Radio Frequency Interference (RFI) is a serious threat to microwave remote sensing. In some cases, signals generated to intentionally disrupt the operation of other systems can also affect measurements by remote sensing instruments. This paper examines the case of jamming of Global Navigation Satellite Systems (GNSS) and its effect on GNSS-Reflectometry and microwave radiometry.

The SMAP microwave radiometer's antenna temperature (TA) is computed from the radiometer outputs using a conventional two-point calibration method. The receiver's gain and noise temperature are calibrated using the internal reference load and internal noise diode. Then the TA is derived. To provide an alternative to the current internal calibration approach for the SMAP L-band microwave radiometer and to determine the potential for reducing hardware for and cost of future projects, one-point calibration is studied. In this work, the internal reference load is used as the single source for one-point calibration, and the receiver noise temperature is modeled. The performance of the one-point calibration is validated by comparing the calibrated TAs, their uncertainties, and long-term calibration drifts to those of the conventional two-point calibration. Comparison results show that one-point calibration can achieve comparable performance, i.e., no larger than 0.07 K rms difference in calibrated TA, to the conventional two-point calibration. In addition, the long-term stability of the internal noise source is also included in the analysis.

The paper presents a commercially available, low-power digital spectrometer Application Specific Integrated Circuit (ASIC). We describe the ASIC architecture, its implementation aspects as well as board level spectrometer solutions. ASIC application examples as well as testing data are also presented. Data show adequate performance parameters for spectroradiometer applications at exceptionally low Size, Weight and Power (SWaP), compared to solutions based on off-the-shelf components. The ASIC development was funded through several NASA Small Business Innovative Research (SBIR) program awards.

Radio Frequency Interference (RFI) has long been a problem for L-band microwave radiometers, such as SMOS, Aquarius and SMAP. This paper reports on the activities performed by the SMAP RFI team to identify and report persistent sources with the aim of decreasing global RFI occurrences at L-band.

This work introduces the ongoing initiatives by the "RFI in Remote Sensing Working Group" within the IEEE Standards Association. The Working Group is taking the lead in standardizing the evaluation of radio frequency interference (RFI) impact on spaceborne microwave remote sensing. This standardization effort aims to enhance the monitoring of RFI and improve the effectiveness of sharing information related to it. It is developing a standard titled "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment". This paper presents these efforts and highlights the recent activities undertaken by this working group.

There is growing recognition of the potential for hyperspectral microwave sensors to improve the vertical resolution of temperature and humidity profiles, especially near Earth's surface, i.e., the planetary boundary layer. In response to the need for broadband, fine-spectral resolution data, NASA has funded the development of a low-power digital spectrometer Application Specific Integrated Circuit (ASIC) through NASA Small Business Innovative Research (SBIR) program awards to Pacific MicroChip Corporation (PMCC.) The outcome of these investments is a commercially available digital spectrometer ASIC that includes a 6-bit, time interleaved analog-to-digital converter (ADC) with a sampling rate up to 8 GSPS. The radiation tolerant, highly-configurable digital spectrometer ASIC can digitize and process signals up to 4 GHz bandwidth with up to 8192 frequency bins while drawing less than 1.75 W of power. To meet a broad range of system requirements, the digital spectrometer ASIC includes a programmable gain amplifier, a 16 GHz Phase Locked Loop (PLL) based frequency synthesizer, a demultiplexer, a poly-phase filter bank, a programmable windowing function, a Fast-Fourier-Transform (FFT) core, a frequency-domain data analysis block, a programmable time (2 µs to 34 s) accumulator of frequency-domain voltage or power, a data readout block, a Serial Peripheral Interface (SPI) for the ASIC's programming and low-speed data interchange, a low-voltage differential signal (LVDS) interface for high-speed data transfer, a digital control unit and built-in ASIC testing features.

Even when radio frequency interference (RFI) is detected, very little is known about the sources of the interference. More information about the sources would facilitate the design of systems to deal with the interference. Reporting of interference by the RFI teams for SMAP and SMOS through international channels has resulted in a decrease in RFI and identification of several sources. Two such cases that have been identified in the USA and are reported here.

In this article, we present a comprehensive sensitivity analysis and geophysical retrieval product demonstration to assess the enhanced information content in atmospheric temperature and water vapor, harnessed in hyperspectral microwave measurements. A particular focus of this study is devoted to quantifying and comparing the impact on retrieval performance resulting from novel spectral bands of the microwave thermal spectrum, by means of data addition and data denial trade studies. Various spectral configurations are assessed, each reflecting specific technology solutions intended to maximize geophysical product performance within feasible size, weight, power, and cost constraints. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to $\sim$40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the window regions, leading to an overall improvement of up to $\sim$50% in the Earth's planetary boundary layer thermodynamic fields. This work concludes with an overview on the state of the art in hyperspectral microwave technology and a discussion on future applications of interest to numerical weather prediction and climate science. The work presented in this study focuses on ocean, clear-sky demonstrations. All-sky, all-surface investigations will be the focus of a follow-up study, as we advance our capability to simulate more complex scenarios and improve scene variability.

