The NOAA-21 Advanced Technology Microwave Sounder (ATMS), launched in November 2022, collected 2-D lunar scan data on March 10, 2023, during its commissioning phase at a Moon phase angle of 34 degrees. The raw data were calibrated using Microwave Calibration Processing System (MiCalPS), a microwave calibration and geolocation tool developed at the University of Maryland. Analysis showed that NOAA-21's lunar antenna gain is generally lower than NOAA-20's due to sampling rate differences. After correcting for beam-pointing errors, disk-averaged lunar brightness temperatures (T-B,Moon(DISK)) were derived for 23-183 GHz. These were compared with NOAA-20 data at 0 degrees phase angle and predictions from the microwave lunar radiative transfer model (MLRTM). The observed model differences were: -3.3 K (K-band), 0.01 K (V), 2.0 K (W), -2.3 K (low-G), and 1.6 K (high-G), consistent with predicted phase delay trends. Further observations at varying Moon phases are recommended to enhance MLRTM validation.

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time-varying receiver characteristics. For numerical weather prediction (NWP) users, 1/f noise introduces interchannel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in the ground data-processing system is essential to enhance the utility of microwave sounding data. This article focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both the frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise is analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

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.

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.

The NASA ISRO synthetic aperture radar (NISAR) mission scheduled for launch in 2024 will provide global L-band radar observations that can be applied to estimate land surface soil moisture. The mission's soil moisture product will be provided at 200-m resolution with a global revisit frequency of 12 days (or 6 days when considering both ascending and descending observations). A time-series ratio algorithm for soil moisture retrieval has been applied to NISAR simulated datasets from airborne UAVSAR measurements in the SMAPVEX12 field campaign. Soil moisture retrieval performance using the algorithm is encouraging, with a correlation coefficient between retrievals and in situ observations greater than 0.7 and an unbiased root-mean-squared Error (RMSE) of 0.05 ${{{\bm{m}}}^3}/{{{\bm{m}}}^3}$. The results suggest that the time-series ratio algorithm will provide soil moisture products that meet an accuracy goal of 0.06 ${{{\bm{m}}}^3}/{{{\bm{m}}}^3}$ unbiased RMSE.

The use of a “time-series ratio” soil moisture retrieval approach is under consideration for the upcoming NISAR mission’s soil moisture product. As such, it is of interest to characterize the algorithm’s anticipated error budget as part of pre-launch activities. This paper develops an error model to estimate retrieval errors for the proposed algorithm. The model accounts for error contributions from speckle and thermal noise as well as uncertainties that arise as part of the retrieval process. Spatial and temporal behaviors of the retrieval errors are determined using a SMAP-based soil moisture climatology. The results show that soil moisture retrieval errors from the time-series ratio method meet the 0.06 m 3 /m 3 unbiased root mean square error (URMSE) performance metric established for the NISAR soil moisture product.

Surface roughness serves as one of key inputs to traditional scattering models to predict bistatic scattered fields; these model predictions can then be used to support the calibration and/or validation of GNSS-R land observations. Lidar datasets over the San Luis Valley and the vicinity of White Sands were acquired in two airborne campaigns to provide information on surface properties for this application, including non-detrended and detrended root mean square (rms) height, mean slopes, and the variance of slopes over a fixed patch size of 30 meters. A spectral analysis of surface roughness using the power spectral density (PSD) was also conducted. The 2-D PSD shows the different characteristics of ‘flat’ and ‘rough’ surfaces. A normalized 1-D PSD derived by averaging 2-D results over azimuth. A parameterization method is proposed to describe this 1-D spectrum using a power law function with an exponent and a scale length. The parameterized spectrum can be applied in further studies of scattering models and the cal/val of GNSS-R land returns.

