This study presents a retrieval study for several physical, chemical, and thermal properties of the lunar regolith using microwave radiometry. The Apollo 15 site was selected for the study owing to the abundance of in-situ measurements regarding such regolith properties, and the 4-channel Chang’E-1 microwave radiometer data were used for the retrieval. The results have demonstrated that diurnal brightness temperature measurements with a wideband microwave radiometer can reveal important regolith parameters related with the regolith thickness, density, and temperature, as well as the amount of ilmenite in the regolith. On the other hand, it has also been observed that the retrievals may not be unique; thus, ancillary data may be needed to constrain the estimates.

Instrument power cycling can be performed in several different ways. Rapid power cycling can reduce the average power draw and provide continuous measurements, at the cost of higher measurement uncertainty. Power cycling can also be performed at longer intervals, from seconds to minutes, allowing more time for the receiver to stabilize, but at the cost of continuous measurements. The use of a convolutional neural network (CNN) can provide the means to produce calibrated measurements in the presence of power cycling over both long and short intervals. CNN and other Artificial Neural Network (ANN) -based calibration approaches have been studied in recent years suggesting they produce calibration errors equal to or lower than those errors produced by using conventional calibration methods [1], [2]. With sufficient training data, the transient response characteristics of a receiver can be incorporated into a CNN model capable of producing calibrated measurements while an instrument is not in equilibrium. In this paper, a simulated radiometer model is used to produce calibration windows with transient response characteristics. A synthetic training dataset is produced to train a CNN which produces calibrated measurements. The CNN calibrator is evaluated against a linear least-squares (LSR) calibration method and shown to produce smaller calibration errors on a second independent synthetic dataset. By enabling the collection of quality calibrated measurements in the presence of rapid receiver power cycling, this technique can improve the power efficiency of space-borne radiometers by enabling measurement calibration prior to temperature stabilization, particularly for radiometer-equipped CubeSats and other SmallSats.

Measurements of Earth-observing radiometers have been reported to be contaminated by radio frequency interference (RFI) due to active emissions in the densely occupied radio spectrum. This study presents a multi-dimensional, one-class Bayesian algorithm to detect and eliminate such RFI. The proposed algorithm was trained with RFI-free measurements only and operated in a feature space where the separation between RFI-free and RFI-contaminated measurements was maximized. Using standard metrics, the performance of the Bayesian algorithm was evaluated, and it was demonstrated that the one-class detection algorithm presented in this paper can outperform the existing state-of-the-art RFI detection algorithms, specifically for the low interference to noise ratio (INR) cases.

This paper discusses the retrieval of important thermal and physical properties of the Antarctic firn via spaceborne microwave radiometry focusing on the Concordia station. Previous studies have indicated that microwave radiometer measurements are sensitive to important properties of the firn from its surface down to deep isothermal ice. Expanding on those work, yearlong AMSR2 and SSMIS radiometer measurements over the Concordia station have been used to create brightness temperature spectrograms, i.e., brightness temperatures versus month and frequency, and these spectrograms have been used to retrieve subsurface density and temperature properties of the firn using a plausible forward radiation model. It has been found that, utilizing the wide microwave spectrum and long-term measurements, density variations due to internal firn layering, as well as seasonal temperature variations in the near-surface firn can be accurately estimated. In addition, densification of the firn with depth and the deep firn temperature can be retrieved with adequate ancillary data. The results mainly suggest that when deployed with other instruments such as ground penetrating radars, wideband microwave radiometers can be useful for characterization of ice sheets in future polar remote sensing missions.

This study presents the potential of L- to X-band wideband radiometry to retrieve important geophysical and thermal properties of the lunar regolith through simulated measurements of a 30-channel 1-10 GHz microwave radiometer. It has been demonstrated that regolith thickness, densification with depth, water ice percentage in the regolith, as well as the geothermal heat flux can be estimated with little (95%) confidence if surface brightness temperatures are collected with such an instrument throughout the entire diurnal cycle. On the other hand, ancillary information regarding other regolith properties such as internal layerings, and the surface density and temperature values may be necessary for a nonunique retrieval; thus, deployment of other instruments such as ground penetrating radars with wideband radiometers would be useful during the future lunar missions.

