Global Positioning System Reflectometry Research Articles

Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms

This study proposes a low-cost and &#x201C;proof-of-concept&#x201D; methodology to obtain high spatial resolution soil moisture (SM) via processing reflected Global Positioning System (GPS) and a multispectral camera data acquired by small Unmanned Aircraft System (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off the shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm uses <i>in-situ</i> SM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210&#x00A0;m by 110&#x00A0;m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)&#x0027;s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 minutes of flight time at a 15-m altitude, and SM can be mapped with 5m&#x00A0;&#x00D7;&#x00A0;5m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eight <i>in-situ</i> SM station datasets via 10-fold and leave-one-probe-out cross-validation techniques. Overall root-mean-square errors (RMSE) of 0.013 m <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> volumetric SM and R-value of 0.95 [-] are obtained for 10-fold cross-validation. The proposed model reached an RMSE of 0.033 m <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> and an R-value of 0.5 [-] in leave-one-probe-out cross-validation. While having limited data, the results indicate that high resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture (PA) applications.

Soil moisture estimation based on triple-frequency multipath error

ABSTRACT Soil moisture (SM) is a critical environmental factor. Therefore, it is of great significance to measure it dynamically and precisely. Studies have proved that Global Positioning System Reflectometry (GPS-R) based on signal-to-noise ratio can realize the monitoring of SM with high-precision, high spatiotemporal resolution, low cost, and under all weather conditions. The transmissions of GPS triple-frequency carrier phase (TRFCP) and triple-frequency pseudorange (TRFP) facilitate obtaining high-quality multipath errors in real-time. Accordingly, to enrich the data sources and methods for SM estimation based on GPS-R, two novel SM estimation methods – the TRFCP multipath error method (TRFCP method) and the TRFP multipath error method (TRFP method) – are proposed. The feasibility of the proposed methods was evaluated by the SM content (SMC) from the Plate Boundary Observatory (PBO) H2O project. The experimental results demonstrate that the phase delays estimated by the proposed methods both are strongly correlated with the PBO SMC, with Pearson correlation coefficients (R) of 0.95 and 0.84, respectively. Furthermore, both methods achieved high estimation accuracy for the SMC.

Antarctic sea ice detection using a shipborne GPS reflectometry setup

The feasibility of Antarctic sea ice detection based on shipborne global positioning system reflectometry (GPS-R) technology is shown in this paper. Because the permittivity of sea water is much higher than that of sea ice, the reflected left-handed circular polarized (LHCP) GPS signal (RL) reflection coefficient of sea water is markedly higher than that of sea ice. The polarization ratio of RL to the direct right-handed circular polarized (RHCP) GPS signal (DR) is used to distinguish between sea water and sea ice in this paper. The experiment was performed on the ship “XueLong” for approximately 9 days from December 2014 to January 2015 during the 31st Chinese National Antarctic Research Expedition (CHINARE 31). The sea ice concentration data with a 25 km × 25 km spatial resolution derived from the National Snow and Ice Data Center (NSIDC) are used for validation and some pictures of sea ice taken from “XueLong” are shown for comparison. The polarization ratios (RL/DR) are calculated, and the correlation coefficient between the polarization ratios (RL/DR) and the sea ice concentrations is −0.66.

GPS reflectometry study detecting snow height changes in the Southern Patagonia Icefield

Southern Patagonia Icefield (SPI) glaciers have been retreating and shrinking at high rates in recent decades, significantly contributing to sea level rise. In spite of the importance of the SPI, very little is known about its mass balance apart from models lacking in situ validation, such as snow accumulation/ablation at high altitude. On the ground, snow changes are normally measured with stakes planted on snow frequently visited. Automation is possible by the use of ranging sensors, based on laser, radar or ultrasound, installed as part of automatic weather stations. In this study, we use for the first time in the SPI the technique of Global Positioning System Reflectometry (GPS-R) for measuring snow level changes. GPS-R uses observations from GPS satellites tracked by GNSS (Global Navigation Satellite Systems) ground stations. GNSS stations, typically installed according to geodetic network standards for geodynamics purposes (e.g., glacial isostatic rebound studies), inadvertently also record the signal reflected on the snow surface in the antenna surroundings. The GPS-R snow level variations were compared to an ultrasonic sensor installed nearby, operating simultaneously between October 2015 and February 2016. During this period the two datasets exhibited a correlation of 0.91 and an RMS of 17 cm, confirming that GNSS receivers can be used not only for geodetic purposes but also for snow accumulation/ablation monitoring in SPI.

Low-Frequency Mean Square Slopes and Dominant Wave Spectral Properties: Toward Tropical Cyclone Remote Sensing

Spectral properties near the dominant wave region influence significantly the surface roughness relevant to ocean remote sensing employing low-frequency microwave sensors. The critical parameters characterizing dominant waves are wind speed and dimensionless spectral peak frequency, which is the inverse wave age. The dimensionless spectral peak frequency can be expressed as an equivalent dimensionless fetch or duration. The connection between dominant waves and surface roughness is the spectral slope. This paper presents a surface wave spectral model designed for low-frequency microwave remote sensing, with special emphasis on tropical cyclone (TC) applications. The key elements of the spectral model are: 1) a general spectral function with coefficients accommodating a variable spectral slope and 2) a parametric function connecting the spectral slope and wind speed, which is established with the mean square slope (MSS) observations obtained inside hurricanes by the global positioning system reflectometry technique. In order to make use of the MSS observations inside hurricanes, parametric models of the spatial distributions of wind speed and dimensionless spectral peak frequency inside TCs are developed. The parametric models are based on the wind and wave similarity relationships derived from analyses of hurricane hunter measurements.

