Sea ice thickness retrieval using Global Navigation Satellite System-Reflectometry (GNSS-R) is a challenging problem in sea ice remote sensing, especially for sea ice thicknesses over 1 m, which is still in the blank stage. In this paper, a seamless stacking-based retrieval method for sea ice thickness is proposed, which reduces the RMSE for thicknesses below 1 m while ensuring the accuracy of sea ice thickness retrieval for thicknesses above 1 m.Principal component analysis (PCA) was used to extract delayed Doppler map (DDM) features, while the scattering coefficient and incidence angle were calculated using TechDemoSat-1 (TDS-1) data. Sea ice salinity and temperature were derived from soil moisture and ocean salinity (SMOS) data and used as inputs to the model along with other features. The performance of four machine learning algorithms - Decision Tree (DT), K Nearest Neighbors (KNN), Support Vector Regression (SVR), and Random Forest (RF) - was compared, and the stacking model was constructed using these four algorithms as the base learner to improve performance. Validation using SMOS data for thicknesses up to 1 m showed that the stacking algorithm significantly improved retrieval accuracy, reducing the RMSE from 7 cm to 0.4 cm and improving the correlation coefficient (r) from 0.94 to 0.99. For thicknesses greater than 1 m, validation using Cryosat-2 data also showed strong performance. In addition, the effect of sea ice parameters on retrieval accuracy and sources of error was analyzed.

Ocean surface wind is vital to the Earth’s meteorological system, and their properties can be detected by spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements. With the growing number of GNSS-R signal sources, machine learning technology exhibits prominent advantages in wind speed estimation. Currently, the deep-learning techniques that establish relationships between GNSS-R measurements and ocean surface wind speeds generally apply grids and sequence structures and lack flexibility and robustness. Additionally, constructing models with individual GNSS-R observations results in the loss of valuable temporal correlation within Delay-Doppler Maps (DDMs). Therefore, this study proposes a novel spatiotemporal graph-based deep neural network (STG-DNN) for retrieving wind speed, which incorporates a graph module with a transformer module to fully exploit the spatial-temporal dependencies of DDMs. Results demonstrate that the graph module significantly improves both the accuracy and reliability in wind speed retrieval. Meanwhile, the transformer module effectively captures temporal features from various DDMs. Validations with Cyclone GNSS (CYGNSS) test data support the superior accuracy of STG-DNN, revealing a correlation coefficient of 0.92 for the wind speeds. The results indicate that the root mean square error (RMSE) of STG-DNN for wind speed is 1.27 m/s, representing improvements of approximately 33.2%, 20.6%, and 13.6% over the minimum variance estimator (MVE), convolutional neural network (CNN), and Vision Graph Neural Networks (VIG), respectively. Additionally, a promising agreement is observed between STG-DNN and ERA5 in the spatial distributions of wind speed retrieval, indicating a robust spatial performance in STG-DNN. As for the temporal scale, the daily variations in retrieval accuracy of STG-DNN exhibit smaller fluctuations compared to both CNN and VIG wind data in the test dataset.

Artificial intelligence (AI) models developed for the Global Navigation Satellite System Reflectometry (GNSS-R) observations are capable of estimating geophysical parameters, especially ocean surface wind speeds. Understanding the decision-making process of deep learning models can be as significant as improving output accuracy in practical applications. This study explores the exploitation of Explainable Artificial Intelligence (XAI) for interpreting complex deep learning models. With the assistance of SHAP (SHapley Additive exPlanations) Gradient Explainer, this study evaluates the impact of both Delay-Doppler Map (DDM) pixels and ancillary parameters on model predictions, which can further help in understanding the role of specific input parameters. Additionally, this study investigates the potential of applying XAI to enhance the accuracy of deep learning models, which can be extended for climate-related applications.

Significant wave height (SWH) is a crucial parameter that characterizes oceanic wave behavior, playing a pivotal role in oceanic research and disaster management strategies. Currently, the observation of SWH predominantly relies on both buoy and spaceborne microwave remote sensing techniques. However, these techniques face challenges when applied in coastal regions. Addressing this issue, this study introduces an innovative approach leveraging reflected signals to estimate SWH based on coastal Global Navigation Satellite System (GNSS) stations. We establish a model for wave height retrieval by examining the temporal variation in signal-to-noise ratio (SNR) data related to SWH, drawing upon the GNSS interferometric reflectometry (GNSS-IR) method for SWH retrieval. Experimental findings highlight the efficacy of the multisystem GNSS-IR SWH inversion. It demonstrates an accuracy of 12 cm, coupled with an average temporal resolution of 29 min, and exhibits a strong correlation coefficient of 0.95 when compared to ocean buoy measurements. The deployment of coastal GNSS stations emerges as a promising source for obtaining true and reliable data on coastal SWH.

