An Innovative Signal Processing Scheme for Spaceborne Integrated GNSS Remote Sensors

This paper presents a concept, “Cookie,” for a satellite particularly suited for dense spatial sampling by future Global Navigation Satellite Systems (GNSS) remote sensing constellations. Solely based on the reception of the direct and reflected signals transmitted by the GNSS, the satellite can provide observations of the Earth useful for a wide range of scientific and operational applications. The Cookie is capable of receiving direct and reflected signals, in both right- and left-hand circular polarizations, from any of the GNSS systems, and from virtually any arrival direction in both the upper and lower field-of-view hemispheres, i.e., providing nearly full 4π spherical coverage toward any navigation satellite not eclipsed by the Earth. The on-board remote sensing payload produces interferometric observables, e.g., the auto- and cross-correlation of several adequate combinations of the received signals. The interferometric processing is general, the same, and transparent, with respect to any of the signals transmitted by the current or planned GNSS systems. The instrument can implement any other suitable processing schemes too. Such payload can provide, in parallel, accurate GNSS Radio-Occultation (GNSS-RO) observations of the atmosphere and forward and backward GNSS Reflectometry (GNSS-R) measurements from the Earth surface. Several Cookies can be piled up inside the fairing of a rocket minimizing the launch cost of a constellation. A constellation of three Cookies has been simulated and its sampling performance characterized. The key concepts of the Cookie payload could be demonstrated through ESA's GNSS Reflectometry, Radio-Occultation, and Scatterometry experiment on board the International Space Station (GEROS-ISS).

Global Navigation Satellite System (GNSS) Radio Occultation measurements can remote sensing Earth's neutral atmosphere and ionosphere. Meanwhile, another GNSS remote sensing technique named GNSS Reflectometry (GNSS-R) has been developed rapidly, which can retrieve sea surface wind speed. The GNSS Occultation Sounder (GNOS) II instrument has been designed to integrate these two GNSS remote sensing techniques in one payload of the third batch FengYun-3 (FY-3) satellites. This paper presents the advancements in research of GNOS II and instrument performance. The qualified module of GNOS II has been manufactured by National Space Science Center (NSSC), Chinese Academy of Sciences. We show that the phase center variety is low enough for the new designed precise orbit determination (POD) antenna to increase the accuracy of precise orbit determination. A wide coverage atmosphere occultation antenna and a 65nm technics ASIC are used in the new generation GNOS instrument. As an important improvement, the GNOS II can generate Delay Doppler Maps (DDMs) of GNSS signals scattered from the ocean surface. The result of test shows that the GNOS II instrument has high performance in precision of code, carrier phase and real-time navigation.

Implementing the International GNSS Service 2021+ Strategic Plan: Vision and Use Cases

When compared to the wide range of atmospheric sensing techniques, global navigation satellite system (GNSS) offers the advantage of operating under all weather conditions, is continuous, with high temporal and spatial resolution and high accuracy, and has long-term stability. The utilisation of GNSS ground networks of continuous stations for operational weather and climate services is already in place in many nations in Europe, Asia, and America under different initiatives and organisations. In Africa, the situation appears to be different. The focus of this paper is to assess the conditions of the existing and anticipated GNSS reference network in the African region for meteorological applications. The technical issues related to the implementation of near-real-time (NRT) GNSS meteorology are also discussed, including the data and network requirements for meteorological and climate applications. We conclude from this study that the African GNSS network is sparse in the north and central regions of the continent, with a dense network in the south and fairly dense network in the west and east regions of the continent. Most stations lack collocated meteorological sensors and other geodetic observing systems as called for by the GCOS Reference Upper Air Network (GRUAN) GNSS Precipitable Water Task Team and the World Meteorological Organization (WMO). Preliminary results of calculated zenith tropospheric delay (ZTD) from the African GNSS indicate spatial variability and diurnal dependence of ZTD. To improve the density and geometry of the existing network, countries are urged to contribute more stations to the African Geodetic Reference Frame (AFREF) program and a collaborative scheme between different organisations maintaining different GNSS stations on the continent is recommended. The benefit of using spaced based GNSS radio occultation (RO) data for atmospheric sounding is highlighted and filling of geographical gaps from the station-based observation network with GNSS RO is also proposed.

