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

TriHex: combining formation flying, general circular orbits and alias-free imaging, for high resolution L-band aperture synthesis

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA’s Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging.Peer ReviewedPostprint (author's final draft

Optimisation de la reconstruction d'image pour SMOS et SMOS-NEXT

Dans le cadre général de l'étude du climat, du cycle de l'eau et de la gestion des ressources en eau, le satellite SMOS (Soil Moisture and Ocean Salinity) a été lancé par l'agence spatiale européenne (ESA) en Novembre 2009 pour fournir des cartes globales d'humidité des sols et de salinité des surfaces océaniques. Les mesures du satellite sont obtenues par un radiomètre interférométrique opérant dans la bande passive 1400-1427 MHz (bande L des micro-ondes). Toutefois, dès les premières mesures de l'instrument, de nombreuses Interférences en Radio Fréquence (RFI) ont été observées, malgré les recommandations de l'Union Internationale des Télécommunications (ITU) qui protègent cette bande pour les applications scientifiques. La dégradation de données à cause des interférences est significative et au niveau international des efforts sont fait par l'ESA et les différentes agences nationales pour l'identification et l'extinction de ces émetteurs. D'un point de vue scientifique l'intérêt porte sur le développement de techniques pour la détection, la localisation au sol des sources d'interférences ainsi que pour la correction de leurs signaux dans les donnés SMOS ; différents objectifs ont donc été poursuivis et ont mené à la définition de différents approches présentées dans cette contribution. En effet la solution idéale serait de corriger l'impact de ces interférences sur les données, en créant synthétiquement des signaux égaux et de signe opposé et d'en tenir compte dans la chaîne de traitement des données. Un outil a donc été développé qui, en utilisant des connaissances a priori sur la scène observée issues des modèles météorologiques, permet de simuler la scène vue par l'instrument. A partir de cette information et des visibilités entre les antennes de l'interféromètre, il est possible de détecter et de décrire précisément ces interférences et donc d'en déduire le signal à soustraire. Bien que l'évaluation des performances d'un algorithme de correction des RFI pour SMOS ne soit pas facile puisqu'elle doit être faite indirectement, des méthodes avec ce but sont proposées et montrent des résultats généralement positifs pour l'algorithme développé. Cependant la difficulté d'évaluer l'impact de la correction à grande échelle, ainsi que pour l'incertitude qui est nécessairement introduite lors de l'application d'un signal synthétique aux données et afin d'éviter une utilisation naïve des résultats de correction, aujourd'hui on écarte l'hypothèse d'une application opérationnelle d'un algorithme de correction. Un produit intermédiaire a alors été développé, par une approche similaire, avec l'objectif de fournir des indications sur l'impact des RFI sur chaque point de chaque image selon des seuils prédéfinis. Un autre objectif a été de fournir un outil en mesure de caractériser rapidement les sources (position au sol, puissance, position dans le champ de vue) pour une zone géographique. Cette méthode utilise les composantes de Fourier de la scène vue par l'instrument pour obtenir une distribution de températures de brillance, dans laquelle les RFI apparaissent comme des points chauds. L'algorithme rapide de caractérisation des sources s'est révélé précis, fiable et robuste, et il pourrait être utilisé pour la définition de bases de données sur les RFI ou pour le suivi de celles-ci à l'échelle locale ou globale. Les résultats de cette méthode ont fournit un jeu de données privilégié pour l'étude des performances de l'instrument et ça a permit de mettre en évidence des potentielles erreurs systématiques ainsi que des variations saisonnières des résultats. Toutes mission spatiale ayant une vie limitée à quelques années, dans un deuxième temps on s'est intéressé à la continuité des mesures des mêmes variables géophysiques, avec le projet de mission SMOS-NEXT. Pour améliorer la qualité des mesures cette mission se propose d'implémenter une technique d'interférométrie novatrice : la synthèse d'ouverture spatio-temporelle, dont le principe est de corréler les mesures entre antennes en positions différentes et à des instants différents, dans les limites de cohérence liées à la