Detecting Targets above the Earth's Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1

: Global Navigation Satellite System (GNSS) reflected signals can be used to remotely sense the Earth’s surface, known as GNSS reflectometry (GNSS-R). The GNSS-R technique has been applied to numerous areas, such as the retrieval of wind speed, and the detection of Earth surface objects. This work proposes a new application of GNSS-R, namely to detect objects above the Earth’s surface, such as low Earth orbit (LEO) satellites. To discuss its feasibility, 14 delay Doppler maps (DDMs) are first presented which contain unusually bright reflected signals as delays shorter than the specular reflection point over the Earth’s surface. Then, seven possible causes of these anomalies are analysed, reaching the conclusion that the anomalies are likely due to the signals being reflected from objects above the Earth’s surface. Next, the positions of the objects are calculated using the delay and Doppler information, and an appropriate geometry assumption. After that, suspect satellite objects are searched in the satellite database from Union of Concerned Scientists (UCS). Finally, three objects have been found to match the delay and Doppler conditions. In the absence of other reasons for these anomalies, GNSS-R could potentially be used to detect some objects above the Earth’s surface.Peer ReviewedPostprint (published version

Feasibility of Oil Slick Detection Using BeiDou-R Coastal Simulation

Oil spills, which can cause severe immediate and long-term harm to marine ecological environments for decades after the initial accident, require rapid and accurate monitoring. Currently, optical and radar satellite images are used to monitor oil spills; however, remote sensing generally needs a long revisit period. Global Navigation Satellite System reflected signals (GNSS-R) can provide all-weather and all-day ocean monitoring and is therefore more suitable for oil spill monitoring. To assess the feasibility of the BeiDou Navigation Satellite System reflected signals (BeiDou-R) in detecting oil slicks, a BeiDou-R coastal simulated experiment is performed in this study on the oil slick distribution of an oil pipeline explosion accident. We set up an observation point and selected observation satellites, and a delay-Doppler map (DDM) of an oil-slicked sea surface under coastal scenarios was created by combining the mean-square slope (MSS) model for oil-slicked/clean surfaces and the Zavorotny–Voronovich (Z–V) scattering model. DDM simulation of the coastal scenarios effectively represents the scattering coefficient distribution of the presence of an oil slick. Theoretical analysis revealed that oil slicks can be detected within a radius of less than 5 km around the specular reflection point (SP) for BeiDou-R coastal simulation

Sensitivity of delay Doppler map in spaceborne GNSS-R to geophysical variables of the ocean

Global Navigation Satellite Systems reflectometry (GNSS-R) is a particular case of a multistatic radar in which the signals transmitted by navigation satellites are the signals of opportunity. These signals can be processed as a radar scatterometer, as a radar altimeter, or as an unfocused synthetic aperture radar. GNSS-R has shown its potential to infer numerous geophysical variables: over land soil moisture, vegetation height, detection of freeze-thaw state, etc., map sea ice extent and type…, and over the ocean wind speed and direction, significant wave height, altimetric measurements or even more recently NASA has released a marine plastics litter product, and some also claim that sea surface salinity (SSS) can be inferred. In addition, retrieval algorithms neglect some of the variations of the delay Doppler map (DDM) that are linked to the observation geometry, i.e., look angle with respect to the speed vectors of the transmitter and receiver. All these different effects impact the DDM peak value and its shape, and may affect the retrieval of geophysical parameters, and ultimately the data interpretation. In this study, the following factors impacting the DDM peak value are studied: the observation geometry, the sea surface temperature, and SSS, the 10 m height wind speed (U 10 ) and direction (WD), the presence of foam, the sea development state, the presence of swell, currents, rain, and the presence of oil slicks perturbing the sea surface roughness. This illustrates the complexity of the challenges presented when trying to retrieve some of these variables, the required corrections, and their accuracy.This work was supported in part by the Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023 (Spain) under Grant PID2021-126436OB-C21, in part by the European Social Fund, and in part by the GENESIS: GNSS Environmental and Societal Missions – Subproject UPC under Grant PID2021-126436OB-C21.Peer ReviewedPostprint (published version

Sea surface oil slick detection and wind field measurement using global navigation satellite system reflectometry

In this thesis, research for improving sea surface remote sensing using the Global Navigation Satellite System-Reflectometry (GNSS-R) signals is presented. Firstly, a method to enable the simulation of GNSS-R delay Doppler Map (DDM) of an oil slicked sea surfaces under general scenarios is proposed. The DDM of oil slicked sea surface under general scenarios is generated by combining the mean-square slope model for oil slicked/clean surfaces and the GNSS-R Zavorotny-Voronovich (Z-V) scattering model. The coordinate system transformation appropriate for general- elevation-angle scenarios is also incorporated. Secondly, a technique to detect sea surface oil spills using reflections from Global Navigation Satellite System (GNSS) satellites is presented. This technique is implemented by compensating the distor- tion induced during the DDM deconvolution process of scattering coefficient retrieval and employing the spatial integration approach (SIA) to retrieve the scattering co- efficients unambiguously using the DDMs obtained by two separate antenna beams. A performance characterization including retrieval accuracy and resolution is demon- strated with respect to the signal-to-noise ratio and the size of oil slicks, respectively. Simulation based on the oil slick distribution of the Deepwater Horizon oil spill ac- cident shows that the retrieval error can be reduced by the SIA after the distortion correction. The technique proposed here can be used to map oil slick extent on the ocean surface or it may be applied generically to produce physical surface maps of the bistatic scattering coefficient from multiple DDM’s from a single space-based platform. Lastly, a novel method is presented to retrieve sea surface wind speed and direction by fitting the two-dimensional simulated GNSS-R DDMs to measured data. An 18- second incoherent correlation is performed on the measured signal to reduce the noise level. Meanwhile, a variable step-size iteration as well as a fitting threshold are used to reduce the computational cost and error rate of the fitting procedure, respectively. Unlike previous methods, all the DDM points with normalized power higher than the threshold are used in the least-square fitting. An optimal fitting threshold is also proposed. To validate the proposed method, the retrieval results based on a dataset from the United Kingdom Disaster Monitoring Constellation satellite are compared with the in-situ measurements provided by the National Data Buoy Center, and good correlation is observed between the two

