We present a concept for UHF/L-band (0.5–2 GHz) remote sensing of Antarctic ice sheet internal temperature using a highly sparse synthetic aperture radiometer constellation. This concept leverages the relative stability of ice sheet thermal emission over long temporal periods to gradually assemble a collection of array baselines which are jointly transformed to develop large image facets. We formulate a calculation of minimum array complexity based on the desired sensitivity, spatial resolution, and time available for observations. We determine from this calculation that such a system can achieve 1–10-km spatial resolution (significantly finer than the program of record) over monthly to yearly timescales with as few as 10–20 elements; even fewer elements are required for observing only the ice sheet center. The inverse problem of reconstructing image facets from mixed-pointing and mixed-configuration observations is posed using a Fourier domain data constraint with a total variational regularization in the image domain. This approach enables image formation from heterogeneous observations while mitigating artifacts. We present a notional constellation design for three satellites which could accomplish the necessary baseline sampling by rotating the phase and semimajor axis of spacecraft relative positions in planar circular orbits (PCOs). We demonstrate image formation by observing system simulations leveraging predictions of Antarctica’s multiwavelength brightness temperature computed from ice sheet thermomechanical and radiative transfer models.

The EZIE mission is a first of its kind to measure the temporal and spatial characteristics of Earth’s ionospheric auroral electrojet currents remotely using a mm-wave radiometer called the Microwave Electrojet Magnetogram (MEM). EZIE measures the 118 GHz oxygen emission line that splits in frequency in the presence of a magnetic field. This effect is known as the Zeeman effect. From these measurements of the 50 km spatial resolution magnetic fields at 80 km altitude the ionospheric currents that caused them can be calculated. The EZIE mission consists of three MEM payloads on three spacecrafts, each MEM contains four polarimetric radiometer receivers with a polyphase filter bank spectrometer. MEM can indirectly measure magnetic field strength and direction. In this paper we present the unique calibration design of the MEM payload that does not include any internal or external calibration sources. We discuss the MEM payload and present results from pre-launch testing and calibration of the MEM system.

We present the STASIS concept, an innovative approach to developing high spatial resolution maps of Antarctic ice sheet thermal emission at P/L band. Rather than using a large real aperture system, the relative stability of ice sheet temperature over time implies that a sparse array system would be able to gradually build up spatial frequency sampling and generate images with 1K sensitivity at 1-10 km spatial resolution over monthly-seasonal time scales. This contrasts with the requirement for full snapshot spatial frequency coverage required by systems for monitoring soil moisture and ocean salinity Sensitivity heuristic calculations are presented, and simulated interferometric observations are generated incorporating a realistic ice sheet thermal emission model.

Venus holds the key to understanding rocky planet evolution in our solar system and beyond. As Earth’s "twin" in terms of size, mass and density and sitting within the habitable zone one might expect that the two planets would evolve very similarly. This expectation is quite erroneous with Venus surface and atmospheric conditions being very different than the Earth. To understand how Venus evolved so differently than the Earth ESA and NASA are sending three missions, EnVision, VERITAS and DaVinci to Venus in the 2030s. ESA’s EnVision mission has a synthetic aperture radar, VenSAR, provided by NASA designed to peer beneath the optically opaque atmosphere and provide high resolution imagery and topography of the surface. Here we describe the expected performance of the VenSAR instrument for its various modes of operation.

We discuss an approach to measuring high-resolution maps of Antarctic ice sheet temperatures using repeat-pass sparsely sampled microwave interferometry. This approach follows from the inference that the relative invariance of ice sheet temperatures on annual timescales obviates the need for high snapshot sensitivity imposed as a requirement for observing more variable regions of the Earth system with interferometers such as SMOS. Such measurements could hypothetically be conducted with spatial resolutions less than 10 km using a small constellation of satellites. We discuss specifically how modifications to sheet-base geothermal heat flux could manifest as observable thermal signatures and a strategy to form images from a mosaic of multiple heterogeneous sparsely sampled observations, and we conclude with comments on necessary areas for future investigations.

We present the Forward Ocean Atmosphere Microwave (FOAM) radiative transfer model, a Python package designed to simulate passive microwave observations of ocean state, and discuss its basic architecture. FOAM is particularly useful for exploring concept architectures for future missions measuring ocean salinity from space. We discuss how FOAM can be used to design missions meeting a range of accuracy and revisit requirements, and we consider an example of ocean salinity retrieval with a wideband radiometer system.