bande spectrale. Suite à des études théoriques, une expérience a été faite en utilisant le radiotélescope de Nançay. Dans le cadre de la thèse les données de cette expérience ont été analysées. Bien que l'étude n'ait pas permit de conclure sur la validité du principe, plusieurs difficultés ont été mises en évidence et ce retour d'expérience sera utile lors de la prochaine campagne de mesure prévue.The Soil Moisture and Ocean Salinity (SMOS) satellite was launched by the European Space Agency (ESA) in November 2009 to allow a better understanding of Earth's climate, the water cycle and the availability of water resources at the global scale, by providing global maps of soil moisture and ocean salinity. SMOS' payload, an interferometric radiometer, measures Earth's natural radiation in the protected 1400-1427 MHz band (microwave, L-band). However, since launch the presence of numerous Radio-Frequency Interferences (RFI) has been clearly observed, despite the International Telecommunication Union (ITU) recommendations to preserve this band for scientific use. The pollution created by these artificial signals leads to a significant loss of data and a common effort of ESA and the national authorities is necessary in order to identify and switch off the emitters. From a scientific point of view we focus on the development of algorithms for the detection of RFI, their localization on the ground and the mitigation of the signals they introduce to the SMOS data. These objectives have led to different approaches that are proposed in this contribution. The ideal solution would consist in mitigating the interference signals by creating synthetic signals corresponding to the interferences and subtract them from the actual measurements. For this purpose, an algorithm was developed which makes use of a priori information on the natural scene provided by meteorological models. Accounting for this information, it is possible to retrieve an accurate description of the RFI from the visibilities between antennas, and therefore create the corresponding signal. Even though assessing the performances of a mitigation algorithm for SMOS is not straightforward as it has to be done indirectly, different methods are proposed and they all show a general improvement of the data for this particular algorithm. Nevertheless due to the complexity of assessing the performances at the global scale, and the uncertainty inevitably introduced along with the synthetic signal, and to avoid a naive use of the mitigated data by the end user, for the time being an operational implementation of mitigation algorithms is not foreseen. Instead, an intermediate solution is proposed which consists of estimating the RFI contamination for a given snapshot, and then creating a map of the regions that are contaminated to less than a certain (or several) threshold(s). Another goal has been to allow the characterization of RFI (location on the ground, power emitted, position in the field of view) within a specified geographic zone in a short time. This approach uses the Fourier components of the observed scene to evaluate the brightness temperature spatial distribution in which the RFIs are represented as "hot spots". This algorithm has proven reliable, robust and precise, so that it can be used for the creation of RFI databases and monitoring of the RFI contamination at the local and global scale. Such databases were in fact created and used to highlight systematic errors of the instrument and seasonal variation of the localization results. The second main research topic has been to investigate the principle of SMOS-NEXT, a prospective mission that aims at assuring the continuity of space-borne soil moisture and ocean salinity measurements in the future with significantly improved spatial resolution of the retrievals. In order to achieve the latter this project intends to implement a groundbreaking interferometric approach called the spatio-temporal aperture synthesis. This technique consists in correlating the signals received at antennas in different places at different times, within the coherence limits imposed by the bandwidth. To prove the feasibility of this technique, a measurement campaign was carried out at the radio-telescope in Nançay, France. Even though the analysis of the experimental data has not allowed concluding on the validity of the measurement principle, a series of difficulties have been highlighted and the thus gained knowledge constitutes a valuable base for the foreseen second measurement campaign