The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) method with high spatial and temporal resolution is used to derive snow depth as a complement to existing snow products because of its ease of implementation. GNSS-IR snow depth retrieval accuracy is affected by the land cover and terrain irregularities on the reflecting surface. The number of full waveforms of the signal-to-noise ratio (SNR) is a reliable indicator of reflector height (RH). More importantly, this feature can be extracted in real time. Considering the presence of noise in the received signal, a fitting process is essential. In this article, we propose to use the particle swarm optimization (PSO) algorithm to fit the SNR oscillatory term and extract the number of fit peaks (NFP) to describe the number of full waveforms. Based on signal optimization, retrieval results are enhanced by exploiting the relationship between the good NFP (G-NFP) derived from the historical data and snow depth, and the operation is devoid of a priori constraints. The validation experiment used GNSS data from the P351 station of the EarthScope Plate Boundary Observatory (PBO) network and in situ snow depth measurements from the Snowdrift Telemetry (SNOTEL) network for 2020–2022. Snow depth retrieval results from 2020 to 2021 were used as historical data to derive the G-NFP distribution statistically. Statistically, each NFP corresponds to roughly 25 cm of snow depth change. The G-NFP distribution was then used in the snow depth retrieval process for 2022. The experimental results show that the root-mean-square errors (RMSEs) for global positioning system (GPS)-S1C, GLONASS-S1C, beidou navigation satellite system (BDS)-S2I, and Galileo-S1C based on the PSO-NFP method are 10, 13, 11, and 12 cm, respectively. Compared to the conventional method (CM), the accuracies have improved by approximately 38%, 43%, 35%, and 33%. Moreover, during the snow-free state, the retrieval accuracies based on the PSO-NFP method are improved by approximately 60% compared to the CM. The results show that the proposed method is very suitable for GNSS stations with large snow depth and terrain fluctuations and improves the retrieval results in the snow-free state. Moreover, NFP does not require prior data and can be extracted in real time, indicating its strong generality and potential to serve as a fundamental metric for other snow depth retrieval methods.

As a novel remote sensing technique, GNSS reflectometry (GNSS-R) opens a new era of retrieving Earth surface parameters. Several studies employ the combination of deep learning and GNSS-R observable delay-Doppler maps (DDMs) to generate ocean wind speed estimation. Unlike these methods that often use convolutional neural networks (CNNs) with inductive bias, we proposed a Transformer-based model, named DDM-Former, to exploit fine-grained delay-Doppler correlation independently. Our model is evaluated on the Cyclone GNSS (CYGNSS) version 3.0 dataset and shown to outperform the other retrieval methods.

The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique based on signal-to-noise ratio (SNR) data is widely used for snow depth retrieval. Since snow depth retrieval in a snow-free state is very important for meteorological monitoring and since many corrections are post-processed to improve the retrieval accuracy, we propose a GNSS-IR snow depth retrieval model based on a back-propagation neural network optimized by a genetic algorithm to detect the snow state and predict snow depth using the frequency, amplitude and phase of the multipath oscillation term as input features. GPS data collected from the P351 station of the PBO network and measured snow depth from the SNOTEL network were used to conduct the experiments. The accuracy of daily snow state detection for the experimental station exceeded 96%. Combined with the snow state detection results for snow depth regression prediction, the experimental results show that the root mean square error of the snow depth retrieval results for P351 station is 12.09 cm. Compared with the traditional model, the retrieval accuracy is improved by 29.1%, and the correlation coefficient also reaches 0.97, indicating that the proposed snow depth retrieval model not only has high accuracy but also has strong stability. In this study, snow state detection is proposed to improve the retrieval accuracy in snow-free conditions, and the possibility of snow depth retrieval without antenna height is provided.

Global Navigation Satellite System-Reflectometry (GNSS-R) is one of the main technologies used for sea ice remote sensing detection, and is based on the multipath interference effect of satellite signals. To improve the GNSS-R sea ice detection performance in terms of accuracy, robustness to noise, and data utilization, a linear discriminant analysis (LDA)-based method was proposed in this paper. Delay-Doppler maps (DDMs) collected from TechDemoSat-1 (TDS-1) were employed as input and classified into different types based on the signal-to noise ratio (SNR) related to the noise effect. For low-effect-noise DDMs, the LDA-based sea-ice detection method presented an accuracy of 95.03%, verifying the feasibility of LDA-based GNSS-R sea-ice detection. For the middle noise effect and high noise effect DDMs, the LDA-based method is more robust to noise effects than the convolutional neural network (CNN) method. Although the detection accuracy decreased when the SNR decreased or integral delay waveform average (IDWA) increased, the LDA-based method was more robust than the CNN-based one. The data utilization and melting period were also analyzed to account for variations in detection accuracy. The LDA-based method used 67.82% more data than previous experiments with threshold IDWA≤58210.32 and SNR>-17.48dB. The melting periods were analyzed based on the noise, SNR, surface reflectivity, and permittivity. When the status of sea ice changes, outliers of surface reflectivity appear, the average permittivity varies in [10, 60], and the detection accuracy decreases during the melting period of sea ice. The results show that the correlation coefficient with the National Oceanic and Atmospheric Administration (NOAA) data is up to 0.93, with different threshold IDWA or IDWA. The LDA-based method predicted results that greatly matched the sea ice distribution from the NOAA data.