The Soil Moisture Active/Passive Mission was launched in 2015 to provide estimates of global surface soil moisture from its L-Band radiometer measurements. The digital backend included in SMAP's radiometer enables radio frequency interference (RFI) to be detected and filtered in real time. The six-year record of SMAP's RFI data available now allows global monitoring of the RFI environment and its changes over time. An automatic tool has been developed for this purpose that generates a table listing the most persistent and strongest sources. This paper provides an analysis of these tables to examine the evolution of the RFI environment over time. The use of the tables generated for reporting RFI sources to national authorities is also discussed.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

For understanding englacial hydrology and its impact on ice sheet mass balance, observations of the liquid water content (LWC) within the ice sheets are needed. Earlier studies have shown the complementary nature of multi-frequency microwave radiometer measurements to detect subsurface LWC distribution in addition to surface LWC, which is critical for understanding the seasonal melt dynamics of ice sheets. In this study, we used 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite, and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite to investigate the multi-frequency response at pan-Greenland scale. The melt indications derived at different frequencies show trends consistent with persistent seasonal subsurface melt water and delayed subsurface refreezing of the seasonal melt water. The result suggests that the seasonal subsurface persistent melt water occurrences that are not captured by the high-frequency retrievals are both temporally and spatially very significant.

The ultra-wideband spectroradiometer instrument aims at measuring the brightness temperature gradient in the upper lunar regolith using a wideband passive microwave spectrometer covering a continuous band from 300 MHz to 6 GHz. As a part of the Development and Advancement of Lunar Instrumentation (DALI) program, the designed ultra-wideband spectrometer is expected to provide lunar heat flux measurements. Difficulty in RF matching across the ultra-wideband and lack of isolators covering the large bandwidth make it challenging to design and calibrate the instrument. The heat-flow spectroradiometer instruments employ internal calibration sources for tracking and detecting mismatch changes in addition to gain variations measurements for stable and reliable radiometric operation.

The Electrojet Zeeman Imaging Explorer (EZIE) is an innovative multi-satellite mission that images the magnetic fingerprint of intense electrical currents flowing in the upper layers of Earth's atmosphere. EZIE's multi-point measurements of these electrojets will provide closure to decades-old, and much debated, mysteries of the interaction between the Earth and the surrounding space. Each of EZIE's three satellites carries a microwave electrojet magnetogram (MEM) instrument which consists of four identical 118-GHz heterodyne spectropolarimeters. They are designed and optimized to cost-effectively meet EZIE's measurement requirements. EZIE's MEM instruments use the Zeeman effect to infer magnetic fields at ~80 km altitude. The technique has been used extensively to derive the Sun's magnetic field. EZIE now applies this technique to the Earth system.

Over the years, a fixed window for smoothing radiometer cold-space and warm-load counts and processing brightness temperature in calibration has been used for all microwave sounders at EUMETSAT and NOAA. Although this practice is based on ground tests and legacy satellites, it remains unclear if this empirical parameter is optimal for in-orbit radiometers, as the space environment is different from the ground and radiometers may drift. We found that the fixed window is not optimal and leads to large noise.We have developed an adaptive window that accommodates channel differences and temporal changes in hardware. Our method has reduced noise by as much as 50% for 183 GHz channels of MetOp-C MHS. We observed temporal jumps and shifts in counts, gain and noise of 89 and 190 GHz, and accordingly, the adaptive window can adjust to reduce such an impact. Further analyses reveal that 1/fnoise plays an important role for determining the adaptive window. 1/fnoise is non-stationary and gives rise to the fluctuation of counts and gain. As a result, for channels with large 1/fnoise a short window should be used to mitigate the fluctuation. Our study suggests an adaptive method has advantages over the fixed method for considering channel differences and timevarying noise.

Striping has been observed in the MetOp-A microwave humidity sounder (MHS) data since its degradation in November 2018. However, accurate striping quantification and characterization remain challenging due to the large scene dynamics observed at W-/G-bands of MHS. Here, we have developed a set of novel algorithms for striping quantification, decomposition, characterization, and simulation. Our algorithm extracts striping from the warm-load and cold-space scenes that are relatively stable. We break down the striping into two parts of thermal and$1/f$noises, and quantify their absolute magnitude and relative ratio. We found a significant increase in striping at 157 GHz, which has more than quadrupled by October 2019 relative to its normal level. Regardless of the degradation, the ratio of thermal and$1/f$noises remains the same. Our simulation reproduces all the characteristics of striping against observation. It is shown that$1/f$noise generates sharp, nonperiodic stripes, while thermal noise also generates stripes but with smoother band features. The latter is due to the periodic calibration that has a chopping effect. The striping percentage, defined as the ratio of$1/f$to total noise, shows no dependence on the scene temperature. Striping is pronounced not only in 157 GHz but also in 89 and 190 GHz with the striping percentage over 50% while lower in 183 GHz of 20%. The results provide insights for quantifying and understanding striping. Our algorithm can be applied to other radiometers and to simulate striping for evaluating its impact on data assimilation and science products.