We present a comprehensive Earth Planetary Boundary Layer temperature and water vapor retrieval improvement demonstration by the use of hyperspectral microwave measurements. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to 40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the so called spectral window regions, leading to an overall PBL temperature and water vapor improvement of up to 50%.

This paper presents an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a 2021 NASA Instrument Incubation Proposal funded project aimed at developing the very first hyperspectral microwave sensor to augment thermodynamic sounding capability from space, with a focus on the Earth's Planetary Boundary Layer. This research responds to the recommendation expressed in the 2018 National Academies of Sciences decadal survey to accelerate the readiness of high-priority PBL observables not feasible for cost-effective spaceflight in 2017–2027. This paper provides an overview on HyMPI's design, configured as the objective instrument concept needed to fly in the future PBL mission and presents preliminary trade studies aim at demonstrating HyMPI's enhanced thermodynamic sounding skill in the Earth's Planetary Boundary Layer over conventional microwave sounders from the current Program of Record.

We present an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a NASA Instrument Incubation Proposal funded research project aimed at developing a hyperspectral microwave instrument intended for enhanced remote sensing of atmospheric temperature and water vapor from space. This paper provides preliminary results on HyMPI's spectral and noise characteristics and a preliminary demonstration of its enhanced water vapor sensitivity and vertical resolution, with a particular focus on the Earth's Planetary Boundary Layer.

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 soil moisture active/passive (SMAP) satellite microwave radiometer has been providing global measurements of L-band thermal emission from Earth since April 2015. Although the radiometer operates in the protected 1400-1427 MHz portion of the radio spectrum, its measurements are still corrupted by either radio frequency interference (RFI) from out-of-band emissions via legal sources or by sources operating in-band illegally. The SMAP radiometer includes a digital backend that enables implementation of multiple ground-based RFI detection and filtering algorithms. This data is used to collect statistics and trends of Earth's RFI environment. This article examines properties of the global RFI environment as observed by SMAP, including information on RFI source properties (obtained from analysis of SMAP multiple detector outputs) and the evolution of the RFI environment in time. Residual RFI contributions after the application of SMAP RFI processing are also examined as preliminary information for the development of future methods to address their effect.

The Soil Moisture Active Passive (SMAP) mission was launched on 31st January 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.

Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity.The Soil Moisture Active Passive (SMAP) mission has an aggressive approach to RFI detection and filtering using dedicated spaceflight hardware and ground processing software.As more sensors push to observe at larger bandwidths in unprotected or shared spectrum, RFI detection continues to be essential.This article presents a deep learning approach to RFI detection using SMAP spectrogram data as input images.The study utilizes the benefits of transfer learning to evaluate the viability of this method for RFI detection in microwave radiometers.The well-known pretrained convolutional neural networks, AlexNet, GoogleNet, and ResNet-101 were investigated.ResNet-101 provided the highest accuracy with respect to validation data (99%), while AlexNet exhibited the highest agreement with SMAP detection (92%).

The soil moisture active passive (SMAP) microwave radiometer is a fully-polarimetric L -band radiometer flown on the SMAP satellite in a 6 AM /6 PM sun-synchronous orbit at 685-km altitude. After the SMAP L1B_TB data product version 4 was released in 2018, the radiometer has undergone further calibration and validation. The goal is to reduce the difference between antenna temperature of ascending and descending orbits during the eclipse, and to reduce the dips in the calibration drift over the cold sky (CS) during the eclipse seasons in 2017 and 2018. The postlaunch calibration algorithm has been revisited by retrieving all of the calibration parameters simultaneously with two different options for the hot calibration source (the global ocean, or the radiometer internal reference load). The performance of the two options are compared here. The option with the radiometer internal reference load has been chosen by the SMAP science team for data release version 5. In addition, a correction offset is applied to the input signal to account for offsets during the early-mission stages with the SMAP synthetic aperture radar transmitter operating alongside the radiometer.

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.

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.

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K-and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at -5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate.

A proof-of-concept experiment has demonstrated that wideband (400 MHz) signals of opportunity (SoOp) transmitted in K- and Ku-bands from geostationary satellites can be used for coastal altimetry. An essential finding from this experiment is that the full broadcast spectrum consisting of multiple digital channels can be processed as a single wideband signal source. An established error model for Global Navigation Satellite System interferometric altimetry was shown to accurately represent the sea surface height (SSH) retrievals when evaluated using the full bandwidth. This experiment was conducted over a 72-h period at Platform Harvest off the Pacific Coast. Colocated tide gauge and LiDAR measurements were used as in situ data. Two anomalies were observed in the experiment: 1) multiple peaks in the cross correlation waveform from one polarization of Ku-band frequency and 2) decrease in signal-to-noise ratio from loss of a data channel. When the instances of multiple peaks were eliminated and the equivalent bandwidth recomputed using only the active channels, SSH error from these cases agreed well with the model prediction. Application of SoOp wideband altimetry will, therefore, require a monitoring capability to identify changes in the transmission spectrum, total power, and waveform shape, for quality control and setting an appropriate observation error covariance. Measurement precision from a satellite receiver is predicted to be between 4 and 6 cm using the error model. SoOp altimetry with these signals may improve coastal measurements and increase the sampling and revisit rate through the use of a constellation of small satellites.