Future smart sensors will be able to utilize the maximum information content from data products, while minimizing the resources required to acquire, downlink, and process data. In-orbit calibration is required for space-borne radiometers in order to correct for gain fluctuations. Many sensors like radiometers are generally only able to produce calibrated scene measurements after reaching steady state. Waiting to reach thermal equilibrium to obtain useful data results in wasted power, excess useless data, and delays in obtaining useful data. Instrument power cycling provides a way to lower power use, but at the cost of pauses in data collection when the instrument is cycled off. Rapid power cycling can be used to reduce the average power draw of a radiometer, at the cost of increased measurement uncertainty. These power cycling techniques have been used on real systems, including the IceCube radiometer [1]. Using a convolutional neural network trained on synthetic data, a simulated radiometer can produce calibrated measurements with lower uncertainties and errors than conventional least-squares-regression (LSR) - based estimators. This approach presents an opportunity to reduce the average power draw of a radiometer by minimizing uncertainties of calibrated data products collected during rapid power cycling.

Radio frequency interference (RFI) in the measurements of Earth-observing radiometers is increasing over time with the increase in frequency spectrum demand for active services [1] . RFI contamination may lead to erroneous retrieval of the critical geophysical parameters in passive remote sensing missions. To overcome this problem, several detection and mitigation algorithms have been proposed and implemented with only limited success in cases of weak or noise–like interference [2] . The existing algorithms can be divided into two categories based on the number of dimensions used in the detection process, i.e., single (e.g., pulse blanking [3] ) and multiple dimensions (e.g., SMAP radiometer [4] ) algorithms. It should be noted that the cumulative information from the multiple dimensions is proven to be more effective than the algorithms that rely on a single dimension [5] , [6] . Furthermore, the occurrence of RFI is diverse. Therefore, currently, there is no labeled baseline dataset is available for RFI–contaminated data. Taking these into consideration, in this work, we have proposed the one class support vector machine algorithm (OCSVM) to use the information from RFI–free measurements alone. Furthermore, we have performed an extensive feature analysis to differentiate between the RFI–contaminated and RFI–free measurements.

Recent advances in CubeSat technologies have enabled use of radiometers deployed in constellations of these small satellites for Earth and space science missions. Advantages of CubeSats such as their low cost, low mass and volume, and lower power requirements, however, are confronted by the challenges in calibration of their payloads as well as intercalibration of CubeSat constellations due to higher sensitivity to ambient conditions. This paper describes a novel system-level calibration framework, called “ACCURACy” to calibrate CubeSat based radiometer constellations as a single system in their entirety with minimal errors and uncertainties. Artificial constellation simulations have demonstrated that ACCURACy, while maintaining the accuracy levels of ideal calibration scenarios, leads to lower uncertainties in calibrated radiometer products compared to state-of-the-art calibration and intercalibration techniques based on overlapping measurements of the constellation members.

In this paper, a revised version of a previously introduced radio frequency interference (RFI) detection technique based on a Support Vector Machine (SVM) algorithm is discussed and evaluated for microwave radiometry. The algorithm was trained and tested in a multi -dimensional feature space created with simulated radiometer measurements where pulsed sinusoidal interference signals with various duty cycle and interference-to-noise (INR) levels were injected into the radiometer data. Initial analyses demonstrated that the introduced algorithm led to significant improvements in RFI detection performance compared to the state-of-the-art methods, especially for low INR and duty cycle values. Future studies will include training the algorithm for different RFI types and implementations with real radiometer data and hardware.

