GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

SNR degradation in GNSS-R measurements under the effects of radio-frequency interference

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Radio-frequency interference (RFI) is a serious threat for systems working with low power signals such as those coming from the global navigation satellite systems (GNSS). The spectral separation coefficient (SSC) is the standard figure of merit to evaluate the signal-to-noise ratio (SNR) degradation due to the RFI. However, an in-depth assessment in the field of GNSS-Reflectometry (GNSS-R) has not been performed yet, and particularly, about which is the influence of the RFI on the so-called delay-Doppler map (DDM). This paper develops a model that evaluates the contribution of intra-/inter-GNSS and external RFI effects to the degradation of the SNR in the DDM for both conventional and interferometric GNSS-R techniques. Moreover, a generalized SSC is defined to account for the effects of nonstationary RFI signals. The results show that highly directive antennas are necessary to avoid interference from other GNSS satellites, whereas mitigation techniques are essential to keep GNSS-R instruments working under external RFI degradation.Peer ReviewedPostprint (author's final draft

Estimativa da umidade do solo por refletometria GNSS : uma revisão conceitual

Soil moisture monitoring enables efficient management and use of water resources, having great importance for several purposes, such as: monitoring of risk areas; delimitation of areas susceptible to flooding; geotechnical activities; and in agriculture development. GNSS Reflectometry (GNSS-R) is a scientific and technological development that allows one to perform proximal or remote sensing, depending on the antenna height concerning the surface, by means of navigation satellites. This method exploits GNSS signals indirectly reaching a receiver antenna after they are reflected on the surrounding surfaces. In this method, direct and indirect GNSS signals that reach the receiving antenna are exploited, after reflection on the surfaces existing around the antenna. The combination of these two signals causes the multipath effect, which affects GNSS observable and deteriorates positioning. On the other hand, when interacting with these reflecting surfaces one can estimate their properties. One of the main advantages of GNSS-R, when compared with the conventional methods, is the intermediate coverage area, as well as, the use of the well-defined structure of GNSS systems that guarantee appropriate temporal resolution. The scope of this paper is to present a conceptual review of GNSS-R applied to soil moisture monitoring.O monitoramento da umidade do solo possibilita o manejo e uso eficiente de recursos hídricos, sendo uma atividade importante em diversas áreas, tais como: no monitoramento de áreas de risco; delimitação de áreas suscetíveis a enchentes; atividades da geotecnia; e na agricultura. A Refletometria GNSS (GNSS-R) é um desenvolvimento científico e tecnológico que permite realizar sensoriamento remoto ou proximal, a depender da altura da antena em relação à superfície, com satélites de navegação. Neste método, explora-se os sinais GNSS que chegam à antena receptora de maneira direta e indireta, após reflexão nas superfícies existentes no entorno da antena. A combinação destes dois sinais ocasiona o efeito de multicaminho, que afeta as observáveis GNSS e deteriora o posicionamento. Por outro lado, ao interagir com estas superfícies, o sinal indireto permite estimar atributos acerca destas superfícies, como por exemplo a umidade do solo. Uma das principais vantagens em relação aos métodos convencionais reside no fato do GNSS-R proporcionar uma área de abrangência intermediária e o uso da estrutura bem estabelecida dos satélites GNSS, que garantem resolução temporal apropriada. O escopo deste trabalho é apresentar uma revisão conceitual acerca do GNSS-R aplicado no monitoramento da umidade do solo

Quantifying the Uncertainty in Ground-Based GNSS-Reflectometry Sea Level Measurements

