Can GNSS reflectometry detect precipitation over oceans?

For the first time, a rain signature in Global Navigation Satellite System Reflectometry (GNSS‐R) observations is demonstrated. Based on the argument that the forward quasi‐specular scattering relies upon surface gravity waves with lengths larger than several wavelengths of the reflected signal, a commonly made conclusion is that the scatterometric GNSS‐R measurements are not sensitive to the surface small‐scale roughness generated by raindrops impinging on the ocean surface. On the contrary, this study presents an evidence that the bistatic radar cross section σ0 derived from TechDemoSat‐1 data is reduced due to rain at weak winds, lower than ≈ 6 m/s. The decrease is as large as ≈ 0.7 dB at the wind speed of 3 m/s due to a precipitation of 0–2 mm/hr. The simulations based on the recently published scattering theory provide a plausible explanation for this phenomenon which potentially enables the GNSS‐R technique to detect precipitation over oceans at low winds

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

The novel space-borne Global Navigation Satellite System Reflectometry (GNSS-R) technique has recently shown promise in monitoring the ocean state and surface wind speed with high spatial coverage and unprecedented sampling rate. The L-band signals of GNSS are structurally able to provide a higher quality of observations from areas covered by dense clouds and under intense precipitation, compared to those signals at higher frequencies from conventional ocean scatterometers. As a result, studying the inner core of cyclones and improvement of severe weather forecasting and cyclone tracking have turned into the main objectives of GNSS-R satellite missions such as Cyclone Global Navigation Satellite System (CYGNSS). Nevertheless, the rain attenuation impact on GNSS-R wind speed products is not yet well documented. Evaluating the rain attenuation effects on this technique is significant since a small change in the GNSS-R can potentially cause a considerable bias in the resultant wind products at intense wind speeds. Based on both empirical evidence and theory, wind speed is inversely proportional to derived bistatic radar cross section with a natural logarithmic relation, which introduces high condition numbers (similar to ill-posed conditions) at the inversions to high wind speeds. This paper presents an evaluation of the rain signal attenuation impact on the bistatic radar cross section and the derived wind speed. This study is conducted simulating GNSS-R delay-Doppler maps at different rain rates and reflection geometries, considering that an empirical data analysis at extreme wind intensities and rain rates is impossible due to the insufficient number of observations from these severe conditions. Finally, the study demonstrates that at a wind speed of 30 m/s and incidence angle of 30°, rain at rates of 10, 15, and 20 mm/h might cause overestimation as large as ≈0.65 m/s (2%), 1.00 m/s (3%), and 1.3 m/s (4%), respectively, which are still smaller than the CYGNSS required uncertainty threshold. The simulations are conducted in a pessimistic condition (severe continuous rainfall below the freezing height and over the entire glistening zone) and the bias is expected to be smaller in size in real environments

A GNSS-R geophysical model function: Machine learning for wind speed retrievals

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

Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study

This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test (MTT) algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Strong agreement is observed between ice phenology records derived from CYGNSS and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Differences during freeze-up (i.e., the period starting with the first appearance of ice on the lake until the lake becomes fully ice covered) ranged from 3 to 21 days with a mean bias error (MBE) and mean absolute error (MAE) of 10 days, while those during breakup (i.e., the period beginning with the first pixel of open water and ending when the whole lake becomes ice-free) ranged from 3 to 18 days (MBE and MAE: 6 and 7 days, respectively). Observations during the breakup period revealed the sensitivity of GNSS reflected signals to the onset of surface (snow and ice) melt before the appearance of open water conditions as determined from MODIS. While the CYGNSS constellation is limited to the coverage of lakes between 38° S and 38° N, the approach presented herein will be applicable to data from other GNSS-R missions that provide opportunities for the monitoring of ice phenology from large lakes globally (e.g., Spire constellation of satellites).This research was undertaken thanks, in part, with support from the Global Water Futures Program funded by the Canada First Research Excellence Fund (CFREF)

Investigation on Geometry Computation of Spaceborne GNSS-R Altimetry over Topography: Modeling and Validation

The spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) offers versatile Earth surface observation. While the accuracy of the computed geometry, required for the implementation of the technique, degrades when Earth’s surface topography is complicated, previous studies ignored the effects of the local terrain surrounding the ideal specular point at a suppositional Earth reference surface. The surface slope and its aspect have been confirmed that it can lead to geolocation-related errors in the traditional radar altimetry, which will be even more intensified in tilt observations. In this study, the effect of large-scale slope on the spaceborne GNSS-R technique is investigated. We propose a new geometry computation strategy based on the property of ellipsoid to carry out forward and inverse calculations of path geometries. Moreover, it can be extended to calculate unusual reflected paths over versatile Earth’s topography by taking the surface slope and aspects into account. A simulation considering the slope effects demonstrates potential errors as large as meters to tens kilometers in geolocation and height estimations in the grazing observation condition over slopes. For validation, a single track over the Greenland surface received by the TechDemoSat 1 (TDS-1) satellite with a slope range from 0% to 1% was processed and analyzed. The results show that using the TanDEM-X 90 m Digital Elevation Model (DEM) as a reference, a slope of 0.6% at an elevation angle of 54 degrees can result in a geolocation inaccuracy of 10 km and a height error of 50 m. The proposed method in this study greatly reduces the standard deviation of geolocations of specular points from 4758 m to 367 m, and height retrievals from 28 m to 5.8 m. Applications associated with topography slopes, e.g., cryosphere could benefit from this method

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

The novel space-borne Global Navigation Satellite System Reflectometry (GNSS-R) technique has recently shown promise in monitoring the ocean state and surface wind speed with high spatial coverage and unprecedented sampling rate. The L-band signals of GNSS are structurally able to provide a higher quality of observations from areas covered by dense clouds and under intense precipitation, compared to those signals at higher frequencies from conventional ocean scatterometers. As a result, studying the inner core of cyclones and improvement of severe weather forecasting and cyclone tracking have turned into the main objectives of GNSS-R satellite missions such as Cyclone Global Navigation Satellite System (CYGNSS). Nevertheless, the rain attenuation impact on GNSS-R wind speed products is not yet well documented. Evaluating the rain attenuation effects on this technique is significant since a small change in the GNSS-R can potentially cause a considerable bias in the resultant wind products at intense wind speeds. Based on both empirical evidence and theory, wind speed is inversely proportional to derived bistatic radar cross section with a natural logarithmic relation, which introduces high condition numbers (similar to ill-posed conditions) at the inversions to high wind speeds. This paper presents an evaluation of the rain signal attenuation impact on the bistatic radar cross section and the derived wind speed. This study is conducted simulating GNSS-R delay-Doppler maps at different rain rates and reflection geometries, considering that an empirical data analysis at extreme wind intensities and rain rates is impossible due to the insufficient number of observations from these severe conditions. Finally, the study demonstrates that at a wind speed of 30 m/s and incidence angle of 30°, rain at rates of 10, 15, and 20 mm/h might cause overestimation as large as ≈0.65 m/s (2%), 1.00 m/s (3%), and 1.3 m/s (4%), respectively, which are still smaller than the CYGNSS required uncertainty threshold. The simulations are conducted in a pessimistic condition (severe continuous rainfall below the freezing height and over the entire glistening zone) and the bias is expected to be smaller in size in real environments

Weltraumgestützte GNSS-Reflektometrie: Fernerkundung der Ozeane und der Atmosphäre

Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technique that exploits the GNSS signals after being reflected off the Earth's surface. Monitoring of ocean wind is one of the applications and the main objective of recently launched satellite missions. This thesis aims at the development and characterization of these geophysical data products. Using the UK TechDemoSat-1 (TDS-1) measurements, a GNSS-R wind speed dataset is developed. The resulting data are evaluated in comparison to those obtained from the Advanced Scatterometer (ASCAT). Wind field estimates of European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis-Interim (ERA-Interim) and in situ observations from the Tropical Atmosphere Ocean (TAO) buoy array in the Pacific are taken as reference. The evaluation using ECMWF winds results in a root-mean-squared error (RMSE) and bias of 2.77 and -0.33 m/s, being comparable to those derived from ASCAT estimates, as large as 2.31 and 0.25 m/s, respectively. The derived winds show a higher level of robustness against rain with an RMSE and bias of 2.94 and -0.21 m/s over oceans under precipitations, in comparison to those obtained from ASCAT measurements, which are 3.16 and 1.03 m/s, respectively. Nonetheless, the BRCS reduces to lower values during rain events at low wind speeds. The signal attenuation by rain is investigated simulating GNSS-R delay-Doppler maps at different rain rates and reflection geometries. It is shown that the resulting bias is smaller than approximately 0.35 m/s (1%) at a wind speed of 30 m/s and an incidence angle of 30 degrees. At the same wind speed and incidence angle, the examination reports that a continuous rain at every cell of the signal propagation path, at rates of 10, 15 and 20 mm/h, could lead to overestimation of wind speed not larger than 0.65 m/s (2%), 1.00 m/s (3%), and 1.3 m/s (4%), respectively. It is concluded that rain attenuation is ignorable within the current GNSS-R applications. Despite a commonly made conclusion that the scatterometric GNSS‐R measurements are not sensitive to the small-scale waves generated by raindrops, this study presents the first evidence that the BRCS is reduced due to rain splash at weak winds, lower than approximately 6 m/s. The decrease in the BRCS derived from TDS-1 measurements is as large as approximately 0.7 dB at the wind speed of 3 m/s due to precipitation at smaller rates than 2 mm/hr. The simulations based on the recently proposed model approves that the rainsplash decreases the BRCS value at low wind speeds. The observed signature could potentially enable the GNSS‐R technique to detect precipitation over oceans after the further characterization studies recommended in this thesis. A model based on the feedforward neural networks is determined to invert the measurements to wind speed. The model results in a significant general improvement of 20% in the RMSE in comparison to the retrieval algorithm deployed initially. The advantages, leading to the achieved improvement, are discussed including the ability to model the effect of the different levels of the Effective Isotropic Radiated Power (EIRP) of GPS satellites. The derived Mean Absolute Error (MAE) of the satellite with Space Vehicle Number (SVN) of 34 is decreased by 32%. Finally, using CYclone GNSS (CYGNSS) measurements, the first satellite constellation fully dedicated to GNSS-R since December 2016, a signature of ocean mesoscale eddies is demonstrated. The Normalized BRCS (NBRCS) behaves conditionally with distinguishable fluctuations, either over the eddy center or in the edges. Statistical analyses are carried out which report that 28.6% of the CYGNSS measurement tracks show a correlation coefficient of 0.7 or more with the two observed patterns over the eddies. Using ancillary data, a strong inverse correlation of NBRCS with sensible heat flux and surface stress in specific conditions is demonstrated.Global Navigation Satellite System -Reflektometrie (GNSS-R) ist eine innovative Fernerkundungsmethode, welche GNSS-Signale nutzt, die von der Erdoberfläche reflektiert werden. Die Überwachung von Ozeanwinden auf globaler Skala ist eine Hauptanwendung und Missionsziel verschiedener kürzlich gestarteter Satelliten. Diese Doktorarbeit hat die Entwicklung und Charakterisierung von geophysikalischen GNSS-R-Datenprodukten zum Inhalt. Basierend auf TechDemoSat-1 (TDS-1)-Messungen wird ein GNSS-R-Windgeschwindigkeitsdatensatz erstellt. Die Ergebnisse werden mit Daten vom Advanced Scatterometer (ASCAT) verglichen. Zusätzlich werden Windfeldschätzungen von ERA-Interim (ECMWF, European Centre for Medium-Range Weather forecasts Reanalysis-Interim) und In-situ-Beobachtungen des TAO (Tropical Atmosphere Ocean) Bojenfeldes im Pazifik als Referenz herangezogen. Der Vergleich zu ECMWF-Winden ergibt ein RMSE (Root Mean Square Error) und Bias von 2,77 und -0,33 m/s. Diese Werte sind mit denen vergleichbar, die aus ASCAT-Daten abgeleitet wurden (2,31 bzw. 0,25 m/s). Die TDS-1-Winde zeigen ein höheres Maß an Robustheit gegenüber Regen mit einem RMSE und Bias von 2,94 und -0,21 m/s über Ozeanen bei Niederschlägen im Vergleich zu den ASCAT-Messungen mit 3,16 bzw. 1,03 m/s. Dennoch reduziert der BRCS sich bei Niederschlag unter niedrigen Windgeschwindigkeiten. Die Signaldämpfung durch Regen wird untersucht, indem GNSS-R DDM (Delay-Doppler Maps) bei verschiedenen Regenraten und Reflexionsgeometrien simuliert werden. Es wird gezeigt, dass der resultierende Bias bei einer Windgeschwindigkeit von 30 m/s und einem Einfallswinkel von 30 Grad kleiner als 0,35 m/s (1%) ist. Bei gleicher Windgeschwindigkeit und gleichem Einfallswinkel ergab die Untersuchung, dass ein kontinuierlicher Regen in jeder Zelle des Signalausbreitungsweges mit Beträgen von 10, 15 und 20 mm/h zu einer Überschätzung der Windgeschwindigkeit kleiner als 0,65 m/s (2%), 1,00 m/s (3%) bzw. 1,3 m/s (4%) führen kann. Daraus wird geschlussfolgert, dass die Signaldämpfung durch Regen in den aktuellen GNSS-R-Anwendungen vernachlässigt werden kann. Regentropfen auf der Meeresoberfläche verändern jedoch deren Rauigkeit. Im allgemeinen wird angenommen, dass GNSS-R-Messungen unempfindlich gegenüber den von Regentropfen erzeugten kleinen Wellen sind. Die vorliegende Studie liefert jedoch erstmals den Beweis dafür, dass sich der BRCS aufgrund von Regentropfen bei schwachem Wind von weniger als 6 m/s verringert. Die aus TDS-1-Messungen abgeleitete Abnahme des BRCS beträgt bei Niederschlagsraten kleiner 2 mm/h und einer Windgeschwindigkeit von 3 m/s etwa 0,7 dB. Die Simulationen, die auf einem kürzlich entwickelten physikalischen Modell basieren, bestätigen, dass Regentropfen den BRCS bei niedrigen Windgeschwindigkeiten verringern. Diese erstmals beobachtete Signatur könnte dazu führen, dass die GNSS-Reflektometrie nach den in dieser Arbeit empfohlenen weiteren Charakterisierungsstudien zur Messung von Niederschlägen über Ozeanen eingesetzt werden kann. Ein Modell, das auf ANN (Artificial Neural Network) basiert, wird entwickelt, um die GNSS-R-Messungen in Windgeschwindigkeiten umzuwandeln. Das Modell führt zu einer signifikanten Verbesserung des RMSE um 20% im Vergleich zu dem ursprünglich verwendeten Berechnungsschema. Die Vorteile, die zu der erreichten Verbesserung führen, werden diskutiert, einschließlich der Fähigkeit, den Effekt der verschiedenen EIRP (Effective Isotropic Radiated Power) von GPS-Satelliten zu modellieren. Beispielsweise wird der abgeleitete MAE (Mean Absolute Error) des GPS-Satelliten mit der Space Vehicle Number (SVN) 34 um 32% verringert. Schließlich wird mit GNSS-Messungen von CYGNSS, der ersten GNSS-R-Mehrsatelliten-konstellation, die seit Dezember 2016 im Orbit ist, eine Signatur von mesoskaligen Ozeanwirbeln nachgewiesen. Der normalisierte BRCS verhält sich unterscheidbar unterschiedlich über dem Wirbelzentrum und den Randgebieten. Es werden statistische Analysen durchgeführt, aus denen hervorgeht, dass 28,6% der CYGNSS-Messspuren einen Korrelationskoeffizienten von 0,7 oder mehr mit dem beobachteten Muster über den Wirbeln aufweisen. Anhand von Zusatzdaten wird eine starke inverse Korrelation von normalisierte BRCS mit dem fühlbaren Wärmestrom (surface heat flux) und der Oberflächenspannung unter bestimmten Bedingungen gezeigt

