Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant.Postprint (author's final draft

Estimation of swell height using spaceborne GNSS-R data from eight CYGNSS satellites

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.This work was supported in part by the National Natural Science Foundation of China under Grant 42174022, in part by the Future Scientists Program of China University of Mining and Technology under Grant 2020WLKXJ049, in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_2003, in part by the Programme of Introducing Talents of Discipline to Universities, Plan 111, Grant No. B20046, and in part by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202106420009).Peer ReviewedPostprint (published version

Azimuthal Dependence of GNSS‐R Scattering Cross‐Section in Hurricanes

Global Navigation Satellite System‐Reflectometry (GNSS‐R) measurements of the ocean surface are sensitive to roughness scales ranging from a few cms to several kms. Inside a hurricane the surface roughness changes drastically due to varying sea age and fetch length conditions and complex wave‐wave interactions caused by its cyclonic rotation and translational motion. As a result, the relationship between the surface roughness at different scale sizes becomes azimuthally dependent, as does the relationship between scattering cross‐section and wind speed as represented by a Geophysical Model Function (GMF). In this work, the impact of this azimuthal variation on the scattering cross‐section is assessed. An empirical GMF is constructed using measurements by the NASA CYclone GNSS (CYGNSS) matched to HWRF reanalysis surface winds for 19 hurricanes in 2017 and 2018. The analysis reveals a 2–8% variation in scattering cross‐section due to azimuthal location, and the magnitude of the azimuthal dependence is found to grow with wind speed.Plain Language SummaryGlobal Navigation Satellite System‐Reflectometry (GNSS‐R) is a technique of studying reflected GPS signals to extract useful information about the surface. CYGNSS is the first of its kind GNSS‐R constellation mission selected by NASAs earth venture program. The goal of the mission is to understand inner core processes in hurricanes by making accurate surface wind speed measurements there. Wind speed at the surface is determined using a GMF that maps the reflection measurement to a wind speed. Due to the complex nature of sea state and wave interactions inside a hurricane, measured scattering cross‐section depends on the azimuthal location of the measurement inside the hurricane system. A modified GMF is proposed here that accounts for the azimuthal dependence. The model is developed by matching up CYGNSS measurements to hurricane winds estimated by the NOAA HWRF model for 19 hurricanes during 2017 and 2018. The new GMF accounts for a 2–8% variation in the measurements due to azimuthal location which increases with wind speed.Key PointsAzimuthal variations of GNSS‐R scattering cross‐section in hurricanes are modeled with sinusoidal harmonicsThe azimuthal harmonics explain 2–8% of the overall variation in scattering cross‐sectionThe magnitude of the azimuthal harmonics increases with increasing wind speedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156153/2/jgrc24060.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156153/1/jgrc24060_am.pd

GNSS Application in Retrieving Sea Wind Speed

In traditional Global Navigation Satellite System (GNSS) application, the reflected GNSS signals from Earth’s surface generally are considered as an interference source to be suppressed or removed. Recently, a new idea which treats the reflected GNSS signal as opportunity source of remote sensing has been proposed to monitor Earth’s physical parameters. This technique is called as GNSS-Reflectometry (GNSS-R) which has the advantages of low-power, -mass and -cost. With the development and modernization of GPS, Galileo, GLONASS, and BeiDou system, spaceborne GNSS could significantly improve the temporal-spatial resolution by receiving and processing the reflected signal from multiple satellites. This chapter mainly describes this new bi-static remote sensing technique. First, basic theories of GNSS-R including spatial geometry, polarization, and scattering model of reflected signal are discussed; second, spaceborne receivers and fast-response processing methods are reviewed and analyzed; finally, the empirical models retrieving wind speed are proposed and demonstrated using the DDM data from the UK-TechDomeSat-1 satellite. Based on the discussion of this chapter, it could be concluded that although GNSS-R still has some key challenges which have to be addressed, it could be an optimal choice of remote sensing in some special conditions, such as the tropical cyclone

Investigating the Sensitivity of Spaceborne GNSS-R Measurements to Ocean Surface Winds and Rain