Wideband radiometers must contend with a challenging RFI environment and with frequency-dependent reflections between receiver components caused by impedance mismatches. Channelizing the bandwidth prior to amplification can prevent strong interference in one portion of bandwidth from corrupting all of the data and can allow for the incorporation of isolators to reduce reflection effects, but the additional components required cause additional losses, reducing the sensitivity of the radiometer. Avoiding channelization in the radiometer front end reduces these losses, but increases the potential impact of reflections and high amplitude RFI sources. This paper investigates tradeoffs between “channel-ized” and “non-channelized” architectures for the proposed PolarRad 0.5-2 GHz microwave radiometer.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for April 2022 and the other one for July 2022. The IOPs will entail a deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

The Soil Moisture Active Passive (SMAP) mission was launched on 31stJanuary 2015 in a 6 AM/ 6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. The passive instrument of SMAP is a fully polarimetric L-band radiometer (1.4GHz) operating with a bandwidth of 24MHz. The radiometer uses a combination of noise-diodes and Dicke-loads for internal calibration with a design similar to that used by the Aquarius or Jason series radiometers [2], [3]. Pre-launch calibration activities had been performed since 2012 on the engineering model of the radiometer. Post-launch calibration activities have been performed to fine-tune and validate the results from the pre-launch calibration. The major calibration activities and lessons learned in the past 8 years will be described in the following sessions.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2021 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in April 2021, and a second one in July 2021. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

The SMAP L-band microwave radiometer is in its extended mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cycles. Instrument behavior has been stable over the past 4 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) were released to the public in 2018 for various science activities. Now the radiometer data are under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are investigated to obtain the optimal solution. In addition, the correction to the radiometer measurement when the SMAP radar transmitter was operational will also be revisited for the next data release. The performances of the calibration revisit and Radio-Frequency Interference (RFI) trends will be presented as well.

Surface roughness parameterization plays an important role in passive microwave soil moisture (SM) retrieval. This article proposes a new formulation for estimating surface roughness. The proposed model incorporates the field-scale (micro) roughness as well as topographic (macro) roughness. The performance of the model is evaluated by inverting the traditional tau-omega model for retrieving SM. The study focuses on the passive active L-band system (PALS) radiometer data collected as a part of two Soil Moisture Active Passive Validation Experiment (SMAPVEX), i.e., SMAPVEX12 (humid Manitoba, Canada) and SMAPVEX15 (semiarid Arizona, USA) with highly different microroughness and macroroughness. The measured surface roughness is observed to increase exponentially with clay fraction (CF). This behavior is minimized with increase in leaf area index (LAI). In the absence of vegetation, the contribution of topography toward surface roughness increases. A higher surface roughness value is estimated for SMAPVEX12, which positively correlate with LAI and CF and negatively correlate with wetness conditions. On the other hand, due to the high topographic variability in SMAPVEX15 region, the contribution of topography (surface curvature) toward total surface roughness is significant. Also, consistently dry SM resulted in high microroughness for SMAPVEX15. Nevertheless, a total surface roughness estimated for SMAPVEX15 region is less than for SMAPVEX12. The surface roughness formulation presented in this study can be extrapolated to any spatial resolution.

A new wideband radiometer covering P/L-band was developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The instrument was deployed on the US Coast Guard Cutter Healy for an Arctic Ocean research cruise from September 13, 2018 to October 20, 2018. This work shows the first results relating sea ice thickness obtained from the measurements taken with the wideband P/L-band radiometer during the campaign. Results from the Artic cruise campaign were also used to study wideband spectral properties of salinity. In addition to this paper, Salinity and wide-band calibration challenges are presented in two other companion papers.

A new ultra-wide P/L-band radiometer instrument has been developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The Arctic field campaign performed with the instrument deployed on the US Coast Guard Cutter Healy from September 13, 2018 to October 20, 2018. A new calibration strategy is developed for the ultra-wide band instrument to minimize the mismatch related effects. A noise-wave model is built to analyze and validate the instrument behavior for the calibration. The calibration strategy is analyzed using the results from the cruise campaign. Sea-ice thickness and sea surface salinity are presented in two other companion papers.

The SMAP L-band microwave radiometer has completed its 3-year primary mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cyclces. Instrument behavior is stable over the past 3 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) was released to the public in 2018 for various science activities. Now the radiometer is under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are being used to obtain the optimal solution. In addition, the correction to the impact on the radiometer measurement when the SMAP radar transmitter was on will also be revisited for next data release.

Global Navigation Satellite System - Reflectometry (GNSSR) techniques have proven successful to retrieve several geophysical parameters such as, ocean wind speed, soil moisture, altimetry, wetland dynamics, snow depth estimations. In this paper, the L2C GPS signals measured from the Soil Moisture Active Passive (SMAP) radar after its malfunction is used to investigate the effect of the vegetation water content on the electromagnetic signal. The SMAP-Reflectometry (SMAP-R) measurements are obtained at V and H polarizations allowing for not only signal-to-noise ratio (SNR) analysis but also for polarimetric ratio (PR) studies.