Unsupervised Machine Learning for GNSS Reflectometry Inland Water Body Detection

Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and position of water bodies are crucial to the ecosystems. Mapping water bodies at a global scale is a challenging task due to the global variety of terrains and water surface. However, the sensitivity of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to different land surface properties offers the potential to detect and monitor inland water bodies. The extensive dataset available in the Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, is used in our investigation. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we show its capability of detecting and mapping inland water bodies. Both bistatic radar cross section (BRCS) and signal-to-noise ratio (SNR) can be used to detect, identify, and map the changes in the position and extent of inland waterbodies. We exploit the potential of unsupervised machine learning algorithms, more specifically the clustering methods, K-Means, Agglomerative, and Density-based Spatial Clustering of Applications with Noise (DBSCAN), for the detection of inland waterbodies. The results are evaluated based on the Copernicus land cover classification gridded maps, at 300 m spatial resolution. The outcomes demonstrate that CYGNSS data can identify and monitor inland waterbodies and their tributaries at high temporal resolution. K-Means has the highest Accuracy (93.5%) compared to the DBSCAN (90.3%) and Agglomerative (91.6%) methods. However, the DBSCAN method has the highest Recall (83.1%) as compared to Agglomerative (82.7%) and K-Means (79.2%). The current study offers valuable insights and analysis for further investigations in the field of GNSS-R and machine learning

Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms

Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6–12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

This article introduces the first use of global navigation satellite system (GNSS) reflectometry for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-noise ratio (SNR) values obtained from the cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Good agreement was achieved between ice phenology records derived from CYGNSS data and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The CYGNSS timings for freeze-up, i.e., the period starting with the first appearance of ice on the lake (freeze-up start; FUS) until the lake becomes fully ice covered (freeze-up end; FUE), as well as those for breakup, i.e., the period beginning with the first pixel of open water (breakup start; BUS) and ending when the whole lake becomes ice-free (breakup end; BUE), were validated against the phenology dates derived from MODIS images. Mean absolute errors are 7, 5, 10, 4, and 5 days for FUS, FUE, BUS, BUE, and ice cover duration, respectively. Observations revealed the sensitivity of GNSS reflected signals to surface melt prior to the appearance of open water conditions as determined from MODIS, which explains the larger difference of 10 days for BUS