). The comprehensive outlier detection and repair method proposed in this study can provide reference for the quality control of input data before the establishment of GNSS-IR model, and effectively improve the inversion accuracy of GNSS-IR soil moisture inversion model.

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.

Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals

ABSTRACT Monitoring snow depth variations aligned with the seasonal cycle is crucial for studies on climate change and its impacts due to global warming. Especially in terms of snow hydrology, the determination and continuous monitoring of snow depth to determine snow water equivalent is a priority in climate, water science, drought, flood, and inundation prediction studies, particularly in the water cycle. Recently, the so-called Global Navigation Satellite Systems – Interferometric Reflectometry (GNSS-IR) method enables to extract environmental radiometric and geometric characteristics of surface where the signals transmitted from the satellites reflect. The reflected signal follows an extra path than the direct one and causes a major error source for accurate point positioning but a useful tool for sensing the environment. However, outliers remain a challenge in long-term GNSS-IR estimations. In this study, we proposed a median-based robust outlier detection (ROD) approach for identifying outliers in long-term GNSS-IR snow depth estimations. To validate our approach, we analysed 5-year GNSS L1 SNR and L2 SNR data from 1 January 2015 to 31 December 2019 provided by AB33 and AB39 GNSS stations in Alaska, U.S.A. using the GNSS-IR method. We validated GNSS-IR estimations using snow depth measurements from the Coldfoot and Fort Yukon stations in the SNOTEL network. Applying ROD to long-term snow depth estimates increased the highest correlation from 91.58% to 94.93%, and reduced the lowest RMSE from 8.6 cm to 6.7 cm. In addition, the improvement rates calculated to assess the contribution of ROD to the results showed improvements of up to 6.9% in correlation and 26.1% in RMSE. Overall, the results demonstrate that ROD can be effectively used to detect outliers in long-term GNSS-IR snow depth time series.

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

For the past 20 years, Global Navigation Satellite System reflectometry (GNSS-R) technology has successfully shown its potential for remote sensing of the Earth’s surface, including ocean and land surfaces. It is a multistatic radar that uses the GNSS signals reflected from the Earth’s surface to extract land and ocean characteristics. Because of its numerous advantages such as low cost, multiple signal sources, and all-day/weather and high-spatiotemporal-resolution observations, this new technology has attracted the attention of many researchers. One of its most promising applications is GNSS-R ocean altimetry, which can complement existing techniques such as tide gauging and radar satellite altimetry. Since this technology for ocean altimetry was first proposed in 1993, increasing progress has been made including diverse methods for processing reflected signals (such as GNSS interferometric reflectometry, conventional GNSS-R, and interferometric GNSS-R), different instruments (such as an RHCP antenna with one geodetic receiver, a linearly polarized antenna, and a system of simultaneously used RHCP and LHCP antennas with a dedicated receiver), and different platform applications (such as ground-based, air-borne, or space-borne). The development of multi-mode and multi-frequency GNSS, especially for constructing the Chinese BeiDou Global Navigation Satellite System (BDS-3), has enabled more free signals to be used to further promote GNSS-R applications. The GNSS has evolved from its initial use of GPS L1 and L2 signals to include other GNSS bands and multi-GNSS signals. Using more advanced, multi-frequency, and multi-mode signals will bring new opportunities to develop GNSS-R technology. In this paper, studies of GNSS-R altimetry are reviewed from four perspectives: (1) classifications according to different data processing methods, (2) different platforms, (3) development of different receivers, and (4) our work. We overview the current status of GNSS-R altimetry and describe its fundamental principles, experiments, recent applications to ocean altimetry, and future directions.

