Global Positioning System Interferometric Reflectometry Research Articles

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.

A GPS-IR Method for Retrieving NDVI From Integrated Dual-Frequency Observations

The global positioning system interferometric reflectometry (GPS-IR) method has the advantage of acquiring observations continuously in all weather conditions, which has great application potential in vegetation remote sensing. However, L-band electromagnetic wave signals are susceptible to various environmental factors, resulting in deviations in GPS-IR observation data at certain times. The accuracy and reliability of the vegetation index retrieving results are challenging to achieve by using single-frequency GPS data. This letter proposed a novel method to combine dual-frequency data for retrieving normalized difference vegetation index (NDVI). We integrated the multipath observations based on the theory of information entropy. Subsequently, a unary linear regression model was established to retrieve the NDVI from the calculated normalized microwave reflection index (NMRI). For validation purposes, the comparative analysis was conducted between the proposed model and the previous single-frequency NDVI retrieving model in terms of retrieval accuracy, based on the continuous observation data acquired by four GPS reference stations in the past five years. The experimental results indicated that the proposed model is available for retrieving the NDVI, with the correlation coefficient of 0.749–0.815 and the root mean square error (RMSE) of 0.056–0.081. Compared with the results acquired by the previous single-frequency NDVI retrieving model, the correlation coefficient of the retrieved NDVI was increased by an average of 18.5%, and the RMSE was reduced by 30.3%. The proposed method in this letter helps further improve the accuracy and continuity of NDVI observation data in some local areas, which contributes to grasping the growth status of vegetation comprehensively.

Estimation and Validation Study of Soil Moisture Using GPS-IR Technique Over a Tropical Region: Variability of SM With Rainfall and Energy Fluxes

Soil Moisture (SM) data play an important role in different fields of research like hydrology, agriculture, climatology, etc. In this article, global positioning system interferometric reflectometry technique was used to estimate SM. Estimated SM data have been validated and compared with collocated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> probe, Soil Moisture and Ocean Salinity (SMOS) satellite measurements, ECMWF ERA-5, and NASA Global Land Data Assimilation System (GLDAS); the former shows a good correlation of 0.98 but the magnitudes of SMOS, ERA-5 are overestimated and GLDAS data are underestimated. Variability of SM with rainfall and energy fluxes like latent and sensible heat fluxes from ERA-5 and GLDAS data are investigated. Observed SM values are positively correlated with rainfall during the study period. Seasonal variations of SM with rainfall in different monsoons are clearly noticed. Latent heat fluxes are more during spring, summer months and positively correlated with rainfall, whereas sensitive heat fluxes show negative correlation.

Novel Quantification of Shallow Sediment Compaction by GPS Interferometric Reflectometry and Implications for Flood Susceptibility

AbstractEstimates of flood susceptibility and land loss in the world's coastal regions depend on our knowledge of sea level rise (SLR) from increases in ocean mass and volume, as well as knowledge of vertical land motion. Conventional approaches to the latter include tide‐gauge and Global Positioning System (GPS) measurements relative to well‐anchored monuments few meters below the surface. However, in regions of rapid Holocene sedimentation, compaction of this material can add a significant component to the surface lowering. Unfortunately, this process has been difficult to quantify, especially for the shallowest material above the monument. Here we use a new technique, GPS interferometric reflectometry, to estimate the rate of this process in the Mississippi Delta and the eastern margin of the North Sea. We show that the rate of shallow compaction is comparable to or larger than the rate of global SLR, adding 35% and 65%, respectively, to the rate of relative SLR by 2100.

Global Positioning System interferometric reflectometry (GPS-IR) measurements of ground surface elevation changes in permafrost areas in northern Canada

Abstract. Global Positioning System interferometric reflectometry (GPS-IR) is a relatively new technique which uses reflected GPS signals to measure surface elevation changes to study frozen-ground dynamics. At present, more than 200 GPS stations are operating continuously in the Northern Hemisphere permafrost areas, which were originally designed and maintained for tectonic and ionospheric studies. However, only one site in Utqiaġvik, Alaska (formerly Barrow), was assessed to be usable for studying permafrost by GPS-IR. Moreover, GPS-IR has high requirements on the ground surface condition, which needs to be open, flat, and homogeneous. In this study, we screen three major GPS networks in Canada and identify 12 out of 38 stations located in permafrost areas as useful ones where reliable GPS-IR measurements can be obtained. We focus on the five Canadian Active Control System stations and obtain their daily GPS-IR surface elevation changes. We find that the ground surface subsided in Alert, Resolute Bay, and Repulse Bay respectively by 0.61±0.04 cm yr−1 (2012–2018), 0.70±0.02 cm yr−1 (2003–2014), and 0.26±0.05 cm yr−1 (2014–2019). At the other two sites of Baker Lake and Iqaluit, the trends are not statistically significant. The linear trends of deformation were negatively correlated with those of thaw indices in Alert, Resolute Bay, and Repulse Bay. Furthermore, in Resolute Bay, we also find that the end-of-thaw elevations during 2003–2012 were highly negatively correlated with the square root of thaw indices. This study is the first one using multiple GPS stations to study permafrost by GPS-IR. It highlights the multiple useful GPS stations in northern Canada, offering multi-year, continuous, and daily GPS-IR surface deformation, which provides new insights into frozen-ground dynamics at various temporal scales and across a broad region.