Azimuth selection for sea level measurements using geodetic GPS receivers

Based on analysis of Global Positioning System (GPS) multipath signals recorded by a geodetic GPS receiver, GPS Reflectometry (GPS-R) has demonstrated unique advantages in relation to sea level monitoring. Founded on multipath reflectometry theory, sea level changes can be measured by GPS-R through spectral analysis of recorded signal-to-noise ratio data. However, prior to estimating multipath parameters, it is necessary to define azimuth and elevation angle mask to ensure the reflecting zones are on water. Here, a method is presented to address azimuth selection, a topic currently under active development in the field of GPS-R. Data from three test sites: the Kachemak Bay GPS site PBAY in Alaska (USA), Friday Harbor GPS site SC02 in the San Juan Islands (USA), and Brest Harbor GPS site BRST in Brest (France) are analyzed. These sites are located in different multipath environments, from a rural coastal area to a busy harbor, and they experience different tidal ranges. Estimates by the GPS tide gauges at azimuths selected by the presented method are compared with measurements from physical tide gauges and acceptable correspondence found for all three sites.

First spaceborne observation of sea surface height using GPS‐Reflectometry

AbstractAn analysis of spaceborne Global Positioning System reflectometry (GPS‐R) data from the TechDemoSat‐1 (TDS‐1) satellite is carried out to image the ocean sea surface height (SSH). An SSH estimation algorithm is applied to GPS‐R delay waveforms over two regions in the South Atlantic and the North Pacific. Estimates made from TDS‐1 overpasses during a 6 month period are aggregated to produce SSH maps of the two regions. The maps generally agree with the global DTU10 mean sea surface height. The GPS‐R instrument is designed to make bistatic measurements of radar cross section for ocean wind observations, and its altimetric performance is not optimized. The differences observed between measured and DTU10 SSH can be attributed to limitations with the GPS‐R instrument and the lack of precision orbit determination by the TDS‐1 platform. These results represent the first observations of SSH by a spaceborne GPS‐R instrument.

Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data

Snow is a water resource and plays a significant role in the water cycle. However, traditional ground techniques for snow monitoring have many limitations, e.g., high-cost and low resolution. Recently, the new Global Positioning System-Reflectometry (GPS-R) technique has been developed and applied for snow sensing. However, most previous studies mainly used GPS L1C/A and L2C Signal-to-Noise Ratio (SNR) data to retrieve snow depth. In this paper, snow depth variations are retrieved from new weak GPS L2P SNR data at three stations in Alaska and evaluated by comparing with in situ measurements. The correlation coefficients for the three stations are 0.79, 0.88 and 0.98, respectively. The GPS-estimated snow depths from the L2P SNR data are further compared with L1C/A results at three stations, showing a high correlation of 0.94, 0.93 and 0.95, respectively. These results indicate that geodetic GPS observations with SNR L2P data can well estimate snow depths. The samplings of 15 s or 30 s have no obvious effect on snow depth estimation using GPS SNR L2P measurements, while the range of 5°–35°elevation angles has effects on results with a decreasing correlation of 0.96 and RMSE of 0.04 m when compared to the range of 5°–30° with correlation of 0.98 and RMSE of 0.03 m. GPS SNR data below 30° elevation angle are better to estimate snow depth.

GPS-R L1 interference signal processing for soil moisture estimation: an experimental study

Global positioning system reflectometry (GPS-R) is an emerging area of GPS applications in microwave remote sensing using multipath reflected signals. Soil moisture estimation is one of the many potential applications of the GPS-R technique. The focus of this study is on investigating the feasibility of soil moisture estimation based on GPS L1 band interference signals which can be readily captured using a low-cost off-the-shelf L1-band GPS receiver. The theoretical background is studied, and the field experiments conducted are described. Power spectrum analysis is performed on the received interference signals to determine the interference signal frequency variation, and cosine similarity is applied to identify the initial phase change. Data collected at a number of continuously operating GPS stations are also analyzed. The results demonstrate that both interference signal frequency and phase have changed significantly after rainfalls occurred. That is, it is possible to estimate soil moisture by analyzing the frequency change and phase shift. However, it is also observed that the phase shift is inconsistent in some cases. Ongoing work will focus on figuring out the source of the inconsistency so that reliable estimation of soil moisture can be achieved.

An intelligent decision-making system for flood monitoring from space

This paper presents the results of a feasibility study on intelligent image processing for flood monitoring on board satellites. The ability to detect temporal changes in images is one of the most important functions in intelligent image processing systems for hazard and disaster monitoring applications. An automatic change detection system is proposed, the purpose of which is to monitor particular areas on earth for flooding events and give warnings to the authorities accordingly. A novel solution to flood detection based on the combined use of optical multispectral imagery and global positioning system reflectometry data is introduced. A fuzzy inference engine is used in the decision-making process, which generates control signals to other subsystems on board the satellite.

Correction of the Sea State Impact in the L-Band Brightness Temperature by Means of Delay-Doppler Maps of Global Navigation Satellite Signals Reflected Over the Sea Surface

This paper presents an efficient procedure based on 2-D convolutions to obtain delay-Doppler maps (DDMs) of Global Navigation Satellite Signals reflected (GNSS-R) over the sea surface and collected by a spaceborne receiver. Two DDM-derived observables (area and volume) are proposed to link the sea-state-induced brightness temperature to the measured normalized DDM. Finally, the requirements to use Global Positioning System reflectometry to accurately correct for the sea state impact on the L-band brightness temperature (quantization levels, decimation, truncation, and noise impact) are analyzed in view of its implementation in the Passive Advanced Unit instrument of the Spanish Earth Observation Satellite (SeoSAT/INGENIO) project.