The sensitivity of land emissivity estimates from AMSR-E at C and X bands to surface properties

Microwave observations at low frequencies exhibit more sensitivity to surface and subsurface properties with little interference from the atmosphere. The objective of this study is to develop a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and to investigate its sensitivity to land surface properties. The developed product complements existing land emissivity products from SSM/I and AMSU by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, are obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties are obtained from the TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. A sensitivity test confirms the small effect of the atmosphere but shows that skin temperature accuracy can significantly affect emissivity estimates. Retrieved emissivities at C- and X-bands and their polarization differences exhibit similar patterns of variation with changes in land cover type, soil moisture, and vegetation density as seen at SSM/I-like frequencies (Ka and Ku bands). The emissivity maps from AMSR-E at these higher frequencies agree reasonably well with the existing SSM/I-based product. The inherent discrepancy introduced by the difference between SSM/I and AMSR-E frequencies, incidence angles, and calibration has been assessed. Significantly greater standard deviation of estimated emissivities compared to SSM/I land emissivity product was found over desert regions. Large differences between emissivity estimates from ascending and descending overpasses were found at lower frequencies due to the inconsistency between thermal IR skin temperatures and passive microwave brightness temperatures which can originate from below the surface. The mismatch between day and night AMSR-E emissivities is greater than ascending and descending differences of SSM/I emissivity. This is because of unique orbit time of AMSR-E (01:30 a.m./p.m. LT) while other microwave sensors have orbit time of 06:00 to 09:00 (a.m./p.m.). This highlights the importance of considering the penetration depth of the microwave signal and diurnal variability of the temperature in emissivity retrieval. The effect of these factors is greater for AMSR-E observations than SSM/I observations, as AMSR-E observations exhibit a greater difference between day and night measures. This issue must be addressed in future studies to improve the accuracy of the emissivity estimates especially at AMSR-E lower frequencies

A Demonstration of the Effects of Digitization on the Calculation of Kurtosis for the Detection of RFI in Microwave Radiometry

Abstract-Microwave radiometers detecting geophysical parameters are very susceptible to radio-frequency interference (RFI) from anthropogenic sources. RFI is always additive to a brightness observation, and so the presence of RFI can bias geophysical parameter retrieval. As microwave radiometers typically have the most sensitive receivers operating in their band, low-level RFI is both significant and difficult to identify. The kurtosis statistic can be a powerful means of identifying some types of low-level RFI, as thermal noise has a distinct kurtosis value of three, whereas thermal noise contaminated even with low-level nonthermal RFI often has other values of kurtosis. This paper derives some benign distortions of the kurtosis statistic due to digitization effects and demonstrates these effects with a laboratory experiment in which a known amount of low-level RFI is injected into a digital microwave radiometer

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Radio frequency interference detection and mitigation techniques for navigation and Earth observation

