This paper presents the processing algorithms for geolocating and calibration of the Cyclone Global Navigation Satellite System (CYGNSS) level 1 land data products, as well as analysis of the spatial resolution of Global Navigation Satellite System Reflectometry (GNSS-R) coherent reflections. Accurate and robust geolocation and calibration of GNSS-R land observations are necessary first steps that enable subsequent geophysical parameter retrievals. The geolocation algorithm starts with an initial specular point location on the Earth’s surface, predicted by modeling the Earth as a smooth ellipsoid (the WGS84 representation) and using the known transmitting and receiving satellite locations. Information on terrain topography is then compiled from the Shuttle Radar Topography Mission (SRTM) generated Digital Elevation Map (DEM) to generate a grid of local surface points surrounding the initial specular point location. The delay and Doppler values for each point in the local grid are computed with respect to the empirically observed location of the Delay Doppler Map (DDM) signal peak. This is combined with local incident and reflection angles across the surface using SRTM estimated terrain heights. The final geolocation confidence is estimated by assessing the agreement of the three geolocation criteria at the estimated surface specular point on the local grid, including: the delay and Doppler values are in agreement with the CYGNSS observed signal peak and the incident and reflection angles are suitable for specular reflection. The resulting geolocation algorithm is first demonstrated using an example GNSS-R reflection track that passes over a variety of terrain conditions. It is then analyzed using a larger set of CYGNSS data to obtain an assessment of geolocation confidence over a wide range of land surface conditions. Following, an algorithm for calibrating land reflected signals is presented that considers the possibility of both coherent and incoherent scattering from land surfaces. Methods for computing both the bistatic radar cross section (BRCS, for incoherent returns) and the surface reflectivity (for coherent returns) are presented. a flag for classifying returns as coherent or incoherent developed in a related paper is recommended for use in selecting whether the BRCS or reflectivity should be used in further analyses for a specific DDM. Finally, a study of the achievable surface feature detection resolution when coherent reflections occur is performed by examining a series of CYGNSS coherent reflections across an example river. Ancillary information on river widths is compared to the observed CYGNSS coherent observations to evaluate the achievable surface feature detection resolution as a function of the DDM non-coherent integration interval.

The Cubesat radiometer radio frequency interference technology validation mission (CubeRRT) was developed to demonstrate real-time onboard detection and filtering of radio frequency interference (RFI) for wide bandwidth microwave radiometers. CubeRRT's key technology is its radiometer digital backend (RDB) that is capable of measuring an instantaneous bandwidth of 1 GHz and of filtering the input signal into an estimated total power with and without RFI contributions. CubeRRT's onboard RFI processing capability dramatically reduces the volume of data that must be downlinked to the ground and eliminates the need for ground-based RFI processing. RFI detection is performed by resolving the input bandwidth into 128 frequency subchannels, with the kurtosis of each subchannel and the variations in power across frequency used to detect nonthermal contributions. RFI filtering is performed by removing corrupted frequency subchannels prior to the computation of the total channel power. The 1 GHz bandwidth input signals processed by the RDB are obtained from the payload's antenna (ANT) and radiometer front end (RFE) subsystems that are capable of tuning across RF center frequencies from 6 to 40 GHz. The CubeRRT payload was installed into a 6U spacecraft bus provided by Blue Canyon Technologies that provides spacecraft power, communications, data management, and navigation functions. The design, development, integration and test, and on-orbit operations of CubeRRT are described in this article. The spacecraft was delivered on March 22nd, 2018 for launch to the International Space Station (ISS) on May 21st, 2018. Since its deployment from the ISS on July 13th, 2018, the CubeRRT RDB has completed more than 5000 h of operation successfully, validating its robustness as an RFI processor. Although CubeRRT's RFE subsystem ceased operating on September 8th, 2018, causing the RDB input thereafter to consist only of internally generated noise, CubeRRT's key RDB technology continues to operate without issue and has demonstrated its capabilities as a valuable subsystem for future radiometry missions.

The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne GNSS-reflectometry mission, which was launched on December 15, 2016 for ocean surface wind speed measurement. CYGNSS includes eight small satellites in the same low earth orbit, so that the mission provides wind speed products having unprecedented coverage both in time and space to study multitemporal behaviors of oceanic winds. The nature of CYGNSS coverage results in some locations on earth experiencing multiple wind speed measurements within a short period of time (a “clump” of observations in time) resulting in a “rapid revisit” series of measurements. Such observations seemingly can provide indications of regions experiencing rapid changes in wind speeds, and therefore serve as an indicator of convective activity. An initial investigation of this concept using simulated and on-orbit CYGNSS measurements is provided in this paper. The temporally “clumped” properties of CYGNSS measurements are examined, and the results show that clump durations and spacing vary with latitude. For example, the duration of a clump can extend as long as a few hours at higher latitudes, with gaps between clumps ranging from 6 to as high as 12 h depending on latitude. Initial examples are provided to indicate the potential of changes within a clump to detect convective activity through a comparison with convective activity indicators derived from model datasets. The results at present are limited by the ongoing calibration of CYGNSS wind speed retrievals, so that future work will be required to obtain a more complete assessment, but nevertheless clearly indicate the potential utility of the method for studies of atmospheric convection.

This paper investigates the performance of multiple approaches (including Maximum Likelihood Estimation) for determining the Doppler frequency of radar returns from clutter scatterers. Fields backscattered from clutter are modeled as a complex stationary random process with a specified temporal correlation function received in the presence of additive thermal noise at a specified clutter-to-noise ratio (CNR). The MLE implementation is formulated, and its results are then compared through a simulation study to those obtained from traditional pulse-pair processing (PPP) and other FFT-based methods in terms of error statistics and computational complexity. The methods are also compared for measurements of sea clutter from an X-band radar system. The results of the study show that the MLE approach has modest advantages over other methods in certain conditions, but that the performance gains obtained may often be insufficient to justify the extra computational cost or a-priori knowledge requirements of the method.

Global Navigation Satellite System Reflectometry (GNSS-R) provides a unique means of inferring geophysical conditions of the Earth’s surface without the need for costly, and often infeasible, in-situ climate monitoring systems. As part of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, and in conjunction with Air New Zealand, we are taking the novel approach of mounting a GNSS-R receiver on a commercial aircraft, which shall allow for an unprecedented collection of climate data over and around the islands of New Zealand. Such data include inundation and coastal dynamics, and soil moisture content and variability. We report back to the community how the OpenSky Network data support our climate monitoring research. We discuss how we use the historical database state-vectors to simulate and visualise the predicted geographical coverage of the airborne GNSS-R receiver. We also discuss how the live API can help monitor our payload in-flight, our investigations into the OpenSky ADS-B coverage over New Zealand, and our plans to expand the coverage.