Global positioning system interferometric reflectometry for accurate tide gauge measurement: Insights from South Beach, Oregon, United States

Global positioning system (GPS) stations located along coastal areas have the ability to measure tide gauge records by reflected signal reception from the sea water surface. In this study we used the GPS signal-to-noise ratio data from the GPS station (44.6257 ⁰N, 124.0455 ⁰W) located at South Beach, Oregon, United States, to estimate the tide gauge records using seasonal measurements. First, we derived the tide gauge record from the station and used traditional tide gauge data from the National Water Level Observation Network (NWLON) sentinel station (Station ID: 9435380) located in Oregon for validation purposes because it was closest to the GPS station. Our results show that the GPS tide gauge and NWLON tide gauge correlate well with the correlation coefficient of 0.942 and the root mean square of their residuals was about 12.90 cm. The corresponding tide gauge prediction by auto-regressive moving average and singular spectrum analysis models are evaluated for their effectiveness over the station. The comparative analysis demonstrates that the GPS tide gauge has improved correlation with auto-regressive moving average predicted tide gauge (correlation coefficient of ~0.981 and root mean square of ~4.80 cm), and singular spectrum analysis predicted tide gauge (correlation coefficient of ~0.998 and root mean square of ~0.88 cm) compared to the NWLON tide gauge (correlation coefficient of ~0.942 and root mean square of ~12.90 cm) values. We believe the outcomes from this study contribute to a better understanding of the numerical modeling of tide gauge records as well as other measurements based on reflectometry techniques.

INVERSION OF SOIL MOISTURE BY WAVELET ANALYSIS AND MULTI-STAR FUSION

Abstract. Soil moisture content is an important parameter in hydrology, meteorology and agriculture, and it is of great significance for meteorological forecast, flood disaster and water resource cycle. Global Positioning System Interferometric Reflectometry (GPS-IR) is a new remote sensing technique with low cost, high efficiency and high resolution.. Using GPS-IR to invert soil moisture, snow depth, sea level and other aspects compared with traditional measurements, greatly reducing the difficulty of measurement and improving the accuracy of estimation. In view of how to efficiently and accurately separate satellite reflection signals and invert soil moisture, this paper considers the time-rate characteristics of wavelet analysis and the advantages of multi-star fusion. This paper proposes a combination of wavelet analysis and multi-star fusion estimation model for the existing research and analysis. First, the GPS satellite straight and reflected signals are separated by wavelet analysis, and then the relative delay phase is obtained by the sine fitting model. The multi-satellite relative delay phase is effectively analyzed and selected by a linear regression model. Finally, the change in soil moisture is estimated by establishing a multi-star fusion model. The feasibility and effectiveness of using wavelet analysis and multiple GPS satellites to estimate soil moisture were compared and analyzed.Take the monitoring data provided by the PBO of the US Sector Boundary Observation Program as an example. Comparative analysis of the feasibility and effectiveness of soil moisture estimation using single or multiple GPS satellites.The results of the two data show that the linear regression equation can better describe the relationship between relative delay phase and soil moisture.Wavelet analysis fully exploits the performance of the identification trend term in the process of satellite reflection signal separation.Using a single satellite for soil moisture inversion, it is difficult to accurately grasp the variation law of soil moisture, and the error of inversion error fluctuates greatly, which is prone to jump phenomenon.Multiple satellites can effectively combine the advantages of each satellite and effectively suppress abnormal jumps.The method of this paper gives full play to the advantages of multiple linear regression models.This model effectively suppresses the transition phenomenon. It combines wavelet analysis and multi-star fusion to effectively suppress abnormal jump values. The estimation error is stable and the inversion accuracy is improved, which provides soil moisture monitoring. An accurate and efficient method.This not only ensures the stability of local errors during the separation of satellite reflected signals, but also effectively suppresses abnormal jumps when estimating a single satellite. A single satellite does not easily affect the inversion process. Therefore, soil moisture estimation can be feasible and effective as a linear event estimate.

Snow Depth Estimation on Slopes Using GPS-Interferometric Reflectometry.