For the first time, rain effects on the polarimetric observations of the global navigation satellite system reflectometry (GNSS-R) are investigated. The physical feasibility of tracking the modifications in the surface roughness by rain splash and the surface salinity by the accumulation of freshwater is theoretically discussed. An empirical analysis is carried out using measurements of a coastal GNSS-R station with two side-looking antennas in right- and left-handed circular polarizations (RHCP and LHCP). Discernible drops in RHCP and LHCP powers are observed during rain over a calm sea. The power drop becomes larger at higher elevation angles. The average LHCP power drops by$\approx ~5$dB at an elevation angle of 45°. The amplitude of the correlation sum shows a dampening, responding to rain rate systematically. The LHCP observations show higher sensitivity to rainfall compared to RHCP observations. The retrieved standard deviation of surface heights shows a steady increase with the rain rate. The derived surface salinity shows a decrease at rains higher than 10 mm/h. This study confirms the potential under environmental conditions of the GNSS-R ground-based station, e.g., with salinity mostly lower than 30 psu, over a calm sea, being a starting point for future investigations.

Snow depth monitoring is meaningful for climate analysis, hydrological research and snow disaster prevention. Global Navigation Satellite System-Reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing towards multi-constellation combined inversion. Aiming at the accuracy of snow depth inversion, this paper introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in-situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network, respectively. The root mean square error (RMSE) of the inversion results is reduced by 20-40% compared to the least squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this paper introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings, and provides an important reference for further research on GNSS-R technology.

Coastal sea level variation as an indicator of climate change is extremely important due to its large socioeconomic and environmental impacts. The ground-based global navigation satellite system (GNSS)-reflectometry (GNSS-R) is becoming a reliable alternative for sea surface altimetry. We investigate the impact of antenna polarization and orientation on GNSS-R altimetric performance at different carrier frequencies. A one-year dataset of ground-based observations at the Onsala Space Observatory using a dedicated reflectometry receiver is used. Interferometric patterns produced by the superposition of direct and reflected signals are analyzed using the least-squares harmonic estimation (LS-HE) method to retrieve sea surface height. The results suggest that the observations from global positioning system (GPS) L1 and L2 frequencies provide similar levels of accuracy. However, the overall performance of the height products from the GPS L1 shows slightly better performance due to more observations. The combination of L1 and L2 observations (L12) improves the accuracy up to 25% and 40% compared to the L1 and L2 heights. The impacts of antenna orientation and polarization are also evaluated. A sea-looking left-handed circular polarization (LHCP) antenna shows the best performance compared to both zenith- and sea-looking right-handed circular polarization (RHCP) antennas. The results are presented using different averaging windows ranging from 15 min to 6 h. Based on a 6-h window, the yearly root mean squared errors (RMSEs) between GNSS-R L12 sea surface heights with collocated tide gauge observations are 2.4, 3.1, and 4.1 cm with the correlation of 0.990, 0.982, and 0.969 for LHCP sea-looking, RHCP sea-looking, and RHCP up-looking antennas, respectively.

Reflectometry measurements have been conducted aboard the German research icebreakerPolarsternduring the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Signals of Global Navigation Satellite Systems (GNSS) were recorded using a dedicated GNSS reflectometry receiver for retrieval of sea-ice reflectivity. The primary goal is reflectometry-based monitoring of sea ice as a part of the Arctic climate study. The dataset presented here covers the expedition’s first leg (late September to mid-December 2019) in the Siberian Sector of the central Arctic (at about 82 ° N to$87~^\circ $N). Daily profiles of reflectivity are retrieved for satellite elevations$< 45^\circ $. In agreement with model prediction, the results show best reflectivity contrast (about 5 dB between compact pack-ice and lower ice concentrations) for observations at left-handed circular polarization and elevation angles of 10°–20°. A daily resolved time series of sea-ice relative permittivity is inverted from the left-handed data. In general, the level of inversion results is at the lower limit of sea-ice values (relative permittivity of 3 and below), potentially indicating an influence of incoherent volume scattering. An occasional increase in the relative permittivity is attributed to the presence of water. Sea-ice profiles show anomalies that are confirmed by enhanced model prediction (slab reflection). A long-term comparison of prediction and retrieved profiles indicates anomalies’ dependence on ice thickness and temperature.

This article analyzes the interferometric measurements of ground-based global navigation satellite systems (GNSSs) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the signal to noise ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach in comparison with the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, e.g., icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.

Monitoring coastal sea level has gained a large socioeconomic and environmental significance. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) offers various geophysical parameters including sea surface height. We investigate a one-year dataset from January to December 2016 to evaluate the performance of GNSS-R coastal sea levels during different sea states. Our experiment setup uses three types of antenna in terms of polarization and orientation. A zenith-looking antenna tracks Right-Handed Circular Polarization (RHCP) direct signals and two sea-looking antennas capture both Left-Handed Circular Polarization (LHCP) and RHCP reflections. The Singular Spectrum Analysis (SSA) is used for extracting interferometric frequency from the data and calculating the heights. The results indicate that the height estimates from the sea-looking antennas have better accuracy compared to the zenith-looking orientation. The LHCP antenna delivers the best performance. The yearly Root Mean Square Errors (RMSE) of 5-min GNSS-R L1 water levels compared to the nearest tide gauge are 2.8 and 3.9 cm for the sea-looking antennas and 4.7 cm for the zenith-looking antenna with correlations of 97.63, 95.02, 95.35 percent, respectively. Our analysis shows that the roughness can introduce a bias to the measurements.