Facilitated interferometric reflectometry evaluation and its application in monitoring three typhoon storm surges in Hong Kong with multi-GNSS constellation

Facilitated interferometric reflectometry evaluation and its application in monitoring three typhoon storm surges in Hong Kong with multi-GNSS constellation

Ship-borne wave gauge using GNSS interferometric reflectometry

ABSTRACT To obtain wide coverage and frequent wave observations, low-cost methods to observe significant wave heights and wave periods from moving vessels were developed by using Global Navigation Satellite System (GNSS) interferometric reflectometry. Because GNSS signals reflected from the sea surface are always delayed respect to those received directly from the satellite, and this delay depends on the distance between the antenna and the sea surface, the amplitude of the received GNSS signals, in which the direct and reflected signals are mixed, shows interferometric variations in the presence of ocean waves. High sampling rate observations of the GNSS signal amplitude can be used to determine the significant wave period and wave height by referring to a look-up table estimated from simulated received GNSS signals based on a realistic wave spectrum. The methods were applied to actual 20-Hz GNSS observations on a ferryboat and qualitatively good estimations of both significant wave heights and wave periods were obtained. Because the look-up tables can be further modified using in situ wave observations, these estimations can be quantitatively improved in the future.

GNSS-R snow depth retrieval algorithm based on PSO-LSTM

The global navigation satellite system (GNSS)-interferometric reflectometry technique has been applied to retrieve snow depth, which has a high potential for application. The GNSS reflectometry classical algorithm retrieves the snow depth by extracting the frequency of the multipath signal and substituting it into an empirical formula. However, the retrieval errors of high and low snow depths are large due to the influence of factors such as surface vegetation and terrain environment. In this paper, we propose a snow depth retrieval algorithm based on a particle swarm optimized long short-term memory (PSO-LSTM) neural network. The algorithm extracted three characteristic parameters (frequency, amplitude, and phase) from the signal-to-noise ratio (SNR) data as inputs, and optimized the LSTM hyperparameters by the PSO algorithm to improve the retrieval accuracy for low snow depths and snow depths close to the antenna. The snow depth retrieval results of global positioning system L1 band SNR data collected from the P351 station in 2022 and AB33 station in 2017 were evaluated in this paper. The snow depth retrieval results of the PSO-LSTM algorithm for P351 station were in high agreement with the snow depth data provided by the snowpack telemetry network; the coefficient of determination ( R2 ) reached 0.986, and the root mean square error (RMSE) and mean absolute error (MAE) were 7.30 cm and 4.94 cm, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 53.0% and 30.4% for the retrieval results of snow depths below 15 cm at the P351 station, and by 76.8% and 84.4% for the retrieval results of snow depths above 117 cm from the 1st day to the 137th day, respectively. Similarly, the RMSE, MAE, and R2 for the 2017 retrieval results at AB33 station were 5.90 cm, 4.25 cm, and 0.965, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 47.9% and 33.0% for the retrieval results of snow depths below 15 cm at the AB33 station, and by 75.4% and 82.3% for the retrieval results of snow depths above 56 cm from the day 46 to day 120. In addition, the snow depth retrieval algorithm was proposed in this paper does not require antenna height and empirical formulas to realize snow depth retrieval, and at the same time, the algorithm effectively improved the retrieval accuracy for both high and low snow depths with strong robustness.

Island-based GNSS-IR network for tsunami detecting and warning

Deep-sea tsunami detection relies on Deep-ocean Assessment and Reporting of Tsunamis (DART), GNSS buoys, and cabled Ocean-Bottom Pressure (OBP) gauges, which are very expensive and difficult to maintain, and often suffer from vandalism or negligent damage. Here, we exploit the potential of establishing a less expensive and more robust island-based geodetic network for tsunami detecting, source reconstruction and warning. The network locates at the coastline of islands and uses a new technique: GNSS Interferometric Reflectometry (GNSS-IR). GNSS-IR retrieves sea levels from combination of the direct and reflected signals from the sea surface sent by satellites. To test the feasibility and efficiency of such a new geodetic network, we use the South China Sea region as an example, and compare its performance in reconciling the variable slip distribution on the Manila megathrust with the previously designed deep-sea monitoring system, i.e., DARTs and planned cable-based OBP gauges. We find that the newly designed GNSS-IR network could work equally well as the cabled OBP network in detecting tsunamis if the stations are built in strategically chosen locations. Combining GNSS-IR with a Kalman filter approach, we demonstrate that carefully situated coastal GNSS stations at global remote deep-ocean islands could function similarly to conventional tide gauges but with advantages of simultaneously measuring relative sea-level and land-height changes, meanwhile suffering lower risk from damaging sea-level events and potential vandalism.

