An RFI Detection Algorithm for Microwave Radiometers Using Sparse Component Analysis

Radio Frequency Interference (RFI) is a threat to passive microwave measurements and if undetected, can corrupt science retrievals. The sparse component analysis (SCA) for blind source separation has been investigated to detect RFI in microwave radiometer data. Various techniques using SCA have been simulated to determine detection performance with continuous wave (CW) RFI

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

Performance assessment of time–frequency RFI mitigation techniques in microwave radiometry

©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.Radio–frequency interference (RFI) signals are a well-known threat for microwave radiometry (MWR) applications. In order to alleviate this problem, different approaches for RFI detection and mitigation are currently under development. Since RFI signals are man made, they tend to have their power more concentrated in the time–frequency (TF) space as compared to naturally emitted noise. The aim of this paper is to perform an assessment of different TF RFI mitigation techniques in terms of probability of detection, resolution loss (RL), and mitigation performance. In this assessment, six different kinds of RFI signals have been considered: a glitch, a burst of pulses, a wide-band chirp, a narrow-band chirp, a continuous wave, and a wide-band modulation. The results show that the best performance occurs when the transform basis has a similar shape as compared to the RFI signal. For the best case performance, the maximum residual RFI temperature is 14.8 K, and the worst RL is 8.4%. Moreover, the multiresolution Fourier transform technique appears as a good tradeoff solution among all other techniques since it can mitigate all RFI signals under evaluation with a maximum residual RFI temperature of 21 K, and a worst RL of 26.3%. Although the obtained results are still far from an acceptable bias Misplaced < 1 K for MWR applications, there is still work to do in a combined test using the information gathered simultaneously by all mitigation techniques, which could improve the overall performance of RFI mitigation.Peer ReviewedPostprint (author's final draft

On the potential of empirical mode decomposition for RFI mitigation in microwave radiometry

Radio-frequency interference (RFI) is an increasing problem particularly for Earth observation using microwave radiometry. RFI has been observed, for example, at L-band by the European Space Agency’s (ESA’s) soil moisture and ocean salinity (SMOS) Earth Explorer and by National Aeronautics and Space Administration’s (NASA’s) soil moisture active passive (SMAP) and Aquarius missions, as well as at C-band by Advanced Microwave Scanning Radiometer (AMSR)-E and AMSR-2, and at 10.7 and 18.7 GHz by AMSR-E, AMSR-2, WindSat, and GPM Microwave Imager (GMI). Therefore, systems dedicated to interference detection and removal of contaminated measurements are nowadays a must in order to improve radiometric accuracy and reduce the loss of spatial coverage caused by interference. In this work, the feasibility of using the empirical mode decomposition (EMD) technique for RFI mitigation is explored. The EMD, also known as Hilbert–Huang transform (HHT), is an algorithm that decomposes the signal into intrinsic mode functions (IMFs). The achieved performance is analyzed, and the opportunities and caveats that this type of methods present are described. EMD is found to be a practical RFI mitigation method, albeit presenting some limitations and considerable complexity. Nevertheless, in some conditions, EMD exhibits a better performance than other commonly used methods (such as frequency binning). In particular, it has been found that EMD performs well for RFI affecting the <25% lower part of the intermediate frequency (IF) bandwidth.This work was supported in part by the Sensing With Pioneering Opportunistic Techniques (SPOT) under Grant RTI2018-099008-B-C21/ AEI/10.13039/501100011033, in part by the RYC-2016-20918 under Grant MCIN/AEI/10.13039/501100011033, and in part by the European Social Fund (ESF), Investing in your future.Peer ReviewedPostprint (author's final draft

Microwave Radiometry at Frequencies From 500 to 1400 MHz: An Emerging Technology for Earth Observations

icrowave radiometry has provided valuable spaceborne observations of Earth’s geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observ- ing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsur- face at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under veg- etation canopies. However, the absence of significant spectrum re- served for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities

Passive Remote Sensing of Lake Ice and Snow using Wideband Autocorrelation Radiometer (WiBAR).