Global Navigation Satellite System reflectometry (GNSS-R) tide gauges are a promising alternative to traditional tide gauges. However, the precision of GNSS-R sea-level measurements when compared to measurements from a colocated tide gauge is highly variable, with no clear indication of what causes the variability. Here, we present a modeling technique to estimate the precision of GNSS-R sea-level measurements that relies on creating and analyzing synthetic signal-to-noise-ratio (SNR) data. The modeled value obtained from the synthetic SNR data is compared to observed root mean square error between GNSS-R measurements and a colocated tide gauge at five sites and using two retrieval methods: spectral analysis and inverse modeling. We find that the inverse method is more precise than the spectral analysis method by up to 60 for individual measurements but the two methods perform similarly for daily and monthly means. We quantify the contribution of dominant effects to the variations in precision and find that noise is the dominant source of uncertainty for spectral analysis whereas the effect of the dynamic sea surface is the dominant source of uncertainty for the inverse method. Additionally, we test the sensitivity of sea-level measurements to the choice of elevation angle interval and find that the spectral analysis method is more sensitive to the choice of elevation angle interval than the inverse method due to the effect of noise, which is greater at larger elevation angle intervals. Conversely, the effect of tropospheric delay increases for lower elevation angle intervals but is generally a minor contribution

On the Response of Polarimetric GNSS-Reflectometry to Sea Surface Roughness

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

GNSS transpolar earth reflectometry exploriNg system (G-TERN): Mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a "dynamic mapper" of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (&lt;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance

Feasibility of GNSS-R ice sheet altimetry in Greenland using TDS-1

Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of the measurements. Here we perform a study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission. TDS-1 was primarily designed to provide sea state information such as sea surface roughness or wind, but not altimetric products. The data have been analyzed with altimetric methodologies, already tested in aircraft based experiments, to extract signal delay observables to be used to infer properties of the Greenland ice sheet cover. The penetration depth of the GNSS signals into ice has also been considered. The large scale topographic signal obtained is consistent with the one obtained with ICEsat GLAS sensor, with differences likely to be related to L-band signal penetration into the ice and the along-track variations in structure and morphology of the firn and ice volumes The main conclusion derived from this work is that GNSS-R also provides potentially valuable measurements of the ice sheet cover. Thus, this methodology has the potential to complement our understanding of the ice firn and its evolution.Peer ReviewedPostprint (published version

First spaceborne observation of sea surface height using GPS-reflectometry

An analysis of spaceborne Global Positioning System reflectometry (GPS-R) data from the TechDemoSat-1 (TDS-1) satellite is carried out to image the ocean sea surface height (SSH). An SSH estimation algorithm is applied to GPS-R delay waveforms over two regions in the South Atlantic and the North Pacific. Estimates made from TDS-1 overpasses during a 6 month period are aggregated to produce SSH maps of the two regions. The maps generally agree with the global DTU10 mean sea surface height. The GPS-R instrument is designed to make bistatic measurements of radar cross section for ocean wind observations, and its altimetric performance is not optimized. The differences observed between measured and DTU10 SSH can be attributed to limitations with the GPS-R instrument and the lack of precision orbit determination by the TDS-1 platform. These results represent the first observations of SSH by a spaceborne GPS-R instrument

GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition

The Global Navigation Satellite System-Reflections (GNSS-R) signal has been confirmed to be used to retrieve sea level height. At present, the GNSS-Interferometric Reflectometry (GNSS-IR) technology based on the least square method to process signal-to-noise ratio (SNR) data is restricted by the satellite elevation angle in terms of accuracy and stability. This paper proposes a new GNSS-IR model combining variational mode decomposition (VMD) for sea level height estimation. VMD is used to decompose the SNR data into intrinsic mode functions (IMF) of layers with different frequencies, remove the IMF representing the trend item of the SNR data, and reconstruct the remaining IMF components to obtain the SNR oscillation item. In order to verify the validity of the new GNSS-IR model, the measurement data provided by the Onsala Space Observatory in Sweden is used to evaluate the performance of the algorithm and its stability in high elevation range. The experimental results show that the VMD method has good results in terms of accuracy and stability, and has advantages compared to other methods. For the half-year GNSS SNR data, the root mean square error (RMSE) and correlation coefficient of the new model based on the VMD method are 4.86 cm and 0.97, respectively