GPS Interferometric Reflectometry Research Articles

Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai–Tibet Plateau

Abstract. Ground surface elevation changes, soil moisture, and snow depth are all essential variables for studying the dynamics of the active layer and permafrost. GPS interferometric reflectometry (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost areas. However, its applicability to estimating soil moisture in permafrost regions has not been assessed. Moreover, these variables were usually measured separately at different sites. Integrating their estimates at one site facilitates the comprehensive utilization of GPS-IR in permafrost studies. In this study, we run simulations to elucidate that the commonly used GPS-IR algorithm for estimating soil moisture content cannot be directly used in permafrost areas, because it does not consider the bias introduced by the seasonal surface elevation changes due to active layer thawing. We propose a solution to improve this default method by introducing modeled surface elevation changes. We validate this modified method using the GPS data and in situ observations at a permafrost site in the northeastern Qinghai–Tibet Plateau (QTP). The root-mean-square error and correlation coefficient between the GPS-IR estimates of soil moisture content and the in situ ones improve from 1.85 % to 1.51 % and 0.71 to 0.82, respectively. We also propose a framework to integrate the GPS-IR estimates of these three variables at one site and illustrate it using the same site in the QTP as an example. This study highlights the improvement to the default algorithm, which makes the GPS-IR valid in estimating soil moisture content in permafrost areas. The three-in-one framework is able to fully utilize the GPS-IR in permafrost areas and can be extended to other sites such as those in the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in the QTP, which fills a spatial gap and provides complementary measurements to ground temperature and active layer thickness.

Brief Communication: Update on the GPS reflection technique for measuring snow accumulation in Greenland

Abstract. GPS interferometric reflectometry (GPS-IR) is a technique that can be used to measure snow accumulation on ice sheets. The footprint of the method (∼1000 m2) is larger than that of many other in situ methods. A long-term comparison with hand measurements yielded an accuracy assessment of 2 cm. Depending on the placement of the GPS antenna, these data are also sensitive to firn density. The purpose of this short note is to make public GPS-IR measurements of snow accumulation for four sites in Greenland, compare these records with in situ sensors, and make available open-source GPS-IR software to the cryosphere community.

Freeboard height and snow depth observed by floating GPS on land-fast sea ice in Nella Fjord, Antarctica

AbstractAlthough altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.

RESEARCH ON MULTI-STAR NONLINEAR REGRESSION ESTIMATION MODEL OF SOIL MOISTURE BASED ON WAVELET ANALYSIS

Abstract. Soil moisture is an important parameter for studying meteorological, hydrological, and agricultural research. How to obtain accurate soil moisture has become a key issue. The traditional method of obtaining soil moisture has the disadvantages of high cost, complicated operation and limited application range. In recent years, using GPS-IR (GPS-Interferometric Reflectometry) technology to estimate soil moisture has become a Research hot. Traditional GPS-IR technology uses low-order polynomial to separate GPS satellite direct and reflected signals, but the separation effect is poor. And single-star inversion accuracy is low. In order to solve these problems, this paper proposes a multi-star nonlinear regression model based on wavelet analysis.Firstly, the satellite direct reflection signal is fitted by wavelet analysis, then the relative phase delay is solved by the nonlinear least squares method. Finally, the multi-star nonlinear regression model is established to estimate the soil moisture. The experiment used the observation data of the P041 station provided by the US Plate Edge Observation Program PBO in 2012. The results show that the wavelet analysis separates the reflected signal better than the low-order polynomial. The model can fully combine the advantages of wavelet analysis and multi-star fusion inversion, and effectively improve the abnormal jumping phenomenon of single-star. The inversion result is significantly upgrade than the traditional method. When the model combination reached double-star and triple-star, the better results were obtained. The R reached 0.922 and 0.948, respectively. The test results increased by 18.6% and 20.9% compared with the traditional method.