In the future, smart sensors will be designed to extract maximum value information, while minimizing the resources required to acquire, downlink, and process data. Many sensors like radiometers are only able to produce calibrated measurements after reaching steady state. However, waiting until reaching thermal equilibrium to obtain useful data leads to wasted power, excess useless data, and delays in obtaining useful data. Power cycling a radiometer is one way to circumvent this requirement, but leads to other challenges, as turning power off to instrument not only stops data acquisition until it is powered on, but also past power-on until it reaches thermal equilibrium again. This paper introduces a framework which will use machine learning algorithms to enable the calibration of a radiometer during its transient state after power-on and in the presence of power cycling, aiming to further reduce resource utilization.

This paper discusses the retrieval of important physical properties of the Antarctic firn via spaceborne microwave radiometry focusing on Concordia and Vostok stations. Previous studies indicated that microwave radiometer measurements are sensitive to important physical properties of the firn from its surface down to deep isothermal ice. Here we demonstrated how brightness temperature differences measured by spaceborne radiometers over different parts of the Antarctic firn are related to changes in physical properties such as density; thus, retrieval of these properties is possible.

This paper provides a brief analysis of existing depthdependent models of the physical properties of the polar firn using available data for subsurface density and grain radius. Results show that across inland Antarctica, firn density increases exponentially with depth. The density has gaussian fluctuations with negative damping. Grain radius increases linearly with depth for up to a few hundred meters. A standardized dataset for in-situ measurements of density and grain radius is formed and input parameters for their depthdependent models are summarized in this paper.

Although ice sheet internal temperature is a first-order control on glacier dynamics, relatively few in situ borehole temperature profiles exist. The ultra-wideband software-defined microwave radiometer (UWBRAD) was designed to estimate internal ice sheet temperature (Ti) by measuring microwave brightness temperatures (Tb) from 0.5 GHz to 2 GHz. The retrieval ofTifromTbis not straightforward, however, due in part to the complicating effects of ice density fluctuations onTb. In this paper, we report a simulation study to assess the feasibility of realizing three science goals: the retrieval of a)Tiat 10 m depth to within 1 K; b) vertically-averagedTito within 1 K; and c) the verticalTiprofile to within 1 K RMSE. Two analyses along the Greenland ice divide are presented. First, we assess the ideal UWBRADTiretrieval precision via the Cramér-Rao Lower Bound (CRLB). Second, we perform a “Virtual Experiment” (VE) using synthetic UWBRAD observations. Both the CRLB and VE analyses indicate that the science goals are achievable with the caveats that ice thickness and UWBRADTbprecision impact performance. Assuming a UWBRADTbprecision of 0.5 K, and for places where ice sheet thickness is less than 3 km, all science goals can be achieved. The results of the study provide a strong indication of the potential of UWBRAD to provide valuable Greenland ice temperature profile information to the scientific community.

Recent advances in space technologies enable science missions using CubeSats equipped with radiometers. Constellations of CubeSats can be used to significant effect, overcoming obstacles in cost, weight, and power. However, these benefits come at a cost, including challenges in calibration due in large part to increased sensitivity of the instrument to ambient conditions. These limitations also mean conventional calibration methods are not always possible. To address this problem, a novel, constellation-level calibration framework called “Adaptive Calibration of CubeSat Radiometer Constellations (ACCURACy)” is being developed. ACCURACy uses instrument-level telemetry data to cluster constellation members into time-adaptive groups of radiometers in similar states and facilitates calibration data sharing within each group for optimum calibration performance. This paper presents a prototype MA TLAB framework using synthetic radiometer data and discusses its calibration performance.

This paper discusses the utilization of the Global Precipitation Measurement (GPM) constellation as a single multi-frequency radiometer to profile subsurface properties of the polar firn. Initial analyses focusing on the Concordia station in Antarctica have demonstrated that GPM brightness temperature measurements can be successfully simulated across a wide frequency spectrum below 100 GHz and are sensitive to important physical properties of the firn from its surface down to deep isothermal ice. Therefore, the GPM constellation provides an excellent opportunity for characterizing the polar firn with broad spatiotemporal coverage.

