Tidal information is a valuable parameter for scientific studies and navigational safety. Despite traditional tide stations and satellite altimeters, shipborne Global Navigation Satellite System (GNSS) altimeters can provide an alternative method for instantaneous measurements. However, due to ship hydrodynamics and draft variations, especially for large vessels, the baseline between the GNSS positioning antenna and the sea surface is always unavailable or less accurate. This case study presents a novel ship-based altimetry method using GNSS-R phase altimetry, which is capable of accurately monitoring tidal information on a moving ship platform. The delay difference between the direct and reflected GNSS paths is obtained from the signal phase difference generated by open-loop tracking through a software-defined receiver. Spectral analysis was used to further solve the integer ambiguity problem of phase measurements, and then, accurate tidal information was obtained based on high-precision GNSS positioning. To evaluate the performance of the system, a case study of a shipborne experiment was conducted. The results show that the ship-based GNSS-R altimetry system can accurately measure the sea surface height variation. The root-mean-squared error (RMSE) is within 3.0 cm compared to the in situ value. This case study demonstrates the potential of ship-borne GNSS-R phase altimetry as an effective and accurate method for tidal monitoring in dynamic maritime environments.

Currently, soil moisture (SM) is mostly measured by conventional contact methods, such as dry weighing and time domain reflectometry (TDR). Global navigation satellite system reflectometry (GNSS-R) is a promising and powerful technique for measuring SM at higher resolutions and lower costs than conventional methods. It can be employed in several observation geometries, including ground-based, airborne, and space-borne instruments. To investigate the potential of retrieving SM by drone-based GNSS-R, we selected the L5-band signal of the geostationary component of the Quasi-Zenith Satellite System (QZSS) as a source. We equipped a drone with two antennas connected to a raw IF data collector and conducted two flights over bare soil sparsely covered with low wheat sprouts at an experimental farm in China. Approximately 5 h of data were collected and processed using a software-defined receiver to calculate the L5-band signal power ratio (reflected/direct). From this power ratio, SM values were subsequently derived every second then smoothed within 5-min intervals. The GNSS-R retrieval accuracy analysis indicated its dependence on both SM and drone flight height. The retrieved data were more stable at lower heights under dry soil conditions and at higher heights under wet conditions, achieving RMSE retrieval errors of 0.022 (SM =0.1805 m3/m3) and 0.056 (SM =0.3675 m3/m3) under dry and wet conditions, respectively.

The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions were used to detect daily soil F/T cycles, including completely frozen, completely thawed, and F/T transition states. Firstly, the calibrated Cyclone GNSS (CYGNSS) reflectivity was used for soil F/T classification. Compared with those of Soil Moisture Active and Passive F/T data and in-situ data, the detection accuracies of CYGNSS reach 75.1 and 81.4%, respectively. Subsequently, the changes in spatial characteristics were quantified, including the monthly occurrence days of the soil F/T state. It is found that the completely frozen and completely thawed states have opposite spatial distributions, and the F/T transition states distribute from the east to the west and then back to the east of the Qinghai-Tibet Plateau, which may be due to varying diurnal temperatures in different seasons. Finally, the first day of thawing, last day of thawing, and thawing period of the F/T year were analyzed in terms of the changes in temporal characteristics. The temporal variation of thawing is mainly different between the western and eastern parts of the Tibetan Plateau, which is in agreement with the spatial variation characteristics. The results demonstrate that the CYGNSS can accurately detect the F/T state of near-surface soil in the daily scale. Moreover, it can complement traditional remote sensing missions to improve the F/T detection capability. It can also expand the applications of GNSS-R technology and provide new avenues for cryosphere research.