Real‐Time Water Levels Using GNSS‐IR: A Potential Tool for Flood Monitoring

AbstractGlobal Navigation Satellite System Interferometric Reflectometry (GNSS‐IR) using low‐cost antennas is a practical solution for monitoring water levels from rivers, lakes and seas that does not require submerging any instruments in water. Here we present a novel method for obtaining real‐time water levels using multiple low‐cost antennas that we validate by comparing with measurements from a co‐located pressure gauge at two sites with variable tides. Additionally, we use survey measurements to show that there is a site‐dependant mean bias in GNSS‐IR measurements up to a few centimeters, but this mean bias can be effectively removed by using a correction for the effect of tropospheric delay. We conclude that GNSS‐IR water level sensors could be a powerful tool for real‐time applications such as flood or storm surge monitoring and water resource management, as well as for improving the spatial coverage of sensors in remote regions.

ArcTiCA: Arctic tidal constituents atlas

Tides in the Arctic Ocean affect ocean circulation and mixing, and sea ice dynamics and thermodynamics. However, there is a limited network of available in situ tidal coefficient data for understanding tidal variability in the Arctic Ocean; e.g., the global TICON-3 database contains only 111 sites above 60°N and 21 above 70°N. At the same time, the presence of sea ice and latitude limits of satellite altimetry complicate altimetry-based retrievals of Arctic tidal coefficients. This leads to a reliance on ocean tide models whose accuracy depend on having sufficient in situ data for validation and assimilation. Here, we present a comprehensive new dataset of tidal constituents in the Arctic region, combining analyses of in situ measurements from tide gauges, ocean bottom pressure sensors and GNSS interferometric reflectometry. The new dataset contains 914 measurement sites above 60°N and 399 above 70°N, with each site being quality-assessed and expert guidance provided to help maximise the usage of the dataset. We also compare the dataset to recent tide models.

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.

Water level measurements using reflected GNSS signals

GNSS Interferometric Reflectometry (GNSS-IR) is a method that can be used to measure water levels. The frequency of the interference pattern created by direct and reflected GNSS signals is used to estimate the height of the GNSS antenna above the reflecting surface. In principle each rising and setting satellite arc that reflects off the water can be used, yielding ~350 water level measurements per day at sites that track the four major constellations and have a good view of the water. Two examples of the GNSS-IR method are presented, one at a coastal site in Australia and the other from the Ems river in Germany. Each site has collocated traditional tide gauge instrumentation. Open source GNSS-IR software is used to analyze the GNSS data from each site. Correlations between GNSS-IR with the traditional gauge are shown to be better than 0.99.

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.

Determination of optimum parameters for measuring sea level using GNSS Interferometric Reflectometry: A Case study of Port Klang, Malaysia

This paper presents a new approach of measuring sea level height using Global Navigation Satellite Systems GNSS Interferometric Reflectometry (GNSS-IR) by method of Signal to Noise Ratio (SNR) data analysis from a single geodetic receiver. The study location was selected at Port Klang Tide Gauge (PKTG) station, Malaysia. The purpose of this study was to identify the optimum criteria for the parameters involved to get the best accuracy of reflected heights. The accuracy of reflected height derived from SNR information is strongly dependent on the value of parameters used for quality control. The parameters involved shall achieve the best possible frequency correlated with the sea surface. There are 5 parameters were identified such as estimation of the satellite's azimuth and elevation angle, identify noise frequency range, selection of frequency amplitude power, and calculation of the peak frequency to noise ratio using the Lomb-Scargle periodogram (LSP). The optimum value of each parameter is important to get accurately selected reflector heights and then produce a quality local sea level time series.