The dynamic distribution and change of terrestrial water, manifested in wetlands that support a wide range of vegetation types and ecosystems, and also in floodplains that are prone to inundations, are very important to the understanding of our changing climate and our ability to mitigate risk. At present all of the existing measurement techniques have serious limitations in the ability to observe terrestrial water bodies globally and at the spatial and temporal scales required to fully capture their dynamics. In the last few years a number of studies in the community of Global Navigation Satellite Systems (GNSS) Reflectometry have been focusing on reflections over wetlands and inundated areas [Zuffada et al, 2016; Nghiem et al, 2017; Chew et al., 2018; Morris et al., 2019], particularly since data analysis of the CYclone GNSS (CYGNSS) mission began showing the ability to resolve small-scale land features such as rivers and bodies of water even partially obstructed by vegetation. CYGNSS is a constellation of 8 microsatellites, that provides significantly increased temporal sampling and revisit rate in the tropical latitudinal band as compared to traditional monolithic instruments, thus enabling a new observing strategy for capturing dynamic water events. In [Zuffada et al., 2017; Nghiem et al., 2017], based on reflected signal characteristics such as peaked (limited spread in delay and doppler) and symmetric shape, and very high reflected peak power, it was hypothesized that over wetlands there are strong coherent specular reflections in the collection area of the signal, originating from (even small) areas of standing water, resulting in the measurements’ magnified sensitivity to water because of its high electric permittivity compared to dry land and/or vegetation. Plots of peak power, corresponding to CYGNSS measurements’ specular points, aggregated over a period of time, and displayed over large regions with complex hydrology such as the Amazon basin clearly showed the potential of CYGNSS to map surface hydrology of intricate scenes at the continental level. At the regional scale, availability of in-situ and other correlative data have led to introduce thresholds in peak power values that differentiate between two binary states, i.e. dry and wet associated with inundations [Chew et al., 2018; Morris et al., 2019]. The limitations of the assumptions used to map wetlands have been analyzed in [Loria et al., 2019 (in preparation)] and shown to be traceable to the complex nature of the scattering from inhomogeneous scenes where the local water topology, surface topography and local meteorology can affect the mix of coherent and incoherent scattering, thus producing highly variable peak power that confounds the measurements. This communication summarizes our best understanding of the retrieval accuracy of GNSS reflectometry for dynamic monitoring of wetlands and inundations. It is based on analysis of actual CYGNSS constellation data acquired over terrestrial water bodiesand discusses the limitations in estimating surface water globally when inhomogeneities at the small scales are at play. Comparisons and cross-validations have been performed with measurements from ALOS-2 and Sentinel-1 data. References Chew, C., Reager, J.T. and Small, E., 2018. CYGNSS data map flood inundation during the 2017 Atlantic hurricane season. Scientific Reports (Nature Publisher Group), 8, pp.1-8. Loria, E. et al. Analysis of Wetland Extent Retrieval Accuracy Using CYGNSS Data. In preparation, 2019. Morris, M., C. Chew, J.T. Reager, R. Shah, C. Zuffada. A Novel Approach to Monitoring Wetland Dynamics using CYGNSS: Everglades Case Study. Remote Sensing of Environment, in press. 2019. Nghiem SV, Zuffada C, Shah R, Chew C, Lowe ST, Mannucci AJ, Cardellach E, Brakenridge GR, Geller G, Rosenqvist A. Wetland monitoring with Global Navigation Satellite System reflectometry. Earth and Space Science. 2017 Jan 1;4(1):16-39. Zuffada, C., Chew, C., Nghiem, S.V., Shah, R., Podest, E., Bloom, A.A., Koning, A., Small, E., Schimel, D., Reager, J.T. and Mannucci, A., 2016, August. Advancing Wetlands Mapping and Monitoring with GNSS Reflectometry. In Living Planet Symposium (Vol. 740, p. 83). Zuffada, C., Chew, C. and Nghiem, S.V., 2017. GNSS-R algorithms for wetlands observations. In IGARSS 2017-2017 IEEE International Geoscience and Remote Sensing Symposium (pp. 1126 – 1129), Fort Worth, TX.