Temporal variability of GNSS-Reflectometry ocean wind speed retrieval performance during the UK TechDemoSat-1 mission

This paper presents the temporal evolution of Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval performance during three years of the UK TechDemoSat-1 (TDS-1) mission. TDS-1 was launched in July 2014 and provides globally distributed spaceborne GNSS-R data over a lifespan of over three years, including several months of 24/7 operations. TDS-1 wind speeds are computed using the NOC Calibrated Bistatic Radar Equation algorithm version 0.5 (C-BRE v0.5), and are evaluated against ERA5 high resolution re-analysis data over the period 2015–2018. Analyses reveal significant temporal variability in TDS-1 monthly wind speed retrieval performance over the three years, with the best performance (~2 m∙s−1) achieved in the early part of the mission (May 2015). The temporal variability of retrieval performance is found to be driven by several non-geophysical factors, including TDS-1 platform attitude uncertainty and spatial/temporal changes in GPS transmit power from certain satellites. Evidence is presented of the impact of the GPS Block IIF Flex mode on retrieved GNSS-R wind speed after January 2017, which results in significantly underestimated ocean winds over a large region covering the North Atlantic, northern Indian Ocean, the Mediterranean, the Black Sea, and the Sea of Okhotsk. These GPS transmit power changes are shown to induce large negative wind speed biases of up to 3 m∙s−1. Analyses are also presented of the sensitivity of TDS-1 wind speed retrieval to platform attitude uncertainty using statistical simulations. It is suggested that a 4° increase in attitude uncertainty can produce up to 1 m∙s−1 increase in RMSE, and that TDS-1 attitude data do not fully reflect actual platform attitude. We conclude that the lack of knowledge about the GNSS-R nadir antenna gain map and TDS-1 platform-attitude limits the ability to determine the achievable wind speed retrieval performance with GNSS-R on TDS-1. The paper provides recommendations that accurate attitude knowledge and a good characterisation of GNSS-R nadir antenna patterns should be prioritised for future GNSS-R missions

Microwave satellite remote sensing for a sustainable sea

The oceans cover roughly 2/3 of the Earth’s surface and are a fundamental ecosystem regulating climate, weather and representing a huge reservoir of biodiversity and natural resources. The preservation of the oceans is therefore not only relevant on an environmental perspective but also on an economical one. A sustainable approach is requested that cannot be simply achieved by improving technologies but calls for a shared new vision of common goods.Within such a complex and holistic problem, the role of satellite microwave remote sensing to observe marine ecosystem and to assist a sustainable development of human activities must be considered. In such a view the paper is meant. Accordingly, the key microwave sensor technologies are reviewed paying particular emphasis on those applications that can provide effective support to pursue some of the UN Sustainable Development Goals. Three meaningful sectors are showcased:oil and gas, where microwave sensors can provide continuous fine-resolution monitoring of critical infrastructures; renewable energy, where microwave satellite remote sensing allows supporting the management of offshore wind farms during both feasibility and operational stages; plastic pollution, where microwave technologies that exploit signals of opportunity offer large-scale monitoring capability to provide marine litter maps of the oceans

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

Sea target detection using spaceborne GNSS-R delay-doppler maps: theory and experimental proof of concept using TDS-1 data

© 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.This study addresses a novel application of global navigation satellite system-reflectometry (GNSS-R) delay-Doppler maps (DDMs), namely sea target detection. In contrast with other competing remote sensing technologies, such as synthetic aperture radar and optical systems, typically exploited in the field of sea target detection, GNSS-R systems could be employed as satellite constellations, so as to fulfill the temporal requirements for near real-time ships and sea ice sheets monitoring. In this study, the revisit time offered by GNSS-R systems is quantitatively evaluated by means of a simulation analysis, in which three different realistic GNSS-R missions are simulated and analyzed. Then, a sea target detection algorithm from spaceborne GNSS-R DDMs is described and assessed. The algorithm is based on a sea clutter compensation step and uses an adaptive threshold to take into account spatial variations in the sea background and/or noise statistics. Finally, the sea target detector algorithm is tested and validated for the first time ever using experimental GNSS-R data from the U.K. TechDemoSat-1 dataset. Performance is assessed by providing the receiver operating characteristic curves, and some preliminary experimental results are presented.Peer ReviewedPostprint (published version

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Detección automática de la zona ciega de un SLAR

Este artículo presenta una metodología para la detección y medida de la zona ciega de un sensor embarcado en aeronave del tipo SLAR. La zona ciega de un SLAR está formada por la región donde hay ausencia de medida o los datos están enmascarados con errores significativos. El objetivo que se busca es detectar esta región, delimitarla y etiquetarla, para reducir las regiones de búsqueda a ROIs específicos, y así, simplificar los procesamientos necesarios para detecciones de otros objetivos, como manchas de hidrocarburos o embarcaciones.Este trabajo ha sido financiado por el proyecto (RTC-2014-1863-8) “ONTIME: Operación remota de Transmisión de Información en Misiones de Emergencia” de la Convocatoria Retos de Colaboración del MINECO