Recent technological developments have enabled usage of constellations of radiometer carrying CubeSats in scientific remote sensing missions. CubeSats, forming such constellations, on the other hand, bring unique challenges in terms of calibration of their instruments as they are easily impacted by ambient conditions. To address this problem, a constellation level calibration framework called “Adaptive Calibration of CUbesat RAdiometer Constellations (ACCURACy)” is introduced in this paper. The framework utilizes machine-learning algorithms such as principal component analysis and density based clustering to separate constellation members into time-adaptive groups of similar-state radiometers based on their telemetry data. Within each group, all radiometers will contribute to a calibration data pool with their absolute calibration measurements. Such shared data pools, which include measurements of different calibration targets at different times, will facilitate frequent N>2-point absolute calibration; thus, reduce and quantify calibration errors and uncertainties.

Multi-frequency microwave radiometer measurements can reveal important subsurface characteristics of ice sheets such as internal temperature, density, grain size and ice thickness, which are critical to understand ice sheet dynamics. This study explores the sensitivity of electromagnetic radiation from ice sheets to these properties across the microwave spectrum, from L-band to W-band. The results indicate that (i) the maximum depth for which surface radiations provide information decreases from >2000 m in L-band to ~3 m in W-band, (ii) seasonal variations in internal temperatures are reflected mostly at frequencies in Ka-band and above, (iii) impact of the geothermal heat flux can be observed mainly in L- and S-bands, and (iv) changes in density and grain size properties affect the electromagnetic penetration depth, thus, may influence surface emissions across the entire microwave spectrum.

NASA's SMAP radiometer was launched in 2015, and owing to its digital backend is providing geolocated raw power moments measured at frequencies between 1400-1424 MHz with 1.2 ms and 1.5 MHz time and frequency resolution. Utilizing SMAP measurements between August 25-31, 2018, temporal and spectral properties of the RFI contamination within the protected portion of L-band have been retrieved. It has been found that the average bandwidth and duration of individual RFI sources are less than 4.5 MHz and 4.8 ms, respectively in most regions with exceptions over Europe, Middle East, and Eastern Asia. On the other hand, multiple RFI sources occupying different parts of the spectrum may lead to available RFI-free spectrum less than 12 MHz. Finally, there were no significant correlations noted among amplitude, bandwidth, and duration of RFI sources.

Although considered as stationary and Gaussian in general, radiometer gain is usually a fluctuating signal with non-stationary properties. Analyses of such non-stationary features is challenging as the radiometer signal cannot be observed independently. On the other hand, time series of post-gain voltages constitute an ensemble set for the radiometer gain which can be used to characterize the radiometer gain. This paper presents a novel technique called "Ensemble Detection" which can analytically retrieve the standard deviation of stationary Gaussian radiometer gain or find an equivalent stationary Gaussian process which represents the non-stationary radiometer gain under different calibration schemes. It has been found that the equivalent Gaussian process for non-stationary radiometer gain heavily depends on the calibration structure and the observation times of the measurand and the calibration references.

The performance of radio frequency interference (RFI) detection and mitigation algorithms depends on the properties of the RFI signals against which they are used. This letter presents the bandwidth, duration, and center frequency of the RFI signals that the Soil Moisture Active Passive (SMAP) radiometer has observed in the course of one week (June 3-9, 2018) over the entire world. It has been shown that L-band RFI is a significant problem on a global scale, and SMAP multidomain RFI detection approach supported by its digital backend is well justified as the bandwidth and duration characteristics of the RFI environment may vary significantly.

Significant information for understanding the Earth's environment such as sea surface temperature, rain rates, and ocean wind speeds, is retrieved using C and X band spaceborne radiometer measurements. However, radio spectrum is a limited resource, and as new communication and military technologies emerge, more and more human-made signals occupy these frequency bands allocated for passive remote sensing of Earth. This paper demonstrates the expansion of the interference problem due to such signals from 2002 to present using 6.9GHz and 10.65GHz AMSR-E and AMSR2 measurements over Europe.