Earth remote sensing using reflected Global Navigation Satellite System (GNSS) signals is an emerging trend, especially for ocean surface wind measurements. GNSS-Reflectometry (GNSS-R) measurements of ocean surface scattering cross section are directly related to the surface roughness at scale sizes ranging from small capillary waves to long gravity waves. These roughness scales are predominantly due to swell, surface winds and other meteorological phenomena such as rain. In this study we are interested in understanding and characterizing the impact of these phenomena on GNSS-R signals in order to develop a better understanding of the geophysical parameters retrieved from these measurements. In the first part of this work, we look at GNSS-R measurements made by the NASA Cyclone Global Navigation Satellite System (CYGNSS) for developing an effective wind retrieval model function for GNSS-R measurements. In a fully developed sea state, the wind field has a constant speed and direction. In this case, a single Fully Developed Seas (FDS) Geophysical Model Function (GMF) is constructed which relates the scattering cross-section to the near surface wind speed. However, the sea age and fetch length conditions inside a hurricane are in general not consistent with a fully developed sea state. Therefore, a separate empirical Young Sea Limited Fetch (YSLF) GMF is developed to represent the conditions inside a hurricane. Also, the degree of under development of the seas is not constant inside hurricanes and conditions vary significantly with azimuthal location within the hurricane due to changes in the relative alignment of the storms forward motion and its cyclonic rotation. The azimuthal dependence of the scattering cross-section is modelled and a modified azimuthal YSLF GMF is constructed using measurements by CYGNSS over 19 hurricanes in 2017 and 2018. Next, we study the impact of rain on CYGNSS measurements. At L-band rain has a negligible impact on the transmitted signal in terms of path attenuation. However, there are other effects due to rain, such as changes in surface roughness and rain induced local winds, which can significantly alter the measurements. In this part of the study we propose a 3-fold rain model for GNSS-R signals which accounts for: 1) attenuation; 2) surface effects of rain; and 3) rain induced local winds. The attenuation model suggests a total of 96% or greater transmissivity at L-Band up to 30mm/hr of rain. A perturbation model is used to characterize the other two rain effects. It suggests that rain is accompanied by an overall reduction in the scattering cross-section of the ocean surface and, most importantly, this effect is observed only up to 15 m/s of surface winds, beyond which the gravity capillary waves dominate the scattering in the quasi-specular direction. This work binds together several rain-related phenomena and enhances our overall understanding of rain effects on GNSS-R measurements. Finally, one of the important objectives for the CYGNSS mission is to provide high quality global scale GNSS-R measurements that can reliably be used for ocean science applications. In this part of the work we develop a Neural Network based quality control filter for automated outlier detection for CYGNSS retrieved winds. The primary merit of the proposed Machine Learning (ML) filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate threshold quality flags for each one.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166140/1/rajibala_1.pd

