GNSS reflection measurements in the form of delay-Doppler maps (DDM) can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under various conditions. The application of deep-learning-based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for the incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing the groundwork for a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data were aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2017–2019 which is generally comparable to existing global soil moisture products, and shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well. Comparisons with in-situ measurements demonstrate the correlation between the network predictions and ground truth with high temporal resolution.

Global navigation satellite system (GNSS) signals are critically important for a wide range of commercial, military, and science applications. Recent studies have identified threats to the performance of GNSS from both intended and unintended sources of radio frequency interference (RFI). Understanding the distribution of the sources of RFI and the nature of the signals that they are emitting is critical to determine and mitigate their effects on the measurements made by GNSS receivers. Terrestrial RFI can be substantially detrimental to the received GNSS signals, affecting the interpretation of related science measurements. NASA's Blackjack/TriG GNSS receivers are used for precise-orbit determination and radio occultation measurements, providing a data record spanning most of the earth's surface for nearly 20 years. We have developed a highly sensitive detection algorithm that uses variations in the measured signal-to-noise ratio (SNR), on the order of 10-50 s, common to all satellites to identify times and locations subject to RFI. Initial work has focused primarily on the detection of the presence of RFI and using the receiver's orbital solution to record the location of detection events. Our intermission analysis creates a unique record of global RFI with the potential for: 1) rigorous detection of the presence of interfering signals during science measurements; 2) geolocation of RFI sources; and 3) characterization of the nature of the transmitted signal to better identify intent. Preliminary analysis has shown that the presence of RFI is well correlated with regional conflicts and other geopolitical activities.

We report measurements of the turbulent evolution of the plasma density profile following the fast injection of lithium pellets into the Levitated Dipole Experiment (LDX) [Boxer et al., Nat. Phys. 6, 207 (2010)]. As the pellet passes through the plasma, it provides a significant internal particle source and allows investigation of density profile evolution, turbulent relaxation, and turbulent fluctuations. The total electron number within the dipole plasma torus increases by more than a factor of three, and the central density increases by more than a factor of five. During these large changes in density, the shape of the density profile is nearly “stationary” such that the gradient of the particle number within tubes of equal magnetic flux vanishes. In comparison to the usual case, when the particle source is neutral gas at the plasma edge, the internal source from the pellet causes the toroidal phase velocity of the fluctuations to reverse and changes the average particle flux at the plasma edge. An edge particle source creates an inward turbulent pinch, but an internal particle source increases the outward turbulent particle flux. Statistical properties of the turbulence are measured by multiple microwave interferometers and by an array of probes at the edge. The spatial structures of the largest amplitude modes have long radial and toroidal wavelengths. Estimates of the local and toroidally averaged turbulent particle flux show intermittency and a non-Gaussian probability distribution function. The measured fluctuations, both before and during pellet injection, have frequency and wavenumber dispersion consistent with theoretical expectations for interchange and entropy modes excited within a dipole plasma torus having warm electrons and cool ions.

Some existing auroral data products are insufficient for ionospheric simulation input on subkilometer spatial scales and high (second) time resolution near the boundaries of arc structures. Ideally, two‐dimensional data maps of the relevant parameters over these small scales would provide models with constraining inputs. Available in situ data have the time and spatial resolution for small‐scale features but only provide a 1‐D cut through the structure. Ground‐based data can provide 2‐D maps but have lower resolution in time and space than is required to accurately interpret the small‐scale structure near an arc. This paper provides a method to construct two‐dimensional maps of auroral parameters from the combination of one‐dimensional in situ data cuts with two‐dimensional ground‐based (and time dependent) camera imagery. Arc boundaries for each image are defined, and the available 1‐D ionospheric flow data are replicated into many 1‐D cuts at different points along the arc, yielding an irregularly sampled 2‐D flow map. These mapped data are fitted to a regular grid via a divergence minimization routine to generate a regularly sampled flow field that is enforced as divergence free. Comparison of the generated 2‐D data maps to available information from camera inversions and other data products is shown, as are assumptions made through the replication process and alternative strategies. Reconstructed flow maps are shown to maintain the small‐scale features near arc boundaries while increasing the dimensionality to 2‐D and to follow the time evolution of the arc structure by comparisons to imagery. The average electric field magnitudes per unit area of the reconstructed and divergence‐minimized flow fields are also calculated and compared between different data sources.

This paper presents data and a procedure for the use of irregular subarrays in planar wideband arrays. Using element-level phase shifters and time delay at subarray ports, the procedure substitutes polyomino subarrays in place of conventional rectangular subarrays to eliminate quantization lobes. The paper focuses on the following details of the design: the choice of subarray size for specified bandwidth, the selection of subarray type, and the tradeoff between bandwidth and peak sidelobe level. These items of the design procedure are detailed for arrays with 4- and 8-element subarrays.

Measurement of ionospheric plasma is often performed by a single in situ device or remotely using cameras and radar. This article describes a small, low-resource, deployed spacecraft used as part of a local, multipoint measurement network. A B-field aligned sounding rocket ejects four of these spin-stabilized spacecraft in a cross pattern. In this application, each spacecraft carries two retarding potential analyzers which are used to determine plasma density, flow, and ion temperature. An inertial measurement unit and a light-emitting diode array are used to determine the position and orientation of the devices after deployment. The design of this spacecraft is first described, and then results from a recent test flight are discussed. This flight demonstrated the successful operation of the deployment mechanism and telemetry systems, provided some preliminary plasma measurements in a simple mid-latitude environment, and revealed several design issues.