Decadal changes of surface elevation over permafrost area estimated using reflected GPS signals

Abstract. Conventional benchmark-based survey and Global Positioning System (GPS) have been used to measure surface elevation changes over permafrost areas, usually once or a few times a year. Here we use reflected GPS signals to measure temporal changes of ground surface elevation due to dynamics of the active layer and near-surface permafrost. Applying the GPS interferometric reflectometry technique to the multipath signal-to-noise ratio data collected by a continuously operating GPS receiver mounted deep in permafrost in Barrow, Alaska, we can retrieve the vertical distance between the antenna and reflecting surface. Using this unique kind of observables, we obtain daily changes of surface elevation during July and August from 2004 to 2015. Our results show distinct temporal variations at three timescales: regular thaw settlement within each summer, strong interannual variability that is characterized by a sub-decadal subsidence trend followed by a brief uplift trend, and a secular subsidence trend of 0.26 ± 0.02 cm year−1 during 2004 and 2015. This method provides a new way to fully utilize data from continuously operating GPS sites in cold regions for studying dynamics of the frozen ground consistently and sustainably over a long time.

Monitoring Bare Soil Freeze–Thaw Process Using GPS-Interferometric Reflectometry: Simulation and Validation

Frozen soil and permafrost affect ecosystem diversity and productivity as well as global energy and water cycles. Although some space-based Radar techniques or ground-based sensors can monitor frozen soil and permafrost variations, there are some shortcomings and challenges. For the first time, we use GPS-Interferometric Reflectometry (GPS-IR) to monitor and investigate the bare soil freeze–thaw process as a new remote sensing tool. The mixed-texture permittivity models are employed to calculate the frozen and thawed soil permittivities. When the soil freeze/thaw process occurs, there is an abrupt change in the soil permittivity, which will result in soil scattering variations. The corresponding theoretical simulation results from the forward GPS multipath simulator show variations of GPS multipath observables. As for the in-situ measurements, virtual bistatic radar is employed to simplify the analysis. Within the GPS-IR spatial resolution, one SNOTEL site (ID 958) and one corresponding PBO (plate boundary observatory) GPS site (AB33) are used for analysis. In 2011, two representative days (frozen soil on Doy of Year (DOY) 318 and thawed soil on DOY 322) show the SNR changes of phase and amplitude. The GPS site and the corresponding SNOTEL site in four different years are analyzed for comparisons. When the soil freeze/thaw process occurred and no confounding snow depth and soil moisture effects existed, it exhibited a good absolute correlation (|R| = 0.72 in 2009, |R| = 0.902 in 2012, |R| = 0.646 in 2013, and |R| = 0.7017 in 2014) with the average detrended SNR data. Our theoretical simulation and experimental results demonstrate that GPS-IR has potential for monitoring the bare soil temperature during the soil freeze–thaw process, while more test works should be done in the future. GNSS-R polarimetry is also discussed as an option for detection. More retrieval work about elevation and polarization combinations are the focus of future development.

GPS-derived estimates of surface mass balance and ocean-induced basal melt for Pine Island Glacier ice shelf, Antarctica

Abstract. In the last 2 decades, Pine Island Glacier (PIG) experienced marked speedup, thinning, and grounding-line retreat, likely due to marine ice-sheet instability and ice-shelf basal melt. To better understand these processes, we combined 2008–2010 and 2012–2014 GPS records with dynamic firn model output to constrain local surface and basal mass balance for PIG. We used GPS interferometric reflectometry to precisely measure absolute surface elevation (zsurf) and Lagrangian surface elevation change (Dzsurf∕ Dt). Observed surface elevation relative to a firn layer tracer for the initial surface (zsurf − zsurf0′) is consistent with model estimates of surface mass balance (SMB, primarily snow accumulation). A relatively abrupt ∼ 0.2–0.3 m surface elevation decrease, likely due to surface melt and increased compaction rates, is observed during a period of warm atmospheric temperatures from December 2012 to January 2013. Observed Dzsurf∕ Dt trends (−1 to −4 m yr−1) for the PIG shelf sites are all highly linear. Corresponding basal melt rate estimates range from ∼ 10 to 40 m yr−1, in good agreement with those derived from ice-bottom acoustic ranging, phase-sensitive ice-penetrating radar, and high-resolution stereo digital elevation model (DEM) records. The GPS and DEM records document higher melt rates within and near features associated with longitudinal extension (i.e., transverse surface depressions, rifts). Basal melt rates for the 2012–2014 period show limited temporal variability despite large changes in ocean temperature recorded by moorings in Pine Island Bay. Our results demonstrate the value of long-term GPS records for ice-shelf mass balance studies, with implications for the sensitivity of ice–ocean interaction at PIG.