A geophysical model function (GMF) for sea ice concentration (SIC) retrieval is developed based on the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data measured by the TechDemoSat-1 (TDS-1) satellite. The spreading characteristics of onboard processed delay-Doppler maps (DDMs) change with the surface roughness, which can be related to the SIC. A GNSS-R observable termed as differential delay waveform (DDW) generated from DDM is first used in this article to estimate SIC. Collocated SIC data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are used as the ground truth to develop and evaluate the SIC model based on the right edge waveform summation (REWS) of DDW. All usable TDS-1 data collected from February 2015 to February 2016 are adopted, and data collected over land were excluded. SIC models of the northern and southern hemispheres (SH) are developed, respectively, for avoiding the impact of geometry. In general, the REWS-based model can achieve a root mean square error (RMSE) of 11.78% and a bias of 1.67% for the northern hemisphere, and 12.10% and 1.94% for the SH, respectively. This article demonstrates the capabilities of the spaceborne GNSS-R in SIC retrieval.

Reflectometry of Global Navigation Satellite Systems (GNSS) signals from the ocean surface has provided a new source of observations to study the ocean-atmosphere interaction. We investigate the sensitivity and performance of GNSS-Reflectometry (GNSS-R) data to retrieve sea surface roughness (SSR) as an indicator of sea state. A data set of one-year observations in 2016 is acquired from a coastal GNSS-R experiment in Onsala, Sweden. The experiment exploits two sea-looking antennas with right- and left-hand circular polarizations (RHCP and LHCP). The interference of the direct and reflected signals captured by the antennas is used by a GNSS-R receiver to generate complex interferometric fringes. We process the interferometric observations to estimate the contributions of direct signals and reflections to the total power. The power estimates are inverted to the SSR using the state-of-the-art model. The roughness measurements from the RHCP and LHCP links are evaluated against match-up wind measurements obtained from the nearest meteorological station. The results report on successful roughness retrieval with overall correlations of 0.76 for both links. However, the roughness effect in LHCP observations is more pronounced. The influence of surrounding complex coastlines and the wind direction dependence are discussed. The analysis reveals that the winds blowing from land have minimal impact on the roughness due to limited fetch. A clear improvement of roughness estimates with an overall correlation of 0.82 is observed for combined polarimetric observations from the RHCP and LHCP links. The combined observations can also improve the sensitivity of GNSS-R measurements to the change of sea state.

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.

PRETTY is a 3U Cubesat mission by a consortium of Technical University Graz, Seibersdorf Laboratories and RUAG Space GmbH with a planned launch in 2022. The satellite is based on the OPS-SAT platform [1] and will host two payloads, a radiation monitor and a passive reflectometer. Within the present publication we will discuss the current status of the reflectometer payload including the most relevant risks and also their mitigation by prototype developments and measurements. A first operational version of the instrument using commercial off the shelf (COTS) demonstration boards, hosting flight representative components for the RF front end as well as for the digital signal processing has been assembled and integrated with the corresponding hard- and software. While measurement results based on using the output of a global navigation satellite system (GNSS) simulator as input to the PRETTY instrument have been presented earlier, we focus within this publication on the result of field tests on representative hardware in order to evaluate the expected L1 band environment for the mission.

A machine learning technique is implemented for retrieving space-borne Global Navigation Satellite System Reflectometry (GNSS-R) wind speed. Conventional approaches commonly fit a function in a predefined form to matchup data in a least-squares (LS) sense, mapping GNSS-R observations to wind speed. In this study, a feedforward neural network is trained for TechDemoSat-1 (TDS-1) wind speed inversion. The input variables, along with the derived bistatic radar cross-section σ0, are selected after investigating the wind speed dependence and the model performance. When compared to an LS-based approach, the derived model shows a significant improvement of 20% in the root mean square error (RMSE). The proposed neural network demonstrates an ability to model a variety of effects degrading the retrieval accuracy such as the different levels of the effective isotropic radiated power (EIRP) of GPS satellites. For example, the derived Mean Absolute Error (MAE) of the satellite with SVN 34 is decreased by 32% using the machine-learning-based approach.

The rapid development of the European Galileo system brings a great opportunity for the real-time retrieval of atmospheric parameters. In this contribution, Galileo observations are employed to retrieve real-time water vapor based on the precise point positioning (PPP) technique, where the benefit of ambiguity resolution on water vapor sensing is also investigated. The obtained atmospheric parameters, including zenith tropospheric delay (ZTD) and precipitable water vapor (PWV), are validated with respect to the postprocessing Global Positioning System (GPS) ZTD products and the PWV products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the real-time ZTDs, derived from the Galileo PPP solutions, agree well with the postprocessing GPS ZTDs. An averaged root-mean-square (rms) value of 8.5 mm for the ZTD differences is achieved for the float solution after an averaged initialization process of about 27.4 min. In terms of the fixed solution, the averaged rms value is decreased to 7 mm and the initialization time is shortened to 20.8 min, showing an improvement of 17.6% and 24.1%, respectively, when compared to the float solution. Furthermore, the derived Galileo-PWVs display good agreement with the ECMWF PWVs, with an accuracy of 1.9 and 1.7 mm for the float and the fixed solution, respectively.

