In 2012, the National Aeronautics and Space Administration (NASA) selected the CYclone Global Navigation Satellite System (CYGNSS) mission coordinated by the University of Michigan (UM) as a low-cost and high-science Earth Venture Mission. The CYGNSS mission was originally proposed for ocean surface wind speed estimation over Tropical Cyclones (TCs) using Earth-reflected Global Positioning System (GPS) signals, as signals of opportunity. The orbital configuration of each CYGNSS satellite is a circular Low Earth Orbit (LEO) with an altitude ~520 km and an inclination angle of ~35°. Each single Delay Doppler Mapping Instrument (DDMI) aboard the eight CYGNSS microsatellites collects forward scattered signals along four specular directions (incidence angle of the incident wave equals incidence angle of the reflected wave) corresponding to four different transmitting GPS spacecrafts, simultaneously. As such, CYGNSS allows one to sample the Earth’s surface along 32 tracks simultaneously, within a wide range of the satellites’ elevation angles over tropical latitudes. Following the Earth Science Division 2020 Senior Review, NASA announced recently it is extending the CYGNSS mission through 30 September 2023. The extended CYGNSS mission phase is focused on both ocean and land surface scientific investigations. In addition to ocean surface wind speed estimation, CYGNSS has also shown a significant ability to retrieve several geophysical parameters over land surfaces, such as Soil Moisture Content (SMC), Above Ground Biomass (AGB), and surface water extent. The on-going science team investigations are presented in this article.

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 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 Earths RFI environment. This paper examines properties of the global RFI environment as observed by SMAP, including information on RFI source properties (obtained from analysis of SMAPs 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.

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.

Cyclone maximum wind retrievals are demonstrated using tracks of full delay-Doppler map (DDM) measurements of the Cyclone Global Navigation Satellite System (CYGNSS) constellation. The method is based on the production of a library of simulated DDMs for a synthetic storm model, as the parameters of the synthetic storm are varied. A matched filter approach between normalized simulated and measured DDMs is then applied to retrieve storm parameters. Because the method uses normalized DDMs in the retrieval, it is insensitive to the absolute power calibration of the DDM and instead relies on the DDM shape. Results from the method are shown for CYGNSS full DDM overpasses of Hurricane Irma as a case study. The maximum wind speed estimates obtained were associated with a retrieval root-mean-square error (RMSE) of 6.89 m/s and an unbiased RMSE 5.20 m/s with a mean difference of 4.52 m/s compared to reported National Hurricane Center Best Track forecasts.

This work further investigates the use of ocean wind speed retrievals from NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission that are closely spaced in space and in time to detect regions in the atmosphere undergoing “rapid” change. As in a previous investigation, CYGNSS measurement “clumps” (i.e., groups of wind speed measurements satisfying specified time/space separation criteria) are used to create a wind speed change detector, and the results are compared to a set of atmospheric properties derived from Modern-Era Retrospective Analysis for Research and Applications 2 model datasets. The analysis uses a four-year CYGNSS Level-2 wind speed dataset developed by the National Oceanic and Atmospheric Administration and highlights the potential for using CYGNSS's wind speed measurements to locate various dynamic activities occurring over the ocean's surface. The presented results also highlight some of the challenges of the method, including the inherent dependence of detection performance on the quality of CYGNSS's wind speed estimates as well as the validity of comparisons to “truth” datasets having temporal resolutions much coarser than those provided by CYGNSS.