Snow accumulation variability on a West Antarctic ice stream observed with GPS reflectometry, 2007–2017

AbstractLand ice loss from Antarctica is a significant and accelerating contribution to global sea level rise; however, Antarctic mass balance estimates are complicated by insufficient knowledge of surface mass balance processes such as snow accumulation. Snow accumulation is challenging to observe on a continental scale and in situ data are sparse, so we largely rely on estimates from atmospheric models. Here we employ a novel technique, GPS interferometric reflectometry (GPS‐IR), to measure upper (<2 m) firn column thickness changes across a 23‐station GPS array in West Antarctica. We compare the results with antenna heights measured in situ to establish the method's daily uncertainty (0.06 m) and with output from two atmospheric reanalysis products to categorize spatial and temporal variability of net snow accumulation. GPS‐IR is an effective technique for monitoring surface mass balance processes that can be applied to both historic GPS data sets and future experiments to provide critical in situ observations of processes driving surface height evolution.

Cover Image, Volume 3, Issue 6

The cover image, by Kristine M Larson, is based on the Overview GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States, DOI: 10.1002/wat2.1167. image

Validation of GPS-IR Soil Moisture Retrievals: Comparison of Different Algorithms to Remove Vegetation Effects

The GPS interferometric reflectometry (GPS-IR) technique can be used to estimate near-surface soil moisture from signal-to-noise ratio (SNR) data collected with standard geodetic instrumentation. However, the effects of vegetation on GPS-IR soil moisture retrievals must be considered in some environments. In situ soil moisture observations from 11 GPS sites are used to compare the performance of three different retrieval algorithms that represent vegetation effects with different degrees of complexity. A bare-soil retrieval algorithm does not perform well, even at sites where seasonal variations in vegetation water content (VWC) are less than 1 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . The range of volumetric soil moisture (VSM) is too large due to the effects of vegetation on phase of the SNR interferogram, yielding an RMSE between in situ and GPS-IR VSM of 0.055 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> . Errors are reduced by an algorithm that adjusts for vegetation effects using variations in the amplitude of the SNR interferogram. RMSE is 0.038 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> using this algorithm, below the typical limit required for validation of satellite data. This simple vegetation algorithm performs poorly at sites where seasonal variations in VWC are 1 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> or greater. A more complex algorithm, that uses amplitude in conjunction with frequency analysis of the SNR interferogeram to predict vegetation effects, provides acceptable performance at these sites ( RMSE = 0.039 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 additional complexity of this algorithm is only warranted at sites where the simple vegetation algorithm cannot adequately represent the effects of the vegetation fluctuations.