After developing a wind speed retrieval algorithm, derived winds from measurements of UK TechDemoSat-1 (TDS-1), from May 2015 to July 2017, are compared to wind products of Advanced Scatterometer showing a reliable performance, especially during rain events. However, a rain signature in GNSS-R observations, a decrease in the value of the bistatic radar cross section at low winds, is demonstrated, which can potentially enable the technique to detect precipitation over oceans induced by low-to-moderate winds. This phenomenon is investigated and finally characterized as the rain splash effect altering the ocean surface roughness. To improve the quality of derived winds, a machine learning technique is implemented for the wind speed inversion as a geophysical model function. The trained feedforward neural network shows a significant improvement of 17% in the wind speed RMSE compared to the LS approach. In the end, one can conclude that space-borne ocean monitoring is evolving existing products with a potential for novel geophysical applications.

Passive remote sensing from space with signals of opportunity has been studied for a long time beginning in the early 90s [1]. The technique uses already existing signals, transmitted from satellites, which are reflected from ground in order to determine properties of earth surface. GNSS signals are the main interest here, due to their well-known signal properties. The Passive reflectometer acts as a bistatic radar, hence no expensive and power-consuming radar transmitter are needed. The received signal is correlated with a local code replica, where the resulting waveform shows properties of the reflected surface area. Instead of using a local code replica, the PRETTY mission will correlate the received reflected signal with the received direct signal. This technique is known as the interferometric approach. The main advantage for the interferometric approach is, that one is not bound to use known signals but can also exploit signals with unknown data modulation, opening up the possibility to use more generic signals for earth observation.The PRETTY satellite architecture will be based on the OPS-SAT architecture with modifications to accommodate the two payloads. RUAG Space GmbH as prime contractor will design the passive reflectometer payload, and Seibersdorf Laboratories is responsible for a novel dosimeter payload for measuring the space radiation environment during the PRETTY space mission. The dosimeter system will assess Total Ionizing Dose (TID) by the use of radiation integrating sensors, as well as LET spectral information, which is related to Single-Event Effects (SEE) in electronic components. Technical University Graz contributes the satellite platform based on the OPS-SAT architecture [2]. Furthermore TU Graz conducts the Manufacturing, Assembly, Integration and Test (MAIT) activities and will be in charge for the operation of the satellite. The scientific advisory group to be established will support the design of the payload and the evaluation of the raw data from the operation of the satellite. Within the present paper we will describe the architecture of the passive reflectometer payload within this 3U CubeSat mission and discuss operational routines and constraints.

Mesoscale ocean eddies are swirling oceanic features with diameters of the order of hundreds of kilometers contributing to different vital processes such as transferring energy across the ocean. This study demonstrates the feasibility of detecting signature of the eddies in space-borne Global Navigation Satellite Systems-Reflectometry (GNSS-R) measurements for the first time. The GNSS signal reflections off the ocean surface collected by the CYGNSS (Cyclone GNSS) mission, are collocated with wind speed estimates from ERA-Interim reanalysis and MetOp ASCAT A/B scatterometer over the mesoscale eddies documented in Aviso's mesoscale eddy trajectory atlas. The analysis of the bistatic radar cross section (σ0) and wind speed retrievals between March 2017 and January 2018, reports several recognizable patterns over the eddies. As an example, the impact of eddy-induced SST (Sea Surface Temperature) anomalies on the overlying wind field, are discussed using two instances of warm- and cold-core eddies near the south coast of Japan. As the most striking fact, the technique shows noticeable promise in detecting ocean eddies as a new application of space-borne GNSS Reflectometry.

Reflection power derived from the global navigation satellite system (GNSS) observations and its sensitivity to sea-ice concentration are investigated in this article. A corresponding experiment has been conducted during the Fram Strait cruise of the Norwegian research vesselLancein summer 2016. The dedicated setup with a GNSS Occultation Reflectometry Scatterometry (GORS) receiver and dual-polarization (left- and right-handed) antenna links recorded 1922 h of reflection events during the 20-day cruise of the ship. The antenna setup, mounted 25.0 m above the waterline, serves to acquire sea surface reflections at grazing angles below 30°. Within a 5-min coherent integration period, direct and reflected signal contributions can be separated. Except for the highest sea states, with roll angle changes of 20° peak to peak, the separation allows to retrieve the reflection power and quantifies it in cross-, co-, and cross-to-co-polar ratios. The sea-ice concentration is inverted from power ratios using a non-linear least-squares algorithm. Additional data on sea-ice concentration gathered by a watchman on the ship are used for validation. The inversion results have a 20% resolution in concentration and 3-h resolution in time. The validation shows that the cross- and cross-to-co-polar data are sensitive to the sea-ice concentration. The respective Pearson correlation of 0.75 and 0.67 further suggests studies to foster the application of the GNSS data for sea-ice reflectometry.