Recent studies have demonstrated that wideband microwave radiometers provide significant potential for profiling important subsurface properties of ice sheets necessary to understand ice sheet dynamics and predict future changes in ice coverage. Different frequencies within the wide spectra of such radiometers result in different electromagnetic propagation losses, thus reveal characteristics at different depths in ice sheets. This study explores the utilization of the Global Precipitation Measurement (GPM) constellation as a single wideband receiver to profile subsurface properties of ice sheets. Results indicated that intercalibrated GPM brightness temperature measurements over Concordia and Vostok stations in Antarctica can provide information regarding the subsurface temperatures and density properties of snow and ice down to a few tens of meters depth from surface.

An ultra-wideband radiometer has been developed to measure subsurface properties of the cryosphere including ice sheets and sea ice. The radiometer measures brightness temperature spectra from 0.5 to 2 GHz using 12 channels, each of which measures scene brightness temperatures over an ~88-MHz bandwidth resolved into 0.24-MHz intervals. The instrument was flown over northwestern Greenland in September 2016 and acquired the first, wideband, low-frequency brightness temperature spectra over the ice sheet and coastal region. The results reveal strong spatial and spectral variations that correlate well with the physical properties of the surface encountered along the flight path, which started over ocean, then passed the rock near the coast, and then up onto the ablation, wet, percolation, and dry snow zones of the interior ice sheet. In particular, strong spectral responses in percolation and dry snow zones are observed and plausibly explained by varying the distribution of horizontal density layers and isolated icy bodies in the upper portion of the firn. The success of the airborne deployment of the instrument and subsequent implementation of algorithms to limit radio frequency interference in unprotected bands is motivating continued airborne investigations as well as stimulating research into the feasibility of a spaceborne instrument.

Tracking the radiometer calibration stability is very important for climate monitoring radiometers as long term accuracy of observations is needed to create reliable climate models. This presentation discusses the advantages of 3-point onboard calibration techniques over 2-point methods to track radiometer calibration stability.

The ultra-wideband software-defined microwave radiometer (UWBRAD) is designed to provide ice sheet internal temperature by measuring low frequency microwave emission. A Bayesian framework is designed to retrieve the ice sheet internal temperature from simulated UWBRAD brightness temperature (Tb). Experiment results showed feasibility to estimate ice sheet internal temperature and improvement of priors.

The Ultra-wideband Software Defined Radiometer (UWBRAD) for ice sheet internal temperature sensing is designed to provide observations of ice sheet brightness temperatures from 500-2000 MHz. This presentation reports on current status of the instrument development, experimental results obtained to date, and plans for a September 2016 airborne deployment over Greenland.

The Soil Moisture Active Passive (SMAP) mission, launched January 31, 2015, provides global observations of 1.4 GHz Earth thermal emissions from space through its L-band radiometer. Although SMAP's radiometer passband lies within the protected 1.4-1.427 GHz band, both unauthorized in-band transmitters as well as out-of-band emissions from transmitters operating at frequencies adjacent to this allocated spectrum have been documented as sources of radio frequency interference (RFI) to the L-band radiometers on SMOS and Aquarius. Low level RFI (0.1-10 Kelvin) is especially problematic as it can be mistaken for natural variability and if left unmitigated can corrupt radiometer measurements leading to flawed retrievals. SMAP has an aggressive approach to RFI mitigation using an advanced digital microwave radiometer to provide time and frequency measurements as well as a comprehensive ground processing algorithm.

Radio-frequency interference (RFI) observations for L-band microwave radiometry during the SMAP Validation Experiment 2012 (SMAPVEX12) airborne campaign are reported in this paper. The soil moisture measurement campaign was conducted in summer 2012 near Winnipeg, MB, Canada, with additional RFI flights over Denver, CO, USA. The Passive Active L-Band sensor (PALS) radiometer of the Jet Propulsion Laboratory was used with a full-bandwidth direct sampling digital backend to measure and store predetection data that is fully resolved in time and frequency. Overviews of SMAPVEX12 and the receiver and digital backend used to collect data are presented, along with the data processing techniques used for RFI detection. Properties of the observed RFI are examined and compared with the results of previous studies. Finally, implications of the results are explained considering current missions such as NASA's Soil Moisture Active Passive Mission.