Over recent years there has been increasing interest from commercial companies to produce observations of the atmosphere using GNSS‐RO (Global Navigation Satellite System – Radio Occultation) techniques. One such company, Spire, has achieved this by launching a large number of cubesats that operate in a constellation to observe the atmosphere. Over a period of 18 months, these data have been assessed in comparison to current GNSS‐RO data. The differences between the observations and a short‐range forecast (the innovations) indicate that the data are closely comparable to currently operational GNSS‐RO data below 30 km. Above this height, the data are noisier than current satellites. Below 30 km the data often have a smaller standard deviation than other observations, which can be achieved by using increased vertical smoothing. Although the vertical correlation length‐scales for Spire data are larger than for the current data, they do not seem excessively large. Given the good performance of the innovation statistics for the Spire data, impact trials were run in the Met Office's NWP system. Tests were run assimilating data from the current observational network, and with no GNSS‐RO data, adding data from Spire in various proportions, and replacing the data from the Metop‐C satellite with data from Spire. It was found that there is a substantial forecast benefit from assimilating Spire data. In accordance with previous work, the forecast impact of increasing the data volume is roughly proportional to the logarithm of the total amount of GNSS‐RO data assimilated. Theoretical arguments suggest a different relationship with the number of observations, but these do not fit the results well.

GPS triple-frequency undifferenced and uncombined precise orbit determination with the consideration of receiver time-variant bias

This paper investigates weak tsunami detection using noisy sea surface height (SSH) measurement data such as that recorded by a satellite-borne receiver and produced by the Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) technique. By studying the patterns of many real tsunamis, a triangle function is proposed to model the shape of tsunami lead waves for theoretical studies. Very similar simulation results are produced when the modeled and real tsunami data are used separately, indicating good modeling accuracy. A bin averaging (BA) technique is proposed for detecting a tsunami, and a hypothesis testing method is developed to decide whether a tsunami is present by examining the BA outputs against a predefined threshold. Mathematical formulas are derived for the probability of detection (PD) and the probability of false alarm (PFA), which can be employed as a guideline in parameter selection and performance evaluation. Simulation results using both modeled and real tsunami data demonstrate that, given a PFA of 10%, the PD can be around 60% when the wave height is 45 cm, and the SSH measurement error standard deviation (STD) is 76 cm. The results also show that, when a suitable bin length is selected, a two-stage hypothesis testing scheme and a one stage one produce very similar results. Based on the methods developed here and elsewhere, the GNSS-R technique has the potential for future tsunami detection.

Raw Global Navigation Satellites Systems (GNSS) data have been directly accessible from mass-market devices running the Android Nougat (or newer) operating system since late 2016. The availability of GNSS raw data made possible to investigate feasibility of using in-built GNSS chipsets within smartphone devices as passive radar receivers for the purpose of land remote sensing. In this study, we integrate smart-phones into small Unmanned Aircraft Systems (UAS) to collect reflected GNSS raw data for the purpose of mapping top 5-cm soil moisture. The reflected GNSS signals collected by the smartphones show high correlation with spatial features on the ground such as ponds, crops, and small creeks. To determine the quality of smartphone in-built antenna and chipset, we conducted several experiments. The results show that (1) the radiation pattern of smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and (2) internal GNSS chip produces observables of sufficient quality when the GNSS smartphone reflected signals are compared with a high quality custom-built dual channel receiver. This paper summarizes the experimental findings and challenges that need to be resolved in order to use the GNSS-Reflectometry (GNSS-R) technique via ubiquitous smartphones from small UASs.