Since the nadir 1-second altimetry precision was first estimated to be of 56 cm when the PARIS concept was developed back in 1993 (it was assumed the interferometric processing of GPS P-code signals and a 4×4 m×m receiving antenna at 700 km orbital altitude) [1], many detailed analyses and experiments have been conducted leading to far more optimistic values, as low as some 15 cm (using the interferometric processing and a 1 m2antenna from 400 km altitude). This paper presents the instrument and concept advances that have allowed such improved expectations.

The swath of airborne or spaceborne sensors increases going from nadir to grazing observations. Especially radar altimeters relying on nadir observed backscatter signals are limited in swath. The forward scattered signals used in GNSS reflectometry (GNSS-R) allow to broaden the view for altimetry to grazing reflections. The anticipated GNSS Reflectometry, Radio Occultation and Scatterometry (GEROS-ISS) experiment [1] with a receiver setup aboard the International Space Station (ISS) is a main driver to study grazing angle altimetry. An early coastal experiment [2] and studies related to the CHAMP satellite mission [3], [4] revealed signatures of grazing reflection in GNSS observations and demonstrated altimetric application resolving ocean tides and ice sheet topography. These studies also showed that grazing reflection data can be retrieved using either coastal geodetic receivers or a spaceborne radio occultation setup. In both cases the differential delay between the reflected signal and the direct (line of sight) signal as well as the corresponding differential Doppler shift are sufficiently small. This means that the reflected signal is in multipath range to the direct signal and samples contain interferometric fringes that allow an altimetric inversion based on carrier phase precision. This multipath range of differential delay and Doppler applies to altitudes <; 50 m of the receiving antenna above the reflecting surface [2] or for grazing geometries with very small elevation angles (<; 1°) that occur during radio occultation events [4]. It is of major interest for GNSS-R applications to extend this range. Previous studies reported carrier phase retrieval mainly below 30° elevation in the reflection point [5], [6]. At higher elevations the diffuse part of the sea surface reflection usually prevents such retreivals. Therefore, grazing reflection considered here refer to elevation angles <; 30°. Considered altitudes include low earth orbits, 300-700 km above sea surface. It is an instrumental challenge to implement tracking algorithms of the reflected signal that allow carrier phase retrievals in this extended range.

In this paper, an approach based on Global Navigation Satellite System-Reflectometry (GNSS-R) is proposed for distinguishing sea ice and water from each other. The Delay-Doppler Map (DDM) of a GNSS signal reflected from sea ice and water show different spreading characteristics. The difference between two adjacent normalized DDMs is a differential DDM observable which provides information about the difference of two DDMs. Through studying whether the pixel number is above or below a predefined threshold, it is possible to determine the type of the reflected surface. The feasibility of the proposed differential DDM based method is validated using the ground-truth sea ice extent map provided by the National Snow and Ice Data Center, USA. The results demonstrate that the probability of detection can be up to 99.88%.

The characterization of global mean sea level is important to predict floods and to quantify water resources for human use and irrigation, especially in coastal regions. Recently, the application of global navigation satellite system reflectometry (GNSS-R) for water level monitoring has been successfully demonstrated. This paper focuses on the retrieval of sea surface height within a field experiment that was conducted at the Onsala Space Observatory (OSO) using the phase-based altimetry method. A continuous phase tracking algorithm, which relies on the GNSS amplitude and phase observations is proposed and works even under rough sea conditions at OSO's coast. Factors impacting the phase-based altimetry model, i.e., atmospheric propagation effects of the GNSS signals and influence of the GNSS-R observation instrument, are discussed. The relationship between the yield of coherent GNSS-R compared to the overall recorded events and the wind speed is investigated in detail. Ground-based sea-level measurements from June 10 to July 3, 2015 demonstrate that altimetric information about the reflecting water surface can be obtained with a root mean square error of 4.37 cm with respect to a reference tide gauge (TG) data set. The sea surface changes, derived from our field experiment and the reference TG, are highly correlated with a correlation coefficient of 0.93. The altimetric information can be retrieved even when the sea surface is very rough, corresponding to wind speeds up to 13 m/s. Moreover, the use of inexpensive conventional GNSS antennas shows that the system is useful for future large-scale sea level monitoring applications including numerous low-cost coastal ground stations.

GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed.

This paper presents a simulation study on Global Navigation Satellite System (GNSS) reflections focusing on a phase altimetric method for ocean topography retrieval. It examines carrier phase residuals of Earth-reflected GNSS signals in preparation for the GNSS Reflectometry Radio Occultation and Scatterometry experiment aboard the International Space Station (GEROS-ISS). The residuals' sensitivity to ocean topography (maximum of 2-m amplitude variation of global sea level) is shown. A trigonometric approach to determine the specular reflection point is proposed. Reflection events are simulated assuming different low Earth orbit receivers and GNSS-type transmitters. Suitable events for phase altimetry are assumed between 5° and 30° elevation lasting between 10 and 15 min with ground tracks length of > 3000 km. Typical along-track footprints (1 s integration time) have a length of about 5 km. Within the assumed elevation range the coherent footprint ellipse has a major axis between 1 and 6 km. A Master-Slave sampling is proposed to approximate large-scale delay and Doppler variations of the reflected signal (Slave channel) relative to the direct signal (Master channel). Slave residuals of an example event are simulated to retrieve a small-scale phase delay for ocean topography inversion. The signal-to-noise ratio restricts the quality of the topography results. Height precision on sub-decimeter level for 30-dB SNR is degraded up to a meter level for 20-dB SNR. Ionosphere-free linear combination allows keeping the precision level. Troposphere refraction degrades precision particularly at the low elevation limit. Precision improves toward higher elevations. The tolerance to ocean roughness decreases in the same way.