The demand for microwave spectrum for commercial and industrial use has been increasing rapidly over the last decade, putting stress on the limited spectral resources for passive microwave remote sensing. Radio frequency interference from man-made sources is expected to become worse over the coming years. At 1.4 GHz, the SMAP mission has implemented and demonstrated advanced interference detection algorithms for its microwave radiometer. This scheme will not be feasible at higher microwave frequencies (above 6 GHz) due to much larger radiometer bandwidths used and the limited downlink data volume available to implement RFI filtering algorithms in the ground processing. In this paper, we present the design, development, and test of an advanced on-board interference detection and RFI filtering digital back-end that is capable of operation for a 1 GHz-radiometer bandwidth. We describe the combined RFI detection algorithms implemented in the digital backend's firmware and the on-board RFI filtering of interference-corrupted data that will be necessary to limit downlink rate requirements for future high-frequency microwave missions.

Juno is a New Frontiers mission to study Jupiter and carries as one of its payloads a six-frequency microwave radiometer to perform atmospheric sounding of the Jovian atmosphere to pressures of approximately 250 bars [1]. Juno was launched from Kennedy Space Center on August 5, 2011 and reached Jupiter orbit on July 4, 2016. The Microwave Radiometer (MWR) operates from 600 MHz to 22 GHz and was designed and built at the Jet Propulsion Laboratory. Because the mission is operating in the harsh Jovian radiation environment, it required an ambitious radiometer design that pushed the limits for an internally calibrated radiometer. The MWR uses noise diodes and a PIN-diode Dicke switch located inside the receiver. Typically, internally calibrated radiometers are designed to minimize the loss and the temperature gradient between the antenna and the noise calibration sources. However, for the MWR, the receivers with the calibration sources needed to reside inside a centralized radiation vault and the large antennas were up to 1.5 m away on the outward facing side of the spacecraft. Producing calibrated antenna temperatures requires a correction for up to 3 dB of front-end loss with a 150 K temperature gradient. In other words, 50% of the received signal at the internal calibration plane originates in the front-end of the radiometer. Additionally, the MWR is operating on a spacecraft rotating through the strong Jovian magnetic field, requiring targeted magnetic shielding of the ferrite isolator inside the receiver.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and filtering technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel and will demonstrate on-board real-time RFI processing. The system is currently under development, with an expected launch date in mid-2018 followed by a one year period of on-orbit operations. CubeRRT spacecraft and radiometer instrument testing as well as the mission concept of operations are described in this paper.

In this paper we discuss the necessary technology required to enable the future of spectrum resource constrained missions. We discuss the CubeSat Radiometer Radio Frequency Interference Technology (CubeRRT) validation mission and the development of its digital backend, necessary for performing on-board RFI detection and filtering for wideband high frequency radiometry. The CubeRRT mission will validate the on-board RFI filtering technology solving technological challenges such as bandwidth, data downlink volume, and RFI types. We present a few initial results of the backend spectrometer leading to full-system integration and test.

The SMAP microwave radiometer will see its third anniversary of operations on March 31, 2018. Instrument behavior is stable over 33 months of operation to date. The physical temperature of the internal calibration sources varies 0.5°C. The bias current of the noise source drifted by less than 0.1%. The avalanche breakdown voltage of the noise diode shows 0.01% seasonal variation. The average NEDT of the radiometer has maintained a stable 1-K value over the period. This stable behavior of the hardware is critical for the consistent calibration. The reflector emissivity was re-estimated using on-orbit data. Use of the new value nearly eliminates bias caused by solar eclipse during the southern hemisphere winter. The radiometer data were recalibrated using, as earlier, global ocean and cold sky views with additional ocean and land views at nadir incidence. The Version 4 recalibrated data exhibit 0.1-K RMS stability over average global ocean and monthly cold-sky views.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and filtering technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel and will demonstrate on-board real-time RFIS processing. The system is currently under development, with an expected launch date in mid-2018 followed by a one year period of on-orbit operations. Development of the CubeRRT spacecraft, radiometer instrument, and concepts of operation are described in this paper.

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TBproduct at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution TBat all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of TBfrom Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP TBat 1km. High resolution soil moisture is subsequently derived from high resolution TBusing inverse models.