An airborne microwave wide-band radiometer (500–2000 MHz) was operated for the first time in Antarctica to better understand the emission properties of sea ice, outlet glaciers and the interior ice sheet from Terra Nova Bay to Dome C. The different glaciological regimes were revealed to exhibit unique spectral signatures in this portion of the microwave spectrum. Generally, the brightness temperatures over a vertically homogeneous ice sheet are warmest at the lowest frequencies, consistent with models that predict that those channels sensed the deeper, warmer parts of the ice sheet. Vertical heterogeneities in the ice property profiles can alter this basic interpretation of the signal. Spectra along the lengths of outlet glaciers were modulated by the deposition and erosion of snow, driven by strong katabatic winds. Similar to previous experiments in Greenland, the brightness temperatures across the frequency band were low in crevasse areas. Variations in brightness temperature were consistent with spatial changes in sea ice type identified in satellite imagery and in situ ground-penetrating radar data. The results contribute to a better understanding of the utility of microwave wide-band radiometry for cryospheric studies and also advance knowledge of the important physics underlying existing L-band radiometers operating in space.

Lidar elevation maps are utilized in a bistatic electromagnetic scattering model of the Cyclone Global Navigation Satellite System over San Luis Valley in Colorado, USA. The results are compared with the results generated using the Shuttle Radar Topography Mission (SRTM) 1-arcsecond global digital elevation model (DEM). The high-resolution lidar maps are also used to generate maps of surface roughness at length scales finer than the horizontal resolution of SRTM. Model results using these maps illustrate the importance of the spatial variation of surface roughness on reflectometry signals. Results also highlight the shortcomings of using SRTM in scattering models and show improvements when a lidar DEM is used.

This paper presents a novel method for core temperature retrieval using microwave radiometry when complex permittivity and heat transfer parameters of the tissue layers of the human subject are unknown. Previous works present methods for core temperature retrieval, but these methods do not account for population variation in the relevant electromagnetic and thermal parameters, which can increase measurement error beyond the clinically acceptable limit of 0.5 °C. Pennes’ bioheat model of a six-tissue-layer human head model combined with a coherent electromagnetic model simulate experimental data. To retrieve core temperature, nonlinear least squares optimization is then used to minimize the difference between the simulated experimental data and an exponential model for physical temperature and the coherent electromagnetic model. By using 20 frequencies spanning from 1-5 GHz, core temperature is retrieved while accounting for population variation in the permittivity and thermal parameters. A Monte Carlo simulation in which the thermal parameters and permittivity vary according to literature derived, population-representative distributions and the core body temperature varies from 18 to 46 °C is used to assess the utility of the retrieval method. Different antenna patterns are tested to explore the effect on retrieval accuracy. The retrieval method has a retrieval error of <0.1 °C when only the thermal parameters are unknown and a retrieval error of <0.5 °C when the thermal parameters and permittivity are unknown, which is within the clinically acceptable error range of 0.5 °C. These results help progress the field of medical microwave radiometry toward being a clinically viable noninvasive measurement that is accurate when measuring all patients.

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 ( $T_{i}$ ) by measuring microwave brightness temperatures ( $T_{b}$ ) from 0.5 to 2 GHz. The retrieval of $T_{i}$ from $T_{b}$ is not straightforward, however, due in part to the complicating effects of ice density fluctuations on $T_{b}$ . In this article, we report a simulation study to assess the feasibility of realizing three science goals: the retrieval of: 1) $T_{i}$ at 10 m depth to within 1 K; 2) vertically averaged $T_{i}$ to within 1 K; and 3) the vertical $T_{i}$ profile to within 1 K RMSE. Two analyses along the Greenland ice divide are presented. First, we assess the ideal UWBRAD $T_{i}$ retrieval 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 UWBRAD $T_{b}$ precision impact performance. Assuming a UWBRAD $T_{b}$ precision 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.

Ice sheet subsurface temperature is important for understanding glacier dynamics, yet existing methods to obtain the temperature of the ice sheet column are limited to in situ sources at present. The ultrawideband software-defined microwave radiometer (UWBRAD) has been developed to investigate the remote sensing of ice sheet internal temperatures. UWBRAD measures brightness temperature spectra from 0.5 to 2 GHz using 12 subchannels and employs a sophisticated algorithm for detection and mitigation of radio frequency interference (RFI). The instrument was deployed during a flight over northwestern Greenland in September 2017 and acquired the first wideband low-frequency brightness temperature spectra over the ice sheet and coastal regions. The results reveal strong spatial and spectral variations that correlate well with internal ice sheet temperature information. In this article, the section of the flight path ranging from the Camp Century to NEEM to NGRIP boreholes is used for subsurface temperature estimation. A “partially coherent” forward model is applied along with a Robin model for the temperature profile and a two-scale model of ice sheet density variations to describe measured brightness temperatures. Using this model, vertical temperature profiles are retrieved along the flight path using a sequential Bayesian estimator; borehole measurements at the three campsites are used to obtain Bayesian priors. The retrieved temperature profiles show reasonable behaviors and demonstrate the potential of ultrawideband microwave radiometry for remotely sensing internal ice sheet temperatures.