Radio-Frequency Interference (RFI) signals are undesired signals that degrade or disrupt the performance of a wireless receiver. RFI signals can be troublesome for any receiver, but they are especially threatening for applications that use very low power signals. This is the case of applications that rely on the Global Navigation Satellite Systems (GNSS), or passive microwave remote sensing applications such as Microwave Radiometry (MWR) and GNSS-Reflectometry (GNSS-R). In order to solve the problem of RFI, RFI-countermeasures are under development. This PhD thesis is devoted to the design, implementation and test of innovative RFI-countermeasures in the fields of MWR and GNSS. In the part devoted to RFI-countermeasures for MWR applications, first, this PhD thesis completes the development of the MERITXELL instrument. The MERITXELL is a multi-frequency total-power radiometer conceived to be an outstanding platform to perform detection, characterization, and localization of RFI signals at the most common MWR imaging bands up to 92 GHz. Moreover, a novel RFI mitigation technique is proposed for MWR: the Multiresolution Fourier Transform (MFT). An assessment of the performance of the MFT has been carried out by comparison with other time-frequency mitigation techniques. According to the results, the MFT technique is a good trade-off solution among all other techniques since it can mitigate efficiently all kinds of RFI signals under evaluation. In the part devoted to RFI-countermeasures for GNSS and GNSS-R applications, first, a system for RFI detection and localization at GNSS bands is proposed. This system is able to detect RFI signals at the L1 band with a sensitivity of -108 dBm at full-band, and of -135 dBm for continuous wave and chirp-like signals when using the averaged spectrum technique. Besides, the Generalized Spectral Separation Coefficient (GSSC) is proposed as a figure of merit to evaluate the Signal-to-Noise Ratio (SNR) degradation in the Delay-Doppler Maps (DDMs) due to the external RFI effect. Furthermore, the FENIX system has been conceived as an innovative system for RFI detection and mitigation and anti-jamming for GNSS and GNSS-R applications. FENIX uses the MFT blanking as a pre-correlation excision tool to perform the mitigation. In addition, FENIX has been designed to be cross-GNSS compatible and RFI-independent. The principles of operation of the MFT blanking algorithm are assessed and compared with other techniques for GNSS signals. Its performance as a mitigation tool is proven using GNSS-R data samples from a real airborne campaign. After that, the main building blocks of the patented architecture of FENIX have been described. The FENIX architecture has been implemented in three real-time prototypes. Moreover, a simulator named FENIX-Sim allows for testing its performance under different jamming scenarios. The real-time performance of FENIX prototype has been tested using different setups. First, a customized VNA has been built in order to measure the transfer function of FENIX in the presence of several representative RFI/jamming signals. The results show how the power transfer function adapts itself to mitigate the RFI/jamming signal. Moreover, several real-time tests with GNSS receivers have been performed using GPS L1 C/A, GPS L2C, and Galileo E1OS. The results show that FENIX provides an extra resilience against RFI and jamming signals up to 30 dB. Furthermore, FENIX is tested using a real GNSS timing setup. Under nominal conditions, when no RFI/jamming signal is present, a small additional jitter on the order of 2-4 ns is introduced in the system. Besides, a maximum bias of 45 ns has been measured under strong jamming conditions (-30 dBm), which is acceptable for current timing systems requiring accuracy levels of 100 ns. Finally, the design of a backup system for GNSS in tracking applications that require high reliability against RFI and jamming attacks is proposed.Les interferències de radiofreqüència (RFI) són senyals no desitjades que degraden o interrompen el funcionament dels receptors sense fils. Les RFI poden suposar un problema per qualsevol receptor, però són especialment amenaçadores per les a aplicacions que fan servir senyals de molt baixa potència. Aquest és el cas de les aplicacions que depenen dels sistemes mundials de navegació per satèl·lit (GNSS) o de les aplicacions de teledetecció passiva de microones, com la radiometria de microones (MWR) i la reflectometria GNSS (GNSS-R). Per combatre aquest problema, sistemes anti-RFI s'estan desenvolupament actualment. Aquesta tesi doctoral està dedicada al disseny, la implementació i el test de sistemes anti-RFI innovadors en els camps de MWR i GNSS. A la part dedicada als sistemes anti-RFI en MWR, aquesta tesi doctoral completa el desenvolupament de l'instrument MERITXELL. El MERITXELL és un radiòmetre multifreqüència concebut com una plataforma excepcional per la detecció, caracterització i localització de RFI a les bandes de MWR més utilitzades per sota dels 92 GHz. A més a més, es proposa una nova tècnica de mitigació de RFI per MWR: la Transformada de Fourier amb Multiresolució (MFT). El funcionament de la MFT s'ha comparat amb el d'altres tècniques de mitigació en els dominis del temps i la freqüència. D'acord amb els resultats obtinguts, la MFT és una bona solució de compromís entre les altres tècniques, ja que pot mitigar de manera eficient tots els tipus de senyals RFI considerats. A la part dedicada als sistemes anti-RFI en GNSS i GNSS-R, primer es proposa un sistema per a la detecció i localització de RFI a les bandes GNSS. Aquest sistema és capaç de detectar senyals RFI a la banda L1 amb una sensibilitat de -108 dBm a tota la banda, i de -135 dBm per a senyals d'ona contínua i chirp fen un mitjana de l'espectre. A més a més, el Coeficient de Separació Espectral Generalitzada (GSSC) es proposa com una mesura per avaluar la degradació de la relació senyal a soroll (SNR) en els Mapes de Delay-Doppler (DDM) a causa del impacte de les RFI. La major contribució d'aquesta tesi doctoral és el sistema FENIX. FENIX és un sistema innovador de detecció i mitigació de RFI i inhibidors de freqüència per aplicacions GNSS i GNSS-R. FENIX utilitza la MFT per eliminar la interferència abans del procés de correlació amb el codi GNSS independentment del tipus de RFI. L'algoritme de mitigació de FENIX s'ha avaluat i comparat amb altres tècniques i els principals components de la seva arquitectura patentada es descriuen. Finalment, un simulador anomenat FENIX-Sim permet avaluar el seu rendiment en diferents escenaris d'interferència. El funcionament en temps real del prototip FENIX ha estat provat utilitzant diferents mètodes. En primer lloc, s'ha creat un analitzador de xarxes per a mesurar la funció de transferència del FENIX en presència de diverses RFI representatives. Els resultats mostren com la funció de transferència s'adapta per mitigar el senyal interferent. A més a més, s'han realitzat diferents proves en temps real amb receptors GNSS compatibles amb els senyals GPS L1 C/A, GPS L2C i Galileo E1OS. Els resultats mostren que FENIX proporciona una resistència addicional contra les RFI i els senyals dels inhibidors de freqüència de fins a 30 dB. A més a més, FENIX s'ha provat amb un sistema comercial de temporització basat en GNSS. En condicions nominals, sense RFI, FENIX introdueix un petit error addicional de tan sols 2-4 ns. Per contra, el biaix màxim mesurat en condicions d'alta interferència (-30 dBm) és de 45 ns, el qual és acceptable per als sistemes de temporització actuals que requereixen nivells de precisió d'uns 100 ns. Finalment, es proposa el disseny d'un sistema robust de seguiment, complementari als GNSS, per a aplicacions que requereixen alta fiabilitat contra RFI.Postprint (published version