NASA's SMAP (Soil Moisture Active Passive) calibration and validation program revealed that the soil moisture products are experiencing difficulties in meeting the mission requirements in certain agricultural areas. Therefore, the mission organized airborne field experiments at two core validation sites to investigate these anomalies. The SMAP Validation Experiment 2016 included airborne observations with the PALS (Passive Active L-band Sensor) instrument and intensive ground sampling. The goal of the PALS measurements are to investigate the soil moisture retrieval algorithm formulation and parameterization under the varying (spatially and temporally) conditions of the agricultural domains and to obtain high resolution soil moisture maps within the SMAP pixels. In this paper the soil moisture retrieval using the PALS brightness temperature measurement in SMAPVEX16 is discussed in relation to in situ and SMAP soil moisture.

The advanced microwave radiometer (AMR) is a critical payload on the recently launched Jason-3 mission, designed to provide the electrical range delay of the radar altimeter signal due to tropospheric water vapor and cloud liquid water [1]. The errors in the wet tropospheric path delay measurements have a direct impact on the record of global mean sea level (GMSL) and could lead to uncertainty in derived trends if spurious drifts in the radiometer are not accounted for. Therefore, it is imperative to quantify and correct radiometer calibration drift, enabling producing of a high-quality stable record of wet tropospheric path delay for use in the development of GMSL.

In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (TB) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on TBfor corn and soybean using ground-based TBobservations. The modeled TBwill be upscaled based upon the USDA crop layer map to compare with the TBobserved in the coarse scales.

In this paper we discuss a polarimateric calibration technique applied on the Soil Moisture Active Passive (SMAP) L-band radiometer. We take advantage of the SMAP antenna rotation and varying incidence angle during pitch maneuvers performed by the spacecraft for periodic cold-sky calibration. We present initial comparisons between the polarization corrected ocean signal at various incidence angles and the expected polarization signal. The difference between the two signals is utilized to back-out cross-polarization coupling parameters. We also discuss factors such as Faraday rotation that impact cross-polarization calibration at low L-band frequencies.

In this paper we discuss the design and development of the Radiometer Digital Backend (RDB) of the CubeSat Radiometer Radio Frequency Interference Technology (CubeRRT) validation mission. We present a brief introduction of the mission and the Radio Frequency Interference (RFI) detection and mitigation algorithm. The digital backend developed for the CubeSat is presented. The digital backend has unique capabilities of taking in wide bandwidths of up to 1GHz and can perform on-board complex operations to detect and filter out RFI in real-time. We present a few initial results of the backend spectrometer leading to full-system integration and test.

The SMOS, and SMAP retrievals are not only influenced by soil moisture, vegetation but also highly sensitive to surface roughness. The current retrieval algorithms use roughness models and parameterization developed at field scale to estimate soil moisture at satellite footprint. These models ignored large scale roughness features due to differences in elevation, concavity etc., observed within the satellite footprint. In this study, it is hypothesized that the satellite observed surface roughness is contributed by micro-roughness (small-scale roughness) and macro-roughness (large-scale roughness), incorporating the scattering features observed at field and satellite scale. The traditional algorithm is used to retrieve soil moisture for SMAPVEX12 (Soil Moisture Active Passive Validation Experiment, 2012), and SMAPVEX15 the field experiments.

The Soil Moisture Active Passive (SMAP) mission was launched on 31stJanuary 2015 in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. The passive instrument of SMAP is a fully polarimetric L-band radiometer (1.4GHz) operating with a bandwidth of 24MHz. The radiometer uses a combination of noise-diodes and Dicke-loads for internal calibration with a design similar to that used by the Aquarius or Jason series radiometers [3]. The SMAP digital backend back-end enables implementation of advanced Radio Frequency Interference (RFI) detection and mitigation algorithms for corrupted L-band measurements [6]. The radiometer uncalibrated raw counts are converted to Level 1B antenna temperatures and brightness temperature (TB) values [2]. These TB values are used with other ancillary data to retrieve soil-moisture products on a 40km global grid. The error requirement for the SMAP radiometer is 1.3K and calibration drift is less than 0.4 K/month to measure soil-moisture with volumetric fraction uncertainty of less than 0.04 m3/m3.

This paper presents upgrades for the Passive Active L-band Sensor (PALS) microwave instrument. PALS is the primary calibration and validation airborne instrument for the Soil Moisture Active Passive (SMAP) mission. PALS has been successfully deployed in the field for more than a decade, increasing in size, weight and complexity. Currently, a state-of-the-art upgrade is being carried out to ensure the instrument is ready to meet current and future science requirements at the same time that is more portable and easy to deploy.

The Soil Moisture Active Passive (SMAP) mission provides a global surface soil moisture (SM) product at 36-km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications, there are also a lot of applications which would benefit from a higher resolution SM product. The SMAP radiometer-based SM product was downscaled to 1 km using Moderate Resolution Imaging Spectroradiometer (MODIS) data and validated against airborne data from the Passive Active L-band System instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP SM. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. SMAPVEX15 offers a unique data set for testing SM downscaling algorithms. The results indicated reasonable skill (root-mean-square difference of 0.053 m3/m3for 1-km resolution and 0.037 m3/m3for 3-km resolution) in resolving high-resolution SM features within the coarse-scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate, and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high-resolution SM product with an accuracy suitable for many applications.