The National Aeronautics and Space Administration's (NASA) Soil Moisture Active and Passive (SMAP) mission, which was launched on January 31, 2015, is providing global measurements of soil moisture and freeze/thaw state. The SMAP radiometer operates within the protected Earth Exploration Satellite Service passive frequency allocation of 1400-1427 MHz. However, unauthorized in-band transmitters and out-of-band emissions from transmitters operating at frequencies adjacent to this allocated spectrum are known to cause interference to microwave radiometry in this band. Because measurement corruption by these terrestrial transmissions, which is referred to as radio-frequency interference (RFI), threatens mission success, the SMAP radiometer includes special flight hardware to enable the detection and filtering of RFI. Results from the first year of SMAP data show the presence of RFI with frequent occurrence over Asia and Europe. During the calibration/validation stage of the mission, the RFI detection and mitigation algorithms were modified to provide enhanced performance. Analysis of the L1B_TB products indicates good algorithmic performance with respect to RFI detection and removal. However, some regions of the globe (e.g., Japan) continue to experience complete data loss. This paper summarizes updates to the SMAP RFI processing algorithms based on prelaunch tests and on-orbit measurements, as well as RFI information obtained in SMAP's first year on orbit.

We investigate the degree to which ultrahigh frequency radio emission can be used to estimate subsurface physical temperature in the polar ice sheets. We combine electromagnetic emission forward models with plausible models of depth-dependent physical properties in the ice sheet. Temperature models are parameterized with variables including accumulation rate, geothermal heat flux, and surface temperature. Scattering is parameterized using empirical observations of grain growth combined with measured densities. Electromagnetic absorption is modeled using dielectric dispersion processes and semiempirical models based on observations. Our models illustrate that information about East Antarctic ice sheet temperature from near the surface to near the base can be gleaned from ultrawideband radiometer data. Based on our modeling study, we illustrate an instrument concept to measure ice sheet temperature profiles comprising a novel ultrawideband radiometer.

Many quantities describing ice sheet dynamics can be measured via remote sensing. However, subsurface ice sheet temperature, a very important parameter which determines in part internal deformation and ice flow, is not currently measured remotely. Direct knowledge is available only through measurements from a small number of boreholes. This paper presents the concept of utilizing microwave radiometry in the 0.5–2GHz frequency band to probe subsurface ice sheet temperatures. Ice sheet geophysical properties and the resulting microwave emission are reviewed, and an initial retrieval algorithm for subsurface ice sheet temperatures is described. Finally an ultrawideband radiometer design to realize the proposed measurements is summarized.

Radio frequency interference observations during the SMAP Validation Experiment 2012 (SMAPVEX12) airborne campaign are reported in this study. The campaign was conducted in Summer 2012 near Winnipeg, Canada with additional flights over Denver, CO. The Passive Active L-Band Sensor (PALS) radiometer of Jet Propulsion Laboratory (JPL) was used with a full bandwidth direct sampling receiver (called IBOB) to measure and store data fully resolved in time and frequency. In this paper, an overview of the campaign, hardware details, signal processing, and RFI detection are explained. Finally, properties of the observed RFI and future work are discussed.

Since the SMOS satellite launch in 2009, its L-band radiometer data have been analyzed in depth by scientists worldwide and have resulted in significant steps forward in different disciplines. As primary objectives of the mission, the main research focus has been related to soil moisture and ocean salinity. However, the availability of a complete long-term, all-weather time-series of calibrated global brightness temperature data has enabled much broader research investigations on other topics such as the Cryosphere. SMOS data collected over central of Antarctica were also analyzed and whereas Tb is in general very stable in time it presents some intriguing spatial variations which are not yet fully explained. Using electromagnetic model simulations, and ancillary data for describing the physical parameters of the ice sheet, the observed variability and features in SMOS data are reproduced and explained.