Interrelationships between soil moisture and precipitation large scales, inferred from satellite observations

Soil moisture influences the water and energy cycles of terrestrial environments, and thus plays an important climatic role. However, the behavior of soil moisture at large scales, including its impact on atmospheric processes such as precipitation, is not well characterized. Satellite remote sensing allows for indirect observation of large-scale soil moisture, but validation of these data is complicated by the difference in scales between remote sensing footprints and direct ground-based measurements. To address this problem, a method, based on information theory (specifically, mutual information), was developed to determine the useful information content of satellite soil moisture records using precipitation observations. This method was applied to three soil moisture datasets derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements over the contiguous U.S., allowing for spatial identification of the algorithm with the least inferred error. Ancillary measures of biomass and topography revealed a strong dependence between algorithm performance and confounding surface properties. Next, statistical causal identification methods (i.e. Granger causality) were used to examine the link between AMSR-E soil moisture and the occurrence of next day precipitation, accounting for long term variability and autocorrelation in precipitation. The probability of precipitation occurrence was modeled using a probit regression framework, and soil moisture was added to the model in order to test for statistical significance and sign. A contrasting pattern of positive feedback in the western U.S. and negative feedback in the east was found, implying a possible amplification of drought and flood conditions in the west and damping in the east. Finally, observations and simulations were used to demonstrate the pitfalls of determining causality between soil moisture and precipitation. It is shown that ignoring long term variability and precipitation autocorrelation can result in artificial positive correlation between soil moisture and precipitation, unless explicitly accounted for in the analysis. In total, this dissertation evaluates large-scale soil moisture measurements, outlines important factors that can cloud the determination of land surface-atmosphere hydrologic feedback, and examines the causal linkage between soil moisture and precipitation at large scales