All channels of the Aquarius radiometer were observed to have calibration instability consisting of a drift in the antenna temperature during the first couple of months of the mission and pseudo-periodic oscillations of the antenna temperature over the mission life. For the version 4 Aquarius processing, both of these anomalies were corrected by removing a time variable bias in the Aquarius measurements relative to a seven-day global average from a salinity model. In order to accurately track long-term variation of salinity on climate scales it is necessary to decouple Aquarius radiometric calibration from ocean salinity models. In this paper, a new technique is used to investigate the nature of anomalies using nonocean vicarious external sources such as Antarctic ice or Amazonian rain forests. Two completely different solutions are developed to correct the pseudo-periodic oscillations as well as the drift of the Aquarius radiometers, decoupling the Aquarius measurements from salinity model.

The Soil Moisture Active/Passive (SMAP) microwave radiometer is a fully polarimetric L-band radiometer flown on the SMAP satellite in a 6 a.m./6 p.m. sun-synchronous orbit at 685-km altitude. Since April 2015, the radiometer has been under calibration and validation to assess the quality of the radiometer L1B data product. Calibration methods, including the SMAP L1B TA2TB [from antenna temperature (TA) to the Earth's surface brightness temperature (TB)] algorithm and TA forward models, are outlined, and validation approaches for calibration stability/quality are described in this paper, including future work. Results show that the current radiometer L1B data product (version 3) satisfies its requirements (uncertainty <;1.3 K and calibration drift <;0.4 K/months, and geolocation uncertainty <;4 km) although there are biases in TA over cold sky and in TB comparing with the Soil Moisture and Ocean Salinity TB v620 data products.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and mitigation technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel, and will demonstrate on-board real-time RFI processing. The system is currently under development, with launch readiness expected in 2018 followed by a one year period of on-orbit operations. Project plans and status are reported in this paper.

In this paper we discuss the steps taken for the calibration and validation of the Soil Moisture Active Passive (SMAP) L-band radiometer. We discuss the use of multiple vicarious sources such as the global ocean mean and celestial cold-sky emissions along with various spacecraft maneuvers to calibrate out gain, offset, antenna pattern of the radiometer. We present initial validation comparison of SMAP brightness temperatures with other L-band missions.

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.

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.

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.

Radio frequency interference (RFI) detection techniques have different challenges and opportunities for interferometric radiometers such as the Microwave Imaging Radiometer using Aperture Synthesis on the Soil Moisture and Ocean Salinity (SMOS) mission. SMOS does not have highly oversampled temporal resolution or subband filters for oversampled spectral resolution, as do other radiometers with enhanced RFI detection capabilities. It does, however, have multisampled angular resolution in the sense that a single location is viewed from many different angles of incidence. This paper compares and contrasts RFI detection algorithms that use measurements made at a variety of different levels of SMOS signal processing, including the visibility domain, brightness temperature spatial domain, and brightness temperature angular domain. The angular domain detection algorithm, in particular, is developed and characterized in detail. Examples of the algorithms applied to cases with RFI (to assess detection skill) and without RFI (to assess false-alarm behavior) are considered.

Recent airborne field campaigns making passive microwave measurements have observed some radio frequency interference (RFI) that remained undetected by the kurtosis RFI-detection algorithm. The current pulsed-sinusoidal model for RFI does not explain this anomalous behavior of the detection algorithm. In this paper, a new RFI model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis algorithm with the new model is evaluated. The behavior of the kurtosis detection algorithm under central-limit conditions due to multiple sources is experimentally verified. The new RFI model offers a plausible explanation for the lack of detection by the kurtosis algorithm of the RFI otherwise observed.

Statistics of radio frequency interference (RFI) observed in the band 1398-1422 MHz during an airborne campaign in the United States are reported for use in analysis and forecasting of L-band RFI for microwave radiometry. The observations were conducted from September to October 2008, and included approximately 92 h of flight time, of which approximately 20 h of “transit” or dedicated RFI observing flights are used in compiling the statistics presented. The observations used include outbound and return flights from Colorado to Maryland, as well as RFI surveys over large cities. The Passive Active L-Band Sensor (PALS) radiometer of NASA Jet Propulsion Laboratory augmented by three dedicated RFI observing systems was used in these observations. The complete system as well as the associated RFI characterization approaches are described, along with the resulting RFI statistical information and examinations of specific RFI sources. The results show that RFI in the protected L-band spectrum is common over North America, although the resulting interference when extrapolated to satellite observations will appear as “low-level” corruption that will be difficult to detect for traditional radiometer systems.