The Soil Moisture Active Passive (SMAP) radiometer operates in the L-band protected spectrum (1400-1427 MHz) that is known to be vulnerable to radio-frequency interference (RFI). Although transmissions are forbidden at these frequencies by international regulations, ground-based, airborne, and spaceborne radiometric observations show substantial evidence of out-of-band emissions from neighboring transmitters and possibly illegally operating emitters. The spectral environment that SMAP faces includes not only occasional large levels of RFI but also significant amounts of low-level RFI equivalent to a brightness temperature of 0.1-10 K at the radiometer output. This low-level interference would be enough to jeopardize the success of a mission without an aggressive mitigation solution, including special flight hardware and ground software with capabilities of RFI detection and removal. SMAP takes a multidomain approach to RFI mitigation by utilizing an innovative onboard digital detector back end with digital signal processing algorithms to characterize the time, frequency, polarization, and statistical properties of the received signals. Almost 1000 times more measurements than what is conventionally necessary are collected to enable the ground processing algorithm to detect and remove harmful interference. Multiple RFI detectors are run on the ground, and their outputs are combined for maximum likelihood of detection to remove the RFI within a footprint. The capabilities of the hardware and software systems are successfully demonstrated using test data collected with a SMAP radiometer engineering test unit.

The SMAP RFI detecting digital backend performance is characterized using real-environment L-band RFI data from the SMAPVEX 2012 campaign. Various types of RFI signals are extracted from the airborne campaign dataset and fed to the SMAP radiometer using an Arbitrary Waveform Generator (AWG). The backend detection performance is tested, and missed-detections are further investigated. Initial results indicate RFI detection performance for the SMAP digital backend is acceptable.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature observations of the Earth since its launch in November 2009. Radio frequency interference (RFI) is clearly present in SMOS data, and RFI detection and mitigation are a challenging problem. Furthermore, the interferometric nature of SMOS observations can cause RFI artifacts in SMOS measurements. This letter reports an analysis of the characteristics of SMOS RFI in North America, including a study of RFI artifacts and a method for their removal. Polarimetric properties and statistics of the resulting observations after artifact removal are also examined as an initial step in characterizing the “true” RFI sources observed in North America.

Measurements of both the Soil Moisture and Ocean Salinity (SMOS) and Aquarius L-band microwave radiometers show a significant presence of radio frequency interference (RFI), although they operate in a protected frequency band where transmission is prohibited. RFI detection and mitigation remain a challenging problem for both missions, especially for low or moderate (i.e., on the order of 10 K or less) amplitude contributions. An algorithm for low-level source detection and mitigation is already included in Aquarius data sets, and both Aquarius and SMOS have distinct attributes that can potentially enable further improvements in detection and mitigation of these sources to some degree. The combination of SMOS and Aquarius data sets may enable further future improvements as well. Initial efforts toward this goal are reported in this paper. Similarities and differences in RFI effects on SMOS and Aquarius are examined, with a particular focus on instrument properties that cause differences in received RFI power in SMOS and Aquarius observations of a specific source. A study is also performed of SMOS observations for regions reported by Aquarius to contain “low-level” RFI. It is shown that the detection of these sources in the SMOS data set is challenging and that the dependence of the SMOS third and fourth Stokes parameters on incidence angle makes the polarimetric features of SMOS difficult to utilize for low-level source detection. However, an angular fitting procedure suggested previously in the literature can, in some cases, detect such sources in horizontal and vertical polarizations.

ESA's SMOS mission has been providing L-band brightness temperature observations of the Earth since launch in Nov. 2009. Radio frequency interference (RFI) is clearly present in SMOS data, and RFI detection and mitigation remains a challenging problem. The interferometric nature of SMOS observations also causes some RFI artifacts in SMOS measurements that do not reflect the actual brightness temperature values. This paper reports on an analysis of SMOS RFI in North America, including a study of RFI artifacts and a method for their removal. Properties of the remaining RFI sources are then examined after artifact removal.