GNSS-R as a source of opportunity for remote sensing of the cryosphere

This work evaluates the potential use of signals from the Global Navigation Satellite Systems (GNSS) that scatter off the Earth surface for the retrieval of geophysical information from the cryosphere. For this purpose, the present study is based on data collected with a dedicated reflectometry GNSS receiver during two field campaigns, which were focused on two types of characteristic surfaces of the cryosphere: thin sea ice covers and thick dry snow accumulations. During the first experiment, the complete process of formation, evolution and melting of sea ice was monitorized for more than seven months in a bay located in Greenland. This type of ice is typically characterized by its thickness, concentration and roughness. Different observables from GNSS reflections are analyzed to try to infer these properties. The ice thickness is linked to the free-board level, defined as the height of the sea ice surface. Accurate phase altimetry is achieved, showing good agreement with an Arctic tide model. In addition, the long term results of ellipsoidal height retrievals are consistent with the evolution of the ice surface temperature product given by MODIS, which is a key parameter in the rate of growth of sea ice. On the other hand, the presence of salinity in the sea ice modifies its dielectric properties, resulting in different amplitude and phase for the co- and cross-polar components of the complex Fresnel coefficients. The polarimetric measurements obtained show good agreement with visual inspections of ice concentration from an Arctic weather station. Finally, the shape of the reflected signals and its phase dispersion are tested as potential signatures of surface roughness. For comparison, ice charts of the experimental area are employed. In particular, maximums in roughness given by the GNSS observables coincide with fast ice events. Fast ice is defined as ice anchored to the coast, where the tidal movements contribute to the development of strange patterns, cracks, and fissures on its surface, thus consistent with the GNSS-R roughness retrievals. The second experiment took place on Antarctica, monitoring a pristine snow area which is well-known for the calibration of remote sensing instruments. Due to the relative stability of the snow layers, the data acquisition was limited to ten continuous days. Interferometric beats were found after a first analysis of the amplitude from the collected signals, which were consistent with a multipath model where the reflector lies below the surface level. Motivated by these results, a forward model is developed that reconstructs the complex received signal as a sum of a finite number of reflections, coming from different snow layers (a snow density profile obtained from in-situ measurements). The interferometric information is then retrieved from the spectral analysis applied to time series from both real and modeled signals (lag-holograms). We find that the frequency bands predicted by the model are in general consistent with the data and the lag-holograms show repeatability for different days. Then, we attempt a proper inversion of the collected data to determine the dominant layers of the dry snow profile that contribute to L-band reflections, which are related to significant gradients of snow density/permittivity.Aquest treball avalua el possible ús dels senyals dels sistemes mundials de navegació per satèl lit (GNSS) que es reflecteixen a la superfície terrestre, per a l’extracció de la informació geofísica de la criosfera. Amb aquest propòsit, el present estudi es basa en dades recollides amb un reflectòmetre GNSS durant dues campanyes experimentals, centrades en dos tipus de superfícies característiques de la criosfera: cobertes de gel marí i gruixudes acumulacions de neu seca. En el primer experiment, el procés complet de formació, evolució i fusió del gel marí va ser monitoritzat durant més de set mesos a una badia situada a Groenlàndia. Aquest tipus de gel es caracteritza típicament amb el seu gruix, concentració i rugositat. Diferents observables de les reflexions GNSS són analitzats per tractar de fer una estimació d’aquestes propietats. El gruix de gel està relacionat amb el nivell de francbord, que a la seva vegada està relacionat amb l’alçada de la superfície de gel marí. S’ha aconseguit altimetria de fase precisa, que mostra correlació amb un model de marea de l’Àrtic. A més, els resultats a llarg termini de l’alçada elipsoidal segueixen l’evolució de les mesures de temperatura de superfície de gel donades per MODIS. La temperatura és un paràmetre clau en el ritme de creixement del gel marí. Per altra banda, la presència de sal a aquest tipus de gel modifica les seves propietats dielèctriques, el que implica variacions d’amplitud i fase per als coeficients de Fresnel complexos amb polaritzacions oposades. Les mesures polarimètriques obtingudes mostren concordança amb els valors de concentració de gel obtinguts des d’una estació meteorològica propera. Finalment, la forma de la senyal reflectida i la dispersió de la seva fase s’evaluen com a potencials indicadors de la rugositat de superfície. Per a la seva comparació, es fan servir mapes del