The Radio frequency Interference Survey of Earth (RISE) is a new type of instrument used to survey and characterize the presence of Radio Frequency Interference (RFI) that can affect microwave radiometers. It consists of a combined microwave radiometer and kurtosis spectrometer with broad frequency coverage and high temporal and spectral resolution. A K-Band airborne version has been built and flown across southeast Michigan. A kurtosis detector is included in RISE to reliably detect the presence of RFI, even at very low levels, and to aid in its characterization. A radiometer is included to measure the impact of the RFI on observed brightness temperature.

A few of the issues faced by the kurtosis detection algorithm on recent field campaigns is discussed here. The performance of the kurtosis algorithm in detecting multiple-source Radio Frequency Interference (RFI) is characterized. A new RFI statistical model is presented in the paper to take into account the behavior of RFI sources under a large foot-print. Results indicate the behavior of the kurtosis ratio under central-limit conditions due to large number of RFI sources.

The kurtosis statistic is an effective detector of pulsed sinusoidal radio frequency interference (RFI) in a microwave radiometer, but it fails to detect RFI when the pulsed sinusoid is present for exactly half of the integration period. That is, the kurtosis is blind at an RFI duty cycle of 50%. In this letter, we explore the possibilities of using a higher order statistic to eliminate this detection blind spot in the kurtosis statistic and to improve RFI detection performance. The sixth-order statistic does have sensitivity at an RFI duty cycle of 50%, but sensitivity is relatively low. In addition, it has a sensitivity comparable with the kurtosis for short duty cycle RFI, under certain circumstances.

In support of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission, a number of soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out. The radiometer used in this context is fully polarimetric and has built-in radio-frequency-interference (RFI)-detection capabilities. Thus, the instrument, in addition to supplying L-band data to the geophysicists, also gave valuable information about the RFI environment. Campaigns were carried out in Australia and in a variety of European locations, resulting in the largest and most comprehensive data set available for assessing RFI at L-band. This paper introduces the radiometer system and how it detects RFI using the kurtosis method, reports on the percentage of data that are typically flagged as being corrupted by RFI, and gives a hint about geographical distribution. Also, examples of polarimetric signatures are given, and the possibility of detecting RFI using such data is discussed.

An inversion algorithm is developed to recover power and duty-cycle of incoming Radio Frequency Interference (RFI) signals from kurtosis. The algorithm applies simulated annealing on multiple kurtosis values obtained from different radiometer integration periods. The paper evaluates the performance of the inversion algorithm by performing Monte-Carlo simulations to obtain error statistics. The inversion capability of the algorithm and its robustness against the 50% duty-cycle blind-spot (generally present for the kurtosis detection algorithm) is demonstrated using experimental data.

Two algorithms used in microwave radiometry for radio-frequency interference (RFI) detection and mitigation are the pulse detection algorithm and the kurtosis detection algorithm. The relative performance of the algorithms is compared both analytically and empirically. Their probabilities of false alarm under RFI-free conditions and of detection when RFI is present are examined. The downlink data rate required to implement each algorithm in a spaceborne application is also considered. The kurtosis algorithm is compared to a pulse detection algorithm operating under optimal RFI detection conditions. The performance of both algorithms is also analyzed as a function of varying characteristics of the RFI. The RFI detection probabilities of both algorithms under varying subsampling conditions are compared and validated using data obtained from a field campaign. Implementation details, resource usage, and postprocessing requirements are also addressed for both algorithms.

The performance of two major Radio Frequency Interference (RFI) detection algorithms is compared. The peak detection algorithm and the kurtosis detection algorithm are characterized using the receiver operating characteristic (ROC) for pulsed sinusoid RFI. Downlink data bandwidth is one of the major design factors to be considered when comparing detection algorithms. Results are presented in this paper that compare the kurtosis algorithm performance with an ideally matched peak detection algorithm. The RFI parameters are also varied to analyze the overall detection performance of both algorithms. Factors such as implementation details, resource usage, post-processing and practicality are also addressed for both algorithms.

Analysis of detectability of the kurtosis algorithm for pulsed-sinusoidal radio frequency interference (RFI) has already been performed in detail. The detectability for wide-band spread-spectrum RFI is investigated here. A commercial RF communications product XBee is used for generating the spread-spectrum signal which is fed to the Agile Digital Detector (ADD) through a bench-top radiometer. ADD measures the probability distribution function of the incoming signal. The performance of the detection algorithm for spread-spectrum RFI is characterized and compared to pulsed-sinusoidal RFI. The sensitivity of the kurtosis algorithm with respect to the spectral properties of the wide-band signal is also investigated.