Vegetation Sensing Using GPS Interferometric Reflectometry: Experimental Results With a Horizontally Polarized Antenna

Measurement of vegetation state is required both for modeling and for satellite data validation. GPS Interferometric Reflectometry (GPS-IR) is a passive microwave remote sensing technique that has been applied to several applications, such as sensing soil moisture, snow depth, ocean roughness and wind speed, and vegetation growth. Previous studies proposed that geodetic GPS data could be used to measure vegetation biophysical parameters, for example, vegetation height and vegetation water content (VWC). However, the measurement range and accuracy are limited because the geodetic GPS antenna is designed to suppress reflections. In this paper, the antenna design for vegetation sensing via GPS-IR is discussed. Then, a customized dipole antenna is developed specifically for vegetation sensing. A field experiment is conducted at Boulder, CO, USA to evaluate the performance of the proposed antenna. The GPS metric, which is the normalized signal-to-noise ratio (SNR) amplitude ${A_{{\rm{norm}}}}$ , shows a linear relationship with in situ measurements of VWC over a range of 0—6 kg/m2, which is much greater than the range from the geodetic data (0—1.0 kg/m 2). The sensitivity of ${A_{{\rm{norm}}}}$ to VWC change is −0.11 (W/W)/(kg/m2), and the average measurement error of VWC is ∼12.4%. Thus, the horizontally polarized antenna is suitable for vegetation monitoring in most environments, including agricultural settings. The effects of soil moisture on GPS SNR amplitude are also documented, but these are minor compared to the decrease in amplitude resulting from vegetation growth.

GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States

GPS interferometric reflectometry is a new environmental sensing technique that can be used to measure near‐surface soil moisture, snow depth, and vegetation water content variations. The spatial scale of this technique, ~1000 m2, is intermediate to that of other in situ sensors (&lt;1 m2) and satellites (&gt;100 km2). Soil moisture and snow depth retrievals have accuracies of 0.04 m3/m3 and 0.04 m, respectively. These accuracies are sufficient for many hydrologic applications. Fortuitously, GPS interferometric reflectometry can be used with consumer‐grade off the shelf GPS instruments that are operated by the geodetic, geophysical, and surveying communities. This means that GPS data from thousands of sites are potentially available for environmental scientists seeking new in situ data for soil moisture, snow depth, and vegetation water content. The technique can be applied to data from existing archives or for new sites. Although the accuracy of the technique has only been evaluated for the GPS constellation, the technique can also be used for other navigation constellations such as GLONASS, Galileo, and Beidou. WIREs Water 2016, 3:775–787. doi: 10.1002/wat2.1167This article is categorized under: Science of Water &gt; Hydrological Processes Science of Water &gt; Methods

Snow depth estimation accuracy using a dual-interface GPS-IR model with experimental results

Measuring snow depth using the GPS interferometric reflectometry is an active microwave remote sensing technique and an emerging approach because of its relatively large spatial coverage and high temporal sampling capability. The current geodetic GPS networks are capable of measuring snow depth in the vicinity of the antenna installation at no additional hardware cost. However, the performance is constrained by the geodetic GPS antenna which was originally designed to minimize the reception of the reflected signal. In our prior work, we proposed a horizontally polarized antenna which has equal gain for both direct and reflected signals and tested its performance for a single snow event. In order to comprehensively assess its performance, we set up a horizontally polarized snow monitor (HPSM) using the improved antenna at Marshall, Colorado, USA, over the 2013---2014 water year. The data from the HPSM clearly shows that the proposed design has high sensitivity to even very light snowfalls. However, some anomalies are observed from the HPSM measurements, which tend to either overestimate or underestimate the actual snow depth. We explain the observed measurement anomalies by replacing the traditional air---snow single-interface model with an air---snow---soil dual-interface model. The effectiveness of the new model is validated by comparing the simulated results to the HPSM measurements. Utilizing the dual-interface model, we simulate the error curve for snow depth estimation given various snow depths and snow permittivities. The error curve shows that the estimation biases can be observed only for shallow snow with a relatively small permittivity value.

An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil

Ground-reflected global positioning system signals measured by a geodetic-quality GPS system can be used to infer temporal changes in near-surface soil moisture for the area surrounding the antenna...