In the last decades, Global navigation satellite systems (GNSS) have provided an exceptional opportunity to retrieve atmospheric parameters globally through GNSS radio occultation (GNSS-RO). In this paper, data of 12 GNSS-RO missions from June 2001 to November 2020 with high resolution were used to investigate the possible widening of the tropical belt along with the probable drivers and impacts in both hemispheres. Applying both lapse rate tropopause (LRT) and cold point tropopause (CPT) definitions, the global tropopause height shows increase of approximately 36 m/decade and 60 m/decade, respectively. Moreover, the tropical edge latitude (TEL) estimated based on two tropopause height metrics, in the northern hemisphere (NH) and southern hemisphere (SH), are different from each other. For the first metric, subjective method, the tropical width from GNSS has expansion behavior in NH with ~ 0.41°/decade and a minor expansion in SH with ~ 0.08°/decade. In case of ECMWF Reanalysis v5 (ERA5) there is no significant contraction in both NH and SH. For Atmospheric Infrared Sounder (AIRS), there are expansion behavior in NH with ~ 0.34°/decade and strong contraction in SH with ~ −0.48°/decade. Using the second metric, objective method, the tropical width from GNSS has expansion in NH with ~ 0.13°/decade, and no significant expansion in SH. In case of ERA5, there is no significant signal in NH while SH has a minor contraction. AIRS has an expansion with ~ 0.13°/decade in NH, and strong contraction in SH with ~ −0.37°/decade. The variability of tropopause parameters (temperature and height) is maximum around the TEL locations at both hemispheres. The total column ozone (TCO) shows increasing rates globally, and the rate of increase at the SH is higher than that of the NH. There is a good agreement between the spatial and temporal patterns of TCO variability and the TEL location estimated from GNSS LRT height. Carbon dioxide (CO2), and Methane (CH4), the most important greenhouse gases (GHGs) and the main drivers of global warming, have a global increasing rate and the increasing rate of the NH is similar to that of the SH. The spatial pattern in the NH is located more pole ward than its equivalent at the SH. Both surface temperature and precipitation increase in time and have strong correlation with GNSS LRT height. Both show higher increasing rates at the NH, while the precipitation at the SH has slight decrease and the surface temperature increases. The surface temperature shows a spatial pattern with strong variability, which broadly agrees with the TEL locations. The spatial pattern of precipitation shows northward occurrence. In addition, Standardized Precipitation Evapotranspiration Index (SPEI) has no direct connection with the TEL behavior.

Snow storage dynamics is essential to predict floods, to quantify water resources for human use and irrigation, and to assess the risk of avalanches. Recently, Global Navigation Satellite System (GNSS) ground stations have been successfully used to continuously estimate snow depth at an intermediate scale of about 1.000 $\text{m}^2$ around the stations. In this study, GNSS signal-to-noise ratio (SNR) data at the station Wettzell, Germany, are used to estimate snow depth variations from 2012 to 2015. The station Wettzell is located in a built-up area. The most challenging task at this site is to separate the GNSS reflections from the ground and from surrounding buildings. We modified the interference approach previously used for snow depth estimation using the phase of the multipath interference pattern instead of their frequency. Additionally, we complemented the analysis by including satellites transmitting the L2P signal into the processing. We studied the performance of the L1 signal. The derived GNSS snow depth ranges between 3 and 25 cm and corresponds well to in situ observations by an ultrasonic sensor, with a correlation of 0.8 for daily time series. The residuals of GNSS snow depths compared to the ultrasonic sensor reveal a root-mean-squared error (RMSE) of 4.3 cm for the L2 and 5.9 cm for the L1 signal with a small bias of 1 cm. The results show that the existing data of the global GNSS tracking network promise to provide valuable complementary snow depth observations to the existing sensors at several hundred sites worldwide, including urban areas.