The rapid development of multi-Global Navigation Satellite Systems (GNSSs, e.g., BeiDou, Galileo, GLONASS, and GPS) and the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) brings great opportunities and challenges for real-time determination of tropospheric zenith total delays (ZTDs) and integrated water vapor (IWV) to improve numerical weather prediction, particularly for nowcasting or severe weather event monitoring. In this paper, we develop a multi-GNSS model to fully exploit the potential of observations from all currently available GNSSs for enhancing real-time ZTD/IWV processing. A prototype multi-GNSS real-time ZTD/IWV monitoring system is also designed and realized at the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences (GFZ) based on the precise point positioning technique. The ZTD and IWV derived from multi-GNSS stations are carefully analyzed and compared with those from collocated Very Long Baseline Interferometry and radiosonde stations. The performance of individual GNSS is assessed, and the significant benefit of multi-GNSS for real-time water vapor retrieval is also evaluated. The statistical results show that accuracy of several millimeters with high reliability is achievable for the multi-GNSS-based real-time ZTD estimates, which corresponds to about 1- to 1.5-mm accuracy for the IWV. The ZTD/IWV with improved accuracy and reliability would be beneficial for atmospheric sounding systems, particularly for time-critical geodetic and meteorological applications.

In the last years extreme flood events occur more frequently in Vietnam. Conventional satellite altimeters offer high altimetric accuracy but with insufficient spatial and temporal resolution. Ground based instrumentation enables a high altimetric accuracy with high temporal resolution, but for a point location only. GNSS-Reflectometry (GNSS-R) reveals new perspectives for water level monitoring. To test the possibility of using this innovative technique as a gauge instrument, a two weeks lasting measurement campaign was conducted in Vietnam, in February 2012. The data analysis showed the presence of multipath effects other than the water level in the direct and the reflected signals that deteriorate the results. Using the Empirical Mode Decomposition method (EMD), the water reflections are isolated from further multipath. With a model of these reflections, a correlation of 0.84 instead of 0.67 between the GNSS-R calculated water level changes and the recorded water level changes from a gauge instrument could be reached. Furthermore, applying EMD improves the standard deviation of the determined water heights from 12cm to 5cm.

GNSS reflectometry (GNSS-R) measurements were conducted in Sep 2012 over Lake Constance between Austria, Germany and Switzerland. A Zeppelin NT (New Technology) type airship equipped with a GORS (GNSS Occultation Reflectometry Scatterometry) receiver setup conducted flight transects (up to 64 km long) at about 500 m altitude above the lake surface. The setup uses two downwards tilted antennas with right-handed and left-handed circular polarisation (RHCP and LHCP) to acquire the reflected signal. An up-looking RHCP antenna acquires the signal on the direct link. The GORS receiver uses a Master-Slave sampling to process direct and reflected signals. An additional geodetic GNSS receiver provides direct link observations to calculate an airship trajectory with centimeter precision. A phase altimetric method is applied to the GORS samples in postprocessing. The height retrieval is initialised with an apriori height of the lake surface. Ancillary water gauge data provides reference at crossover points to adapt the apriori height. This height referencing allows to mitigate the phase-ambiguity induced bias. In this demonstration study interpolated water gauge data serve as reference. Height retrievals of an example event agree with lake surface undulations predicted by the GCG-05 (German Combined QuasiGeoid 2005) model. For RHCP (LHCP) observations a mean difference of 7 cm (5 cm) and a corresponding standard deviation of 3 cm (4 cm) is reported that confirm the phase altimetric performance.

With the availability of real-time high-rate GPS observations and precise satellite orbit and clock products, the interest in the real-time precise point positioning (PPP) technique has greatly increased to construct displacement waveforms and to invert for source parameters of earthquakes in real time. Furthermore, PPP ambiguity resolution approaches, developed in the recent years, overcome the accuracy limitation of the standard PPP float solution and achieve comparable accuracy with relative positioning. In this paper, we introduce the real-time PPP service system and the key techniques for real-time PPP ambiguity resolution. We assess the performance of the ambiguity-fixed PPP in real-time scenarios and confirm that positioning accuracy in terms of root mean square of 1.0-1.5 cm can be achieved in horizontal components. For the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes, the displacement waveforms estimated from ambiguity-fixed PPP and those provided by the accelerometer instrumentation are consistent in the dynamic component within few centimeters. The PPP fixed solution not only can improve the accuracy of coseismic displacements but also provides a reliable recovery of earthquake magnitude and of the fault slip distribution in real time.