Vegetation Sensing Using GPS-Interferometric Reflectometry: Theoretical Effects of Canopy Parameters on Signal-to-Noise Ratio Data

The potential to use GPS signal-to-noise ratio (SNR) data to estimate changes in vegetation surrounding a ground-based antenna is evaluated. A 1-D plane-stratified model that simulates the response of GPS SNR data to changes in both soil moisture and vegetation is presented. The model is validated against observations of SNR data from four field sites with varying vegetation cover. Validation shows that the average correlation between modeled and observed SNR data is higher than the average correlation between concurrent SNR observations from different satellite tracks at a site. The model also reproduces variations in the SNR metrics amplitude, phase, and effective reflector height over a range of vegetation wet weights from 0 to 4 $\hbox{kg}\cdot\hbox{m}^{-2}$ , with $r^{2}$ values of 0.79, 0.84, and 0.62, respectively. Model simulations indicate that the amplitude of SNR oscillations may be used to estimate vegetation amount when vegetation wet weight is below 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ . When vegetation wet weight exceeds 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ , the sensitivity of amplitude to changes in vegetation amount decreases. Phase of SNR oscillations also varies consistently with vegetation up to 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ . However, phase is also very sensitive to soil moisture variations, thus limiting its utility for estimating vegetation. Effective reflector height is not a consistent indicator of vegetation change. Beyond 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ , the constant frequency assumption used to characterize SNR fluctuations does not adequately describe observed data. A more complex approach than the standard SNR metrics used here is required to extend GPS-Interferometric Reflectometry sensing to thicker canopies.

Comparison of Snow Data Assimilation System with GPS reflectometry snow depth in the Western United States

In this study, we compare gridded snow depth estimates from the Snow Data Assimilation System (SNODAS) with snow depth observations derived from GPS interferometric reflectometry (GPS-IR) from roughly 100 Plate Boundary Observatory sites in the Western United States spanning four water-years (2010–2013). Data from these sites are not assimilated by SNODAS; thus, GPS-IR measurements provide an independent data set to evaluate SNODAS. Our results indicate that at 80% of the sites, SNODAS and GPS-IR snow depth agree to better than 15-cm root mean square error, with correlation coefficients greater than 0.6. Significant differences are found between GPS-IR and SNODAS for sites that are distant from other point measurements, are located in complex terrain or are in areas with strong vegetation heterogeneities. GPS-IR estimates of snow depth are shown to provide useful error characterization of SNODAS products across much of the Western United States and may have potential as an additional data assimilation source that could help improve SNODAS estimates. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.

Snow measurement by GPS interferometric reflectometry: an evaluation at Niwot Ridge, Colorado

AbstractSnow is a critical storage component in the hydrologic cycle, but current measurement networks are sparse. In addition, the heterogeneity of snow requires surveying larger areas to measure the areal average. We presented snow measurements using GPS interferometric reflectometry (GPS‐IR). GPS‐IR measures a large area (~100 m2), and existing GPS installations around the world have the potential to expand existing snow measurement networks. GPS‐IR uses a standard, geodetic GPS installation to measure the snow surface via the reflected component of the signal. We reported GPS‐IR snow depth measurements made at Niwot Ridge, Colorado, from October 2009 through June 2010. This site is in a topographic saddle at 3500 m elevation with a peak snow depth of 1.7 m near the GPS antenna. GPS‐IR measurements are compared with biweekly snow surveys, a continuously operating scanning laser system and an airborne light detection and ranging (LIDAR) measurement. The GPS‐IR measurement of peak snowpack (1.36–1.76 m) matches manual measurements (0.95–1.7 m) and the scanning laser (1.16 m). GPS‐IR has RMS error of 13 cm (bias = 10 cm) compared with the laser, although differences between the measurement locations make comparison imprecise. Over the melt season, when the snowpack is more homogenous, the difference between the GPS‐IR and the laser is reduced (RMS = 9 cm, bias = 6 cm). In other locations, the GPS and the LIDAR agree on which areas have more or less snow, but the GPS estimates more snow on the ground on tracks to the west (1.58 m) than the LIDAR (1.14 m). Copyright © 2011 John Wiley &amp; Sons, Ltd.