This study performs the first multiclass sea ice classification using global navigation satellite system (GNSS) reflectometry (GNSS-R) data from the Fengyun-3E (FY-3E) satellite. A principal component analysis (PCA) based denoising scheme for GNSS-R delay Doppler maps (DDMs) is proposed. The newly proposed processing methods are applied to FY-3E satellite tracks to retain the most significant features of the data. After removing noise from the DDMs using PCA, DDM classification features are calculated using methods established in the literature. An extremely randomized trees (ET) machine learning (ML) classifier was used to classify the sea ice using the denoised classification observables. The classification results are validated using labels derived from National Snow and Ice Data Center (NSIDC) sea ice concentration data. The output classification accuracy was 87.14% with a 0.7834 Kappa coefficient. Per class F1 scores were 89.72%, 85.53%, and 81.34% for first-year ice (FYI), multi-year ice (MYI), and thin ice (TI), respectively. The proposed method produced improvements across all performance metrics compared to classification without PCA based observables and denoising.

Global navigation satellite systems (GNSSs) are widely utilized in various scientific domains such as agriculture, health monitoring, weather forecasting, and crop health detection. GNSS is a satellite network utilized in positioning and navigation including location-based services and precise orbit determination. The satellite navigation system offers various advantages such as real-time location and temporal synchronization. The Global Positioning System (GPS) is one of the well-known GNSSs, operated by the United States. Some of the other examples may include GPS; Galileo; Russia’s GLONASS, known as the Global Navigation Satellite System; and the BeiDou Navigation Satellite System (BDS). GNSS has found many applications in the field of precision agriculture, timing and synchronization in telecommunication, search and rescue operations, military and defense purposes, Earth observation, construction, and weather forecasting. However, GNSS has also been explored in the accurate and reliable positioning and timing information for climate change analysis and modeling. Therefore, it is essential to examine the potential of GNSS in climate monitoring and modeling. This chapter will present a review of GNSS types, GNSS remote sensing applications, and challenges faced by GNSS. Additionally, this chapter also explores the meta-analysis of the publications or research contributions made by different authors in the field of GNSSs.

The neutral atmosphere (or troposphere) causes refraction in radio frequency signals, which results in errors in Global Navigation Satellite Systems (GNSS) measurements. In meteorology, this effect can represent important measurements of the concentration of atmospheric constituents, especially in regions where conventional high-altitude atmospheric sounding (radiosondes) cannot be performed. There are two GNSS techniques used for this. In the first one, GNSS receivers are located on terrestrial stations that provide estimates of the vertically integrated moisture content (Precipitable Water Vapor - PWV). In the second case, receivers are in space platforms, which obtains profiles of atmospheric pressure, temperature and humidity, known as GNSS radio occultation. These measurements have significant potential for nowcasting applications (30 minutes in advance) of extreme precipitation events (>35 mm). This paper presents a review of the state of the art in the synergy between Geodesy and Meteorology for modeling the neutral atmosphere (neutrosphere), its effect on GNSS positioning and in the estimation of atmospheric constituents, and their applications. Furthermore, it offers the improvements and new challenges developed in modeling the delay for high accuracy positioning.

Radio-Frequency Interference (RFI) is a growing problem for applications based on the Global Navigation Satellite Systems (GNSS), including Earth observation using GNSS — Reflectometry (GNSS-R). Nowadays, many efforts are being concentrated to develop RFI signal detectors with high sensitivity, which can help to control the proliferation of RFI generators or jammers. This work aims at designing and testing an instrument capable of detecting and localizing RFI signals at GNSS bands using a microwave radiometer architecture, and a combination of statistical and time-frequency digital detection techniques. The instrument has been tested using different kinds of RFI signals, and its performance has been evaluated as a function of the RFI power. A sensitivity of −135 dBm for continuous wave and chirp signals is reported.