A fundamental principle of local interaction of radio waves with a refractive spherical medium is formulated and illustrated using the radio occultation (RO) method of remote sensing of the atmosphere and the ionosphere of the Earth and the planets. In accordance with this principle, the main contribution to variations of the amplitude and the phase of radio waves propagating through a medium makes a neighborhood of a tangential point, where the gradient of the refractive index is perpendicular to the radio wave trajectory. A necessary and sufficient condition (a criterion) is established to detect the displacement of the tangential point from the radio ray perigee using analysis of the RO experimental data. This criterion is applied to the identification and the location of layers in the atmosphere and the ionosphere by the use of Global Positioning System RO data. RO data from the CHAllenge Minisatellite Payload (CHAMP) are used to validate the criterion introduced when significant variations of the amplitude and the phase of the RO signals are observed at the RO ray perigee altitudes below 80 km. The detected criterion opens a new avenue in terms of measuring the altitude and the slope of the atmospheric and ionospheric layers. This is important for the location determination of the wind shear and the direction of internal wave propagation in the lower ionosphere and possibly in the atmosphere. The new criterion provides an improved estimation of the altitude and the location of the ionospheric plasma layers compared with the backpropagation radio-holographic method previously used.

A comparison campaign to evaluate and compare troposphere delays from different ray-tracing software was carried out under the umbrella of the International Association of Geodesy Working Group 4.3.3 in the first half of 2010 with five institutions participating: the GFZ German Research Centre for Geosciences (GFZ), the Groupe de Recherche de Geodesie Spatiale, the National Institute of Information and Communications Technology (NICT), the University of New Brunswick, and the Institute of Geodesy and Geophysics of the Vienna University of Technology. High-resolution data from the operational analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) for stations Tsukuba (Japan) and Wettzell (Germany) were provided to the participants of the comparison campaign. The data consisted of geopotential differences with respect to mean sea level, temperature, and specific humidity, all at isobaric levels. Additionally, information about the geoid undulations was provided, and the participants computed the ray-traced total delays for 5$^{\circ}$elevation angle and every degree in azimuth. In general, we find good agreement between the ray-traced slant factors from the different solutions at 5$^{\circ}$elevation if determined from the same pressure level data of the ECMWF. Standard deviations and biases are at the 1-cm level (or significantly better for some combinations). Some of these discrepancies are due to differences in the algorithms and the interpolation approaches. If compared with slant factors determined from ECMWF native model level data, the biases can be significantly larger.

The ionosphere and atmosphere may have significant impacts on the high-stable navigational signals of the Global Positioning System (GPS) in the communication link satellite to satellite. The classification of the different types of the ionospheric impact on the phase and amplitude of the GPS signals at altitudes of 40-90 km is introduced using the CHAllenging Minisatellite Payload (CHAMP) radio occultation (RO) data. An analytical model is elaborated for the description of the radio wave propagation in the stratified ionosphere and atmosphere. The propagation medium consists of sectors having the spherically symmetric distributions of refractivity. The newly developed model presents analytical expressions for the phase path and refractive attenuation of radio waves. The model explains significant amplitude and phase variations at altitudes of 40-90 km of the RO ray perigee associated with the influence of the inclined ionospheric layers. An innovative eikonal acceleration technique is described and applied to the identification and location of the inclined ionospheric layers using the comparative analysis of the amplitude and phase variations of the RO signals.

GITEWS (German-Indonesian Tsunami Early Warning System) is one of the responses that were triggered by the tsunami offshore Sumatra in 2004. Since tsunamis are a global phenomenon, satellite based techniques like GNSS-Reflectometry (GNSS-R) are predestined as major components of future early warning systems. GNSS-R altimetry from space is expected to be applicable for tsunami detection. With a constellation of small satellites oceans could be monitored with high temporal and spatial coverage. Key components of such GNSS-R constellations are appropriate receivers. Therefore GFZ started activities related to the development of such receivers in cooperation with industry. Their performance was already successfully tested during several ground based campaigns. We review these measurements and introduce recent activities for airborne applications of GNSS-R. In addition we introduce results of a simulation study, where the tsunami detection performance of various GNSS-R micro satellite constellations was investigated for tsunami detection at the Indian Ocean and the Mediterranean.

Global positioning system (GPS) radio occultation (RO) observations aboard low earth orbiting (LEO) satellites provide a powerful tool for global atmospheric sounding. Almost continuously activated since mid-2001, the challenging minisatellite payload (CHAMP) GPS RO experiment provides up to 200 vertical atmospheric profiles per day. In this paper, we intercompare CHAMP RO humidity results and analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) with coinciding measurement of ozone and water vapor by airbus in-service aircraft (MOZAIC) data collected during aircraft ascents and descents. About 320 coinciding profiles with CHAMP were found from 2001 to 2006 (coincidence radius: 3 h, 300 km). Between about 650 and 300 hPa, the CHAMP-MOZAIC humidity bias is smaller than the ECMWF-MOZAIC bias. On the other hand, the standard deviation between MOZAIC and CHAMP humidity is slightly higher than that between MOZAIC and ECMWF through the entire altitude range. Apart from the water vapor validation (ascent and descent data), we also compare MOZAIC cruise data at an altitude of typically 10-11 km with CHAMP refractivity and temperature results (dry retrieval), and corresponding ECMWF analysis data. Whereas refractivity data from MOZAIC, CHAMP, and ECMWF show excellent agreement, the CHAMP temperature exhibits a cold bias of about 0.9 K in comparison to MOZAIC and ECMWF.