Snow is one of the most critical sources of freshwater, which influences the global water cycle and climate change. However, it is difficult to monitor global snow variations with high spatial–temporal resolution using traditional techniques due to their costly and labor-intensive nature. Nowadays, the Global Positioning System Interferometric Reflectometry (GPS-IR) technique can measure the average snow depth around a GPS antenna using its signal-to-noise ratio (SNR) data. Previous studies focused on the use of GPS data at sites located in flat areas or on very gentle slopes. In this contribution, we propose a strategy called the Tilted Surface Strategy (TSS), which uses the SNR data reflected only from the flat quadrants to estimate the snow depth instead of the conventional strategy, which employs all the SNR data reflected from the whole area around a GPS antenna. Three geodetic GPS sites from the Plate Boundary Observatory (PBO) project were chosen in this experimental study, of which GPS sites p683 and p101 were located on slopes with their gradients up to 18% and the site p025 was located on a flat area. Comparing the snow depths derived with the GPS-IR TSS method with the snow depth results provided with the GPS-PBO, i.e., GPS-IR with the conventional strategy, the Snowpack Telemetry (SNOTEL) network measurements and gridded Snow Data Assimilation System (SNODAS) estimates, it was found that the snow depths derived with the four methods had a good agreement, but the snow depth time series with the GPS-IR TSS method were closer to the SNOTEL measurements and the SNODAS estimates than those with GPS-PBO method. Similar observations were also obtained from the cumulative snowfall time series. Results generally indicated that for those GPS sites located on slopes, the TSS strategy works better.

GPS Interferometric Reflectometry Reveals Cyclic Elevation Changes in Thaw and Freezing Seasons in a Permafrost Area (Barrow, Alaska)

AbstractGround surface over permafrost area undergoes seasonal subsidence and uplift caused by the annual thawing and freezing of the active layer. Applying the Global Positioning System (GPS) interferometric reflectometry technique to the signal‐to‐noise ratio data collected by a continuously operating GPS station in a permafrost area in Barrow, we retrieve the daily surface elevation changes on snow‐free days over a decade (2007–2016). Among these years, 2016 had the longest snow‐free season, offering the longest and most complete records of elevation changes. Use this year as an example, we show that the ground subsided in thaw season and then uplifted from September to early November (freezing season) with an amplitude of 5.1 ± 0.2 cm. We also develop a composite model that includes both thaw and freeze indices to characterize the cyclic movements. Our composite model effectively explains the observed cyclic elevation changes and could be used in other permafrost studies.

A Case Study for Inferring Freshwater Lake Ice Thickness by GPS Interferometric Reflectometry

The potential for inferring freshwater lake ice thickness by using the global positioning system (GPS) is explored. In particular, lake ice thickness is estimated by using a nonlinear least squares fitting algorithm. The inputs to this algorithm are GPS signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antennna are between 5 degrees and 25 degrees. A 1-day experiment with a snow-covered frozen lake using GPS satellite PRN 10 shows potential for inferring lake ice thickness by incorporating the GPS-IR model. For this satellite, the average inferred thickness (38.0 cm) slightly underestimates the in situ measurements (39.4 cm +/- 1.3 cm). GPS satellites PRN 2 and PRN 24 are also used in this study. However, their received signals did not provide the necessary information to infer a reasonable lake ice thickness.

Estimating snow water equivalent for a slightly tilted snow-covered prairie grass field by GPS interferometric reflectometry

Snow water equivalent (SWE) measurements are necessary for the management of water supply and flood control systems in seasonal snow-covered regions. SWE measurements quantify the amount of water stored in snowpack; it can be estimated by the product of snow depth and density. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry model (GPS-IR) that incorporates a slightly tilted surface (GPS-IRT). The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. A 1-day experiment with a snow-covered prairie grass field using GPS satellites PRN 15 and PRN 18 shows potential for inferring snow water equivalent using GPS-IRT. For this case study, the average inferred snow depth (12.4 cm) from the two satellite tracks underestimates the in situ measurements (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.085 g∙cm-3) from the two satellite tracks is within the in situ measurement range (0.08 g∙cm-3 ± 0.02 g∙cm-3). Consequently, the average inferred SWE (1.05 g∙cm-2) from the two satellite tracks is within the in situ calculation range (1.40 g∙cm-2 ± 0.36 g∙cm-2). These results are also compared with the GPS-IR model.

Snow Water Equivalent Estimation for a Snow-Covered Prairie Grass Field by GPS Interferometric Reflectometry

The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SWE). The product of snow density and depth provides an estimate of SWE. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antenna are between 50 and 300. A snow-covered prairie grass field experiment shows potential for inferring snow water equivalent using GPS-IR. For this case study, the average inferred snow depth (17.9 cm) is within the in situ measurement range (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.13 g.cm-3) overestimates the in situ measurements (0.08 g.cm-3 ± 0.02 g.cm-3). Consequently, the average inferred SWE (2.33 g.cm-2) also overestimates the in situ calculations (1.38 g.cm-2 ± 0.36 g.cm-2).