The National Aeronautics and Space Administration (NASA) - Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) mission plan to launch a SAR operating at L- and S-band with a 12-day repeat frequency. A global soil moisture product at 200 m spatial resolution derived from 200 m NISAR radar measurements is currently under development. Although several retrieval algorithms are being investigated, this paper focuses on a “time series ratio” retrieval approach. In order to understand and assess the performance of this algorithm, an error model has been developed and is reported in this paper. The model is applied to examine errors as a function of the instrument characteristics and for a given location. Initial progress in including vegetation effects and in predicting errors as a function of spatial location is also described.

Wideband microwave radiometry holds promise for new in-sights for passive remote sensing of snow packs. We report on a plot-scale, season-long field experiment using two wideband microwave radiometers operating with about 1 GHz of bandwidth at L-band frequencies. Despite considerable radio frequency interference (RFI), there remains many 100's of MHz of usable bandwdith for measurement. We report on the wideband signatures of a snowpack that varies in depth from no snow to more than 57 cm deep.

In this paper, an Analytical Kirchhoff Solution (AKS) and Numerical Kirchhoff approach (NKA) are used to study coherent and incoherent land surface near specular scattering at L and P bands. The AKS model includes both coherent and incoherent waves, and includes the effects of topographic slopes and elevations. The land profile is modelled as a summation of three scales of surface roughness corresponding to “microwave”, “fine topography”, and “coarse topography”, where the microwave roughness and fine topography are treated as random processes while the coarse topography is deterministic. An airborne lidar survey performed over the San Luis Valley, CO is used to obtain surface roughness information for the simulation results of $\mathrm{P}$ and L-band scattering. Results using the lidar surface data show that coherent reflection can dominate returns from a 5 km by 5 km area at P band, while incoherent scattering dominates L band returns in the same scenario.

The physical process of GPS signal reflection from Earth's land surface is of interest in order to support the development of land applications using GNSS-Reflectometry (GNSS-R) data. A key question is understanding when GNSS-R land returns should be examined in terms of their reflectivity or normalized radar cross section (NRCS). An airborne lidar DEM obtained near White Sands, NM is used to compute surface roughness properties. The roughness map is then correlated with the recurrence of coherency derived from three years of CYGNSS observations. The results show that “medium scale” rms heights within the first Fresnel zone of less than < ~ 10 cm are associated with coherent reflections. Initial analysis of a raw IF data track further demonstrates the decrease of coherence with increased surface roughness.

This paper examines the utility of a wideband, physics-based model to determine human core body or brain temperature via microwave radiometry. Pennes's bioheat equation is applied to a six-layer human head model to generate the expected layered temperature profile during the development of a fever. The resulting temperature profile is fed into the forward electromagnetic (EM) model to determine the emitted brightness temperature at various points in time. To accurately retrieve physical temperature via radiometry, the utilized model must incorporate population variation statistics and cover a wide frequency band. The effect of human population variation on emitted brightness temperature is studied by varying the relevant thermal and EM parameters, and brightness temperature emissions are simulated from 0.1 MHz to 10 GHz. A Monte Carlo simulation combined with literature-derived statistical distributions for the thermal and EM parameters is performed to analyze population-level variation in resulting brightness temperature. Variation in thermal parameters affects the offset of the resulting brightness temperature signature, while EM parameter variation shifts the key maxima and minima of the signature. The layering of high and low permittivity layers creates these key maxima and minima via wave interference. This study is one of the first to apply a coherent model to and the first to examine the effect of population-representative variable distributions on radiometry for core temperature measurement. These results better inform the development of an on-body radiometer useful for core body temperature measurement across the human population.

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.