A constellation of small, low-cost satellites is able to make scientifically valuable measurements of the Earth which can be used for weather forecasting, disaster monitoring, and climate studies. Eight CYGNSS satellites were launched into low Earth orbit on December 15, 2016. Each satellite carries a science radar receiver which measures GPS signals reflected from the Earth surface. The signals contain information about the surface, including wind speed over ocean, and soil moisture and flooding over land. The satellites are distributed around their orbit plane so that measurements can be made more often to capture extreme weather events. Innovative engineering approaches are used to reduce per satellite cost, increase the number in the constellation, and improve temporal sampling. These include the use of differential drag rather than propulsion to adjust the spacing between satellites and the use of existing GPS signals as the science radars’ transmitter. Initial on-orbit results demonstrate the scientific utility of the CYGNSS observations, and suggest that a new paradigm in spaceborne Earth environmental monitoring is possible.

The global navigation satellite system-reflectometry (GNSS-R) field has experienced an exponential growth as it is becoming relevant to many applications and has captivated the attention of an elevated number of research scholars, research centers and companies around the world. Primarily based on the contents of two Special Issues dedicated to the applications of GNSS-R to Earth observation, this review article provides an overview of the latest advances in the GNSS-R field. Studies are reviewed from four perspectives: (1) technology advancements, (2) ocean applications, (3) the emergent land applications, and (4) new science investigations. The technology involved in the GNSS-R design has evolved from its initial GPS L1 LHCP topology to include the use of other GNSS bands (L2, L5, Galileo, etc.), as well as consider RHCP/LHCP-receiving polarizations in order to perform polarimetric studies. Ocean applications have included developments towards ocean wind speed retrievals, swell and altimetry. Land applications have evolved considerably in the past few years; studies have used GNSS-R for soil moisture, vegetation opacity, and wetland detection and monitoring. They have also determined flood inundation, snow height, and sea ice concentration and extent. Additionally, other applications have emerged in recent years as we have gained more understanding of the capabilities of GNSS-R.

The Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of eight microsatellites that provide observations of surface wind speed in all precipitating conditions. A method for estimating tropical cyclone (TC) metrics—maximum surface wind speed V_MAX, radius of maximum surface wind speed R_MAX, and wind radii (R₆₄, R₅₀, and R₃₄)—from CYGNSS observations is developed and tested using simulated CYGNSS observations with realistic measurement errors. Using two inputs, 1) CYGNSS observations and 2) the storm center location, estimates of TC metrics are possible through the use of a parametric wind model algorithm that effectively interpolates between the available observations as a constraint on the assumed wind speed distribution. This methodology has a promising performance as evaluated from the simulations presented. In particular, after quality-control filters based on sampling properties are applied to the population of test cases, the standard deviation of retrieval error for V_MAX is 4.3 m s⁻¹ (where 1 m s⁻¹ = 1.94 kt), for R_MAX is 17.4 km, for R₆₄ is 16.8 km, for R₅₀ is 21.6 km, and for R₃₄ is 41.3 km (where 1 km = 0.54 n mi). These TC data products will be available for the 2017 Atlantic Ocean hurricane season using on-orbit CYGNSS observations, but near-real-time operations are the subject of future work. Future work will also include calibration and validation of the algorithm once real CYGNSS data are available.

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.

The Soil Moisture Active Passive (SMAP) mission became one of the newest spaceborne Global Navigation Satellite System–Reflectometry (GNSS-R) missions collecting Global Positioning System (GPS) bistatic radar measurements when the band-pass center frequency of its radar receiver was switched to the GPS L2C band. SMAP-Reflectometry (SMAP-R) brings a set of unique capabilities, such as polarimetry and improved spatial resolution, that allow for the exploration of scientific applications that other GNSS-R missions cannot address. In order to leverage SMAP-R for scientific applications, a calibration must be performed to account for the characteristics of the SMAP radar receiver and each GPS transmitter. In this study, we analyze the unique characteristics of SMAP-R, as compared to other GNSS-R missions, and present a calibration method for the SMAP-R signals that enables the standardized use of these signals by the scientific community. There are two key calibration parameters that need to be corrected: The first is the GPS transmitted power and GPS antenna gain at the incidence angle of the measured reflections and the second is the convolution of the SMAP high gain antenna pattern and the glistening zone (Earth surface area from where GPS signals scatter). To account for the GPS transmitter variability, GPS instrument properties—transmitted power and antenna gain—are collocated with information collected from the CYclone Global Navigation Satellite System (CYGNSS) at SMAP’s range of incidence angles (37.3° to 42.7°). To account for the convolutional effect of the SMAP antenna gain, both the scattering area of the reflected GPS signal and the SMAP antenna footprint are mapped on the surface. We account for the size of the scattering area corresponding to each delay and Doppler bin of the SMAP-R measurements based off the SMAP antenna pattern, and normalize according to the size of a measurement representative to one obtained with an omnidirectional antenna. We have validated these calibration methods through an analysis of the coherency of the reflected signal over an extensive area of old sea ice having constant surface characteristics over a period of 3 months. By selecting a vicarious scattering surface with high coherency, we eliminated scene variability and complexity in order to avoid scene dependent aliases in the calibration. The calibration method reduced the dependence on the GPS transmitter power and gain from ~1.08 dB/dB to a residual error of about −0.2 dB/dB. Results also showed that the calibration method eliminates the effect of the high gain antenna filtering effect, thus reducing errors as high as 10 dB on angles furthest from SMAP’s constant 40° incidence angle.