Snow cover plays a vital role in providing the water supplies for domestic, industrial, and agricultural purposes. Conventionally, differential scatter darkening technique is used to detect the snow thickness. This technique is region specific and depends on the statistics of snow grain sizes. Ice formation process and ice thickness monitoring are important parameters in analyzing the overall pressure exerted to the off-shore structures such as wind farms. The traditional method for measuring the lake ice thickness is by a cumbersome drilling process through the ice. For future in-situ or remote planetary applications, the detection and analysis of ice sheets on or near the surface is one of the primary objectives of many planetary exploration missions. These applications demonstrate the requirement for an accurate remote sensing instrument, which can estimate the ice thickness without disturbing or breaking the ice. In this work, a novel microwave remote sensing technique to accurately estimate the thickness of any layered low-absorbing media including snow pack and fresh water ice using wideband autocorrelation radiometer (WiBAR) is presented. This technique relies on finding the autocorrelation response of the upwelling brightness temperature. The autocorrelation response provides enough information to estimate the microwave travel time delay of the doubly reflected thermal emission between the top and bottom interfaces an consequently the thickness of the snow or ice layer can be obtained. Several post processing techniques are developed to capture the periodicity of the ripples in the power spectral density domain. These techniques are capable of detecting very weak ripples deeply buried under noise. A compressive sensing based algorithm is also developed for detecting the thickness of ice/snow layers using 1/10 of the Nyquist rate samples. We have successfully designed, implemented, and tested a handheld ground base ice/snow thickness sensor in the frequency range of 1-3GHz or 7-10GHz under several scenarios including snow on top of undulated and vegetation covered terrain, ice over the lake water, air gap above a water surface and below a dielectric sheet, and snow cover under the forest canopy in the presence of radio frequency interference (RFI) with accuracy of within 1.5cm.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110419/1/hnejati_1.pd

Development of Radio Frequency Interference Detection Algorithm for Passive Microwave Remote Sensing

Radio Frequency Interference (RFI) signals are man-made sources that are increasingly plaguing passive microwave remote sensing measurements. RFI is of insidious nature, with some signals low power enough to go undetected but large enough to impact science measurements and their results. With the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite in November 2009 and the upcoming launches of the new NASA sea-surface salinity measuring Aquarius mission in June 2011 and soil-moisture measuring Soil Moisture Active Passive (SMAP) mission around 2015, active steps are being taken to detect and mitigate RFI at L-band. An RFI detection algorithm was designed for the Aquarius mission. The algorithm performance was analyzed using kurtosis based RFI ground-truth. The algorithm has been developed with several adjustable location dependant parameters to control the detection statistics (false-alarm rate and probability of detection). The kurtosis statistical detection algorithm has been compared with the Aquarius pulse detection method. The comparative study determines the feasibility of the kurtosis detector for the SMAP radiometer, as a primary RFI detection algorithm in terms of detectability and data bandwidth. The kurtosis algorithm has superior detection capabilities for low duty-cycle radar like pulses, which are more prevalent according to analysis of field campaign data. Most RFI algorithms developed have generally been optimized for performance with individual pulsed-sinusoidal RFI sources. A new RFI detection model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis detection algorithm under such central-limit conditions is evaluated. The SMOS mission has a unique hardware system, and conventional RFI detection techniques cannot be applied. Instead, an RFI detection algorithm for SMOS is developed and applied in the angular domain. This algorithm compares brightness temperature values at various incidence angles for a particular grid location. This algorithm is compared and contrasted with other algorithms present in the visibility domain of SMOS, as well as the spatial domain. Initial results indicate that the SMOS RFI detection algorithm in the angular domain has a higher sensitivity and lower false-alarm rate than algorithms developed in the other two domains.Ph.D.Atmospheric and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86308/1/samisra_1.pd

The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;value&lt;/sub&gt; data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results