Global navigation satellite system reflectometry (GNSS-R) altimetry has great potential to provide high spatial–temporal resolution sea surface heights (SSHs) at low cost. Interferometric phase measurements between direct and reflected signals can be used for altimetry retrieval to achieve high-precision solutions. The motions of the medium Earth orbit (MEO) satellites cause interferometric phase change rapidly, which would increase the probability of occurrence of the phase unwrapping errors than the case of the geosynchronous Earth orbit (GEO) satellite. In order to overcome this problem, we propose a coastal GNSS-R altimetry algorithm using the signals from Quasi-Zenith Satellite System (QZSS) GEO satellite. Precise SSH variations can be achieved using the interferometric phase measurements without ambiguity fixed. We also perform coastal experiments on a trestle using a specialized GNSS-R setup to verify our algorithm. It is composed of an intermediate frequency (IF) data collector and two antennas. The up-looking antenna is used to receive direct signals, while the down-looking antenna receives the signals reflected from the sea surface. Raw IF data sampled at 62 MHz are collected and processed to derive interferometric carrier phase delay measurements using a self-developed software-defined receiver. Approximately 7 h of reflector heights are retrieved at 1-min intervals and the solutions are evaluated via comparison with measurements provided by a 26-GHz altimetry radar located near the GNSS-R setups. The results show that the root mean square error (RMSE) of sea level estimation is about 1.4 cm by using the QZSS GEO data.

Global Navigation Satellite System Reflectometry (GNSS-R) is a passive technique for remote sensing of soil moisture, which has continuous all-day and all-weather applicability on different platforms. New GNSS signals with advanced modulation and higher power are expected to improve the performance of GNSS-R. In this study, we performed a ground-based dual-antenna GNSS-R experiment on farmland and collected 15-min raw intermediate frequency data with central frequency of 1175.42 MHz hourly over two different 24-h periods. The power ratio between the direct and reflected signals from QZSS satellites were computed using a self-developed software-defined receiver with 1-ms coherent integration and 200-ms incoherent adds. Then, soil moisture was resolved using a semiempirical model based on the power ratios. Solutions were evaluated using measurements obtained using a time-domain reflectometry probe. Results demonstrated that signal power-ratio-based QZSS signals can be used to retrieve soil moisture under bare soil conditions. Moreover, for signal power-ratio-based case, results from geostationary orbit (GEO) satellite signals (STD: 0.013 m3/m3 and 0.007 m3/m3) performed better than those from inclined geosynchronous orbit (IGSO) satellite signals (STD: 0.033–0.071 m3/m3).

Compared with tide gauges, Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) can provide low-cost, long-term sea-level data that are not susceptible to crustal loading. Signal-to-noise ratio (SNR) observables in GNSS files are commonly used for GNSS-MR; however, these observables are not always present, especially in early GNSS files. Several different combinations of codes and carrier-phases for GNSS-MR as substitutes to extract sea level have been proposed; however, the requirement of these methods for application of cycle slip detection or multi-frequency observations to isolate multipath signals reduces their applicability. Here, we propose a new method for sea-level estimation using Signal Strength Indicator (SSI) data in GNSS observation files, which is an alternative to existing methods because SSI data always exist. To verify the proposed method, we used four multi-GNSS data from three stations to monitor sea level. Sea-level estimations with root-mean-square errors of 7–8, 5–9, 12–15 and 9–13 cm relative to in-situ data were retrieved, and the correlation coefficients for these stations were bigger than 0.98, 0.98, 0.93 and 0.96, respectively. Moreover, the proposed method measures sea levels with precision similar with the traditional SNR method. In addition, sea-level results derived from the proposed method at these stations were further applied to estimate ocean tides. Ocean-tide coefficients for several main tides determined by different data were in good agreement.

The applications of Global Navigation Satellite System Reflectometry (GNSS-R) altimetry will mainly rely on the precision of this remote-sensing system. Comparing with nadir measurement of satellite altimetry and water gauges, GNSS-R altimetry makes off-nadir measurements with high spatial and temporal resolution come true. To enhance the data precision and spatial resolution, an improved cGNSS-R altimetry system, including one uplooking geodetic GNSS receiver, one downward left-handed circularly polarized GNSS-R receiver, and one tamed atomic clock, was adopted. The objective of the introduced atomic clock is to provide an accurate and synchronous time frequency signal for both receivers, and receiver clock errors can be removed in the regular single difference geodetic processing. Therefore, the unknown parameters in the computation of cGNSS-R were reduced to only one, caused by the phase subtraction between two receivers. By this proposed method, water level height can be derived from one GNSS satellite's signals, and the spatial resolution will be improved greatly due to more observations. An experimental water level measurement over a smooth inland lake is presented with GNSS-R carrier-phase processing. Then, GNSS-R-derived local water level height is compared within situobservations. A high degree of agreement is found in these two independent data, and the standard deviation of the derived water level height is better than 1 cm at 1-Hz sample.