gel de la zona experimental. En concret, els valors màxims a la rugositat estimada a partir pels observables GNSS coincideixen amb el gel fixe, que es refereix a gel ancorat a la costa, on els moviments de les marees contribueixen al desenvolupament de patrons estranys, esquerdes i fissures en la seva superfície. El segon experiment es va dur a terme a l’Antàrtida, monitoritzant una àrea de neu pristina que és ben coneguda per al calibratge d’instruments de teledetecció. A causa de la relativa estabilitat de les capes de neu, l’adquisició de dades es va limitar a deu dies consecutius. Es van trobar pulsacions interferomètriques a partir d’un primer anàlisi de l’amplitud de les senyals recollides, les quals eren compatibles amb un model de propagació multicamí a on el reflector es troba per sota del nivell de superfície. Com a conseqüència d’aquests resultats, s’ha desenvolupat un model que reconstrueix el senyal complexe rebut com la suma d’un nombre finit de reflexions, procedents de diferents capes de neu (determinat per mesures locals). La informació interferomètrica es recupera després de l’anàlisi espectral aplicat a les sèries temporals tant de les senyals reals, com de les modelades (lag-hologrames). Trobem que les bandes de freqüències predites pel model són en general consistents amb les dades i que els lag-hologrames mostren repetibilitat per dies diferents. Posteriorment, es realitza un anàlisi de les dades recollides per determinar les capes dominants del perfil de neu seca que contribueixen a les reflexions en banda L, i que a la seva vegada, estan relacionades amb gradents significatius de densitat/permitivitat.Este trabajo evalúa el posible uso de las señales de los sistemas globales de navegación por satélite (GNSS) que se reflejan en la superficie terrestre para la extracción de información geofísica de la criosfera. Con este propósito, el presente estudio se basa en datos recogidos con un reflectómetro GNSS durante dos campañas experimentales, centradas en dos tipos de superficies características de la criosfera: capas de hielo marino y gruesas acumulaciones de nieve seca. Durante el primer experimento, el proceso completo de formación, evolución y fusión del hielo marino fue monitorizado durante más de siete meses en una bahía ubicada en Groenlandia. Este tipo de hielo se caracteriza típicamente por su grosor, concentración y rugosidad. Diferentes observables de las reflexiones GNSS son analizados para tratar de estimar dichas propiedades. El espesor de hielo está relacionado con el nivel de francobordo o borda libre, que a su vez está relacionado con la altura de la superficie de hielo marino. Se ha logrado altimetría de fase precisa, mostrando correlación con un modelo de marea del Ártico. Además, los resultados a largo plazo de la altura elipsoidal siguen la evolución de las mediciones de temperatura de superficie de hielo proporcionadas por MODIS. La temperatura es un parámetro clave en el ritmo de crecimiento del hielo marino. Por otro lado, la presencia de sal en este tipo de hielo modifica sus propiedades dieléctricas, lo que implica variaciones en las amplitudes y fases de los coeficientes complejos de Fresnel con polarizaciones opuestas. Los resultados polarimétricos concuerdan con los valores de concentración de hielo obtenidos mediante inspección visual desde una estación meteorológica cercana. Por último, la forma de la señal reflejada y la dispersión de su fase son evaluadas como potenciales indicadores de la rugosidad de superficie. Para su comparación, se emplean mapas del hielo de la zona experimental. En particular, valores máximos de rugosidad estimada por los observables GNSS coinciden con hielo fijo, que se refiere al hielo anclado a la costa, donde los movimientos de las mareas contribuyen al desarrollo de patrones extraños, grietas y fisuras en su superficie. El segundo experimento se llevó a cabo en la Antártida, monitorizando una área de nieve pristina que es bien conocida para la calibración de instrumentos de teledetección. Debido a la relativa estabilidad de las capas de nieve, la adquisición de datos se limitó a diez días consecutivos. Se encontraron pulsaciones interferométricas a partir de un primer análisis de la amplitud de las señales recibidas, las cuales eran compatibles con un modelo de propagación multicamino donde el reflector se encuentra por debajo del nivel de la superficie. Como consecuencia de estos resultados, se ha desarrollado un modelo que reconstruye la señal recibida como la suma de un número finito de reflexiones, procedentes de diferentes capas de nieve (caracterizados por mediciones locales). La información interferométrica se recupera después del análisis espectral aplicado a las series temporales tanto de las señales reales, como de las modeladas (lag-hologramas). Encontramos que las bandas de frecuencias predichas por el modelo son en general consistentes con los datos y que los lag-hologramas muestran repetibilidad para días diferentes. Posteriormente, se realiza un análisis de los datos recogidos para determinar las capas dominantes del perfil de nieve seca que contribuyen a las reflexiones en banda L, y que a su vez, están relacionadas con gradientes significativos de densidad/permitivida