Analysis of Gnss-R Observations for Altimetry and Characterization of Earth Surfaces

Global Navigation Satellite Systems (GNSS) provide abundant, opportunistic signals that can be used to probe the Earth’s environment and surface. Utilizing reflected GNSS signals for remote sensing is called GNSS Reflectometry (GNSS-R). Sensing of the ocean, land, and ice, with potentially dense measurement coverage and rapid revisit times, is possible due to the distributed geometry of GNSS constellations. GNSS-R can provide some advantages over other Earth observation systems, like traditional radar altimeters or microwave radiometers. GNSS signals are well characterized and encoded with precise ranging and timing information. There are multiple transmitters in view at any time, and GNSS signals occupy a protected frequency band (L-band) that penetrates Earth’s atmosphere in all weather conditions.This dissertation focuses on the development of methods and analysis techniques to observe sea surface height and sea ice extent with reflected GNSS signals. A tool-kit is developed to take advantage of experimental data sets from aircraft and spacecraft, and to produce state-of-the-art altimetric retrievals. Algorithms for the re-tracking of altimetric delays are demonstrated. Techniques to characterize and models to correct GNSS-R path delay errors are built through analysis of TechDemoSat-1 (TDS-1) and NASA’s Cyclone Global Navigation Satellite System (CYGNSS) flight data. Neither TDS-1 nor CYGNSS were designed to make precise altimetry observations. Thus, this work evaluates practical performance limitations of these GNSS-R observations, and establishes requirements for future missions. Altimetry results with height retrieval standard deviation of σH = 11 m with 1 sec and σH = 3.8 m with 10 sec observations, are shown.This work creates a foundation of techniques that can support future GNSS-R missions dedicated to ocean surface altimetry by producing results with sufficient accuracy and precision to the ocean science community. These tools are built to inform future mission designs and aid scientific interpretation of GNSS-R measurements

Improved GNSS-R bi-static altimetry and independent digital elevation models of Greenland and Antarctica from TechDemoSat-1

Improved digital elevation models (DEMs) of the Antarctic and Greenland ice sheets are presented, which have been derived from Global Navigation Satellite Systems-Reflectometry (GNSS-R). This builds on a previous study (Cartwright et al., 2018) using GNSS-R to derive an Antarctic DEM but uses improved processing and an additional 13 months of measurements, totalling 46 months of data from the UK TechDemoSat-1 satellite. A median bias of under 10 m and root-mean-square errors (RMSEs) of under 53 m for the Antarctic and 166 m for Greenland are obtained, as compared to existing DEMs. The results represent, compared to the earlier study, a halving of the median bias to 9 m, an improvement in coverage of 18 %, and a 4 times higher spatial resolution (now gridded at 25 km). In addition, these are the first published satellite altimetry measurements of the region surrounding the South Pole. Comparisons south of 88∘ S yield RMSEs of less than 33 m when compared to NASA's Operation IceBridge measurements. Differences between DEMs are explored, the limitations of the technique are noted, and the future potential of GNSS-R for glacial ice studies is discussed

Improved GNSS-R bi-static altimetry and independent DEMs of Greenland and Antarctica from TechDemoSat-1

Improved Digital Elevation Models (DEMs) of the Antarctic and Greenland Ice Sheets are presented, derived from Global Navigation Satellite Systems-Reflectometry (GNSS-R). This builds on a previous study (Cartwright et al., 2018) using GNSS-R to derive an Antarctic DEM but uses improved processing and an additional 13 months of measurements, totalling 46 months of data from the UK TechDemoSat-1 satellite. A median bias of under 10 m and root-mean-square (RMS) errors of under 53 m for the Antarctic and 166 m for Greenland are obtained, as compared to existing DEMs. The results represent, compared to the earlier study, a halving of the median bias to 9 m, an improvement in coverage of 18 %, and a four times higher spatial resolution (now gridded at 25 km). In addition, these are the first published satellite altimetry measurements of the region surrounding the South Pole. Comparisons south of 88° S yield RMS errors of less than 33 m when compared to NASA’s Operation IceBridge measurements. Differences between DEMs are explored and the future potential for ice sheet monitoring by this technique is noted

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

Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry

Ocean altimetry with Global Navigation Satellite Systems signals (GNSS) signals is a remote sensing technique that measures the height of the sea surface through the difference in path length of the direct and reflected signal. Code altimetry estimates this parameter by tracking the code delay after performing correlations with a GNSS signal replica. It is of limited precision due to the low signal-to-noise ratio (SNR) and narrow bandwidth of the ocean-reflected GNSS signal. However, the potential advantages of the GNSS-R systems such as high temporal resolution and spatial coverage are a motivation to improve its altimetric precision. In this article, we present a performance assessment of the Likelihood Map Waveform tracking technique, a method based on Maximum Likelihood Estimation theory that exploits the available reflected power in a more efficient way than the single tracking point methods. We use a modification of the theoretical optimal solution that achieves a better performance than previous methods. We estimate it, in terms of SNR gain, using Monte Carlo method with a detailed stochastic model of the signal, and with actual signals from the Cyclone Global Navigation Satellite System. The gain values obtained were between 1.64 and 3.66 dB in the theoretical analysis, and between 1.69 and 2.62 dB with the real data, confirming the potential of the proposed approach.Facultad de IngenieríaInstituto de Investigaciones en Electrónica, Control y Procesamiento de Señale

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

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

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

A generic level 1 simulator for spaceborne GNSS-R missions and application to GEROS-ISS ocean reflectometry

©2017 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.In the past decade Global Navigation Satellites System Reflectometry (GNSS-R) has emerged as a new technique for earth remote sensing for various applications, such as ocean altimetry and sea state monitoring. After the success of the GNSS-R demonstrator payloads aboard the UK-DMC or TDS-1 satellites; at present, there are several missions planned to carry GNSS reflectometers. The GNSS rEflectometry, Radio Occultation, and Scatterometry onboard International Space Station (GEROS-ISS) is an innovative ISS experiment exploiting GNSS-R technique to measure key parameters of ocean, land, and ice surfaces. For GEROS-ISS mission, the European Space Agency (ESA) supported the study of GNSS-R assessment of requirements and consolidation of retrieval algorithms (GARCA). For this, it was required to accurately simulate the GEROS-ISS measurements including the whole range of parameters affecting the observation conditions and the instrument, which is called GEROS-SIM. To meet these requirements, the PAU/PARIS end-to-end performance simulator (P$^{2}$EPS) previously developed by UPC BarcelonaTech was used as the baseline building blocks for the level 1 (L1) processor of GEROS-SIM. P$^{2}$EPS is a flexible tool, and is capable of systematically simulating the GNSS-R observations for spaceborne GNSS-R missions. Thanks to the completeness and flexibility, the instrument-to-L1 data module of GEROS-SIM could be implemented by proper modification and update of P$^{2}$EPS. The developed GEROS-SIM was verified and validated in the GARCA study as comparing to the TDS-1 measurements. This paper presents the design, implementation, and results of the GEROS-SIM L1 module in a generic way to be applied to GNSS-R instruments.Peer ReviewedPostprint (author's final draft

Soil moisture estimation synergy using GNSS-R and L-Band microwave radiometry data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work was (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work was also (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Joan Francesc Munoz-Martin received support from the grant for the recruitment of early-stage research staff FI-DGR 2018 of the AGAUR - Generalitat de Catalunya (FEDER), Spain; Christoph Herbert received the support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050 and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673; David Llavería received support from an FPU fellowship from the Spanish Ministry of Education FPU18/06107.Peer ReviewedPostprint (published version