The kurtosis statistic is an effective detector of pulsed sinusoidal RFI in a microwave radiometer, but it fails to detect RFI when the pulsed sinusoid is present for exactly half of the integration period. That is, the kurtosis is blind at an RFI duty cycle of 50%. In this paper, we explore the possibilities of using a higher order statistic to eliminate this detection blind spot in the kurtosis statistic. The higher order statistic does have sensitivity at an RFI duty cycle of 50%, but sensitivity is relatively low.

A radio-frequency interference (RFI) detection algorithm has been developed for the Aquarius microwave radiometer. The algorithm compares individual brightness temperature samples with a local mean obtained from neighboring samples. If the sample under test significantly deviates from the local mean, then it is assumed to be corrupted by RFI. The algorithm has several adjustable parameters to optimize RFI detection. The performance of the algorithm has been characterized as a function of these parameters using a new form of RFI ldquoground truthrdquo that is based on the kurtosis of the amplitude distribution of the predetected voltages of a radiometer. Ground-based radiometric data obtained from the JPL-PALS campaign were used to assess the performance of the algorithm. False-alarm rates and the dependence of false alarms on worst case naturally occurring brightness temperature variations on orbit are determined as functions of the adjustable parameters of the algorithm.

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.

The performance of a previously developed algorithm for radio frequency interference (RFI) detection and mitigation is experimentally evaluated. Results obtained from CoSMOS, an airborne campaign using a fully polarimetric L-band radiometer are analyzed for this purpose. Data is collected using two separate integration times, as a result of which sensitivity of the detection algorithm is measured. The impact of RFI on remotely sensed data over land and sea is also presented.

Radio frequency interference (RFI) from anthropogenic sources in microwave radiometers detecting geophysical parameters is both common and insidious. As this RFI is always additive to the brightness, the presence of undetected RFI can bias the geophysical parameter retrieval. As radiometers have the most sensitive receivers operating in their band, low levels of RFI are both significant and difficult to identify. The kurtosis statistic is a tool being explored as a means of detecting low level RFI in microwave receivers. The performance of the kurtosis statistic as a detector of RFI is introduced.

An algorithm is developed to detect the presence of radio frequency interference (RFI) with the aquarius radiometer. The detection algorithm identifies individual samples of the antenna temperature that deviate significantly from the average value of nearby samples. The algorithm is tested and characterized using a new form of RFI "ground truth" that is based on measurements of the kurtosis of the amplitude distribution of the pre-detected signal. With ground truth available, adjustable parameters of the algorithm can be optimized.

A new type of microwave radiometer detector that is capable of identifying low-level pulsed radio frequency interference (RFI) has been developed. The Agile Digital Detector can discriminate between RFI and natural thermal emission signals by directly measuring other moments of the signal than the variance that is traditionally measured. The kurtosis is the ratio of the fourth central moment of the predetected voltage to the square of the second central moment. It can be an excellent indicator of the presence of RFI. A number of issues that are related to the proper calculation of the kurtosis are addressed. The mean and standard deviation of the kurtosis, in both the absence and the presence of pulsed sinusoidal RFI, are derived. The kurtosis is much more sensitive to short-pulsed RFI-such as from radars-than to continuous-wave RFI. The minimum detectable power for pulsed sinusoidal RFI is found to be proportional to (M3N)-1/4, whereNis the number of independent samples andMis the number of frequency subbands in the receiver.

A new type of microwave radiometer detector has been developed that is capable of identifying low level Radio Frequency Interference (RFI) and of reducing or eliminating its effect on the measured brightness temperature. The Agile Digital Detector (ADD) can discriminate between RFI and natural thermal emission signals by directly measuring higher order moments of the signal than the variance that is traditionally measured. After detection, the ADD then uses spectral and temporal filtering methods to selectively remove the RFI. ADD performance has been experimentally verified and its performance characterized while connected to an airborne C-Band radiometer (the NOAA/ETL PSR) installed on a NASA WB-57 flying over major urban centers. Index Terms—microwave radiometer, radio frequency interference

A new type of microwave radiometer detector has been developed that is capable of identifying high and low levels of radio-frequency interference (RFI) and of reducing or eliminating its effect on the measured brightness temperatures. High-level, localized RFI can be easily identified by its unnatural appearance in brightness temperature imagery. Low-level or persistent RFI can be much more difficult to identify and filter out. The agile digital detector (ADD) can discriminate between RFI and natural thermal emission signals by directly measuring higher order moments of the signal than the variance that is traditionally measured. After detection, the ADD then uses spectral filtering methods to selectively remove the RFI. ADD performance is experimentally verified in controlled laboratory tests and in the field near a commercial air traffic control radar. High-level RFI is easily identified and removed. Very low level RFI contamination, with power levels as low as the radiometric measurement uncertainty of the radiometer, is also shown to be reliably detected and removed.