# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 1 Introduction  
Following the first proposal of the GNSS-R principle for scatterometry in the late 1980s [1], and later in the early 1990s for ocean altimetry [2], the technology was later demonstrated from low-Earth-orbit altitudes by the UK-Disaster Monitoring Constellation (UK-DMC) [3] and UK TechDemoSat-1 (TDS-1) missions [4, 5]. It has since been implemented as a dedicated mission with the NASA CyGNSS constellation of small satellites [6]. GNSS-R receivers detect and correlate reflected GNSS signals with the direct signals from the same GNSS transmitter to produce delay Doppler maps (DDMs). GNSS-R is particularly promising as it does not require a dedicated transmitter, thereby reducing the cost, mass, and power requirements of the instrument. These advantages offer the potential to deploy GNSS-R sensors as payloads of opportunity aboard other missions or as low-cost multi-satellite constellations.  
Examining the correlation between the reflected and direct signals allows inference of a number of Earth surface parameters, which can be broadly categorised as being associated with surface reflectivity (scatterometry) or surface elevation (altimetry). GNSS-R has been particularly well demonstrated for the remote sensing of ocean wind speed [7, 8, 9, 5], and also for other geophysical variables including sea ice and soil moisture [10, 11]. Retrieval of ocean wind speed works on the principle of increased wind speed driving an increase in the mean square slope (MSS) of waves. The normalised bistatic radar cross-section (NBRCS) describes the strength of the signal scattered of the rough surface in the directionof the receiver's antenna, which will decrease with an increase in MSS, as detailed by the bistatic radar equation [12].  
NASA CyGNSS is a constellation of eight small satellites (aka flight models, or FM) with GNSS-R receivers as the unique scientific payload. CyGNSS has the primary objective of measuring wind speed in hurricanes and tropical cyclones with high temporal frequency [13]. This goal is addressed by using a constellation of eight satellites in a low inclination orbital plane to enable rapid revisit times (of the order of hours). In addition, the use of L-band microwave signals (GPS L1 wavelength \(\sim\)19 cm) should make GNSS-R less sensitive to attenuation due to the presence of water vapour or liquid rain in the atmosphere, as in the case of tropical cyclones. Since launch, CyGNSS GNSS reflections have also been shown to be sensitive to other geophysical parameters, including soil moisture content, aboveground biomass, and inundation [11; 14], providing an opportunity to also demonstrate the potential of GNSS-R for the remote sensing of land and hydrological properties.  
A new version of the CyGNSS products (v3.0) was recently released by NASA. v3.0 uses an improved calibration approach that aims to address some of the anomalies observed in previous versions of CyGNSS Level-2 winds, including anomalous temporal trends and biases between GPS blocks (production generations of GPS satellites) [15]. For the first time, CyGNSS v3.0 data processing includes signal calibration sub-modules that make use of data collected by the up-pointing zenith antenna to directly measure the power of direct signals available from GPS satellites [16] following previous efforts to characterise GPS transmit power and antenna patterns using ground-based data [17]. This paper presents an assessment of these latest CyGNSS products at Level 1, with particular attention given to the temporal stability and spatial homogeneity of CyGNSS calibrated Level-1 observables.