# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 1 Introduction  
Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) performs Earth remote sensing by measuring reflections off the Earth's surface by signals transmitted from various GNSS constellations, including the U.S. Global Positioning System (GPS), the European Galileo constellation, and others. All of the satellites within a GNSS constellation typically transmit within the same frequency bands, but use spread spectrum techniques to distinguish the transmissions of different space vehicles [1]. This allows an appropriately designed GNSS receiver to track multiple GNSS signals simultaneously, all of which can potentially be used for surface remote sensing. a history and overview of GNSS-R and its applications can be found in [2]. The potential of GNSS-R to perform land Earth observations was first demonstrated from a space platform in 2007 using a small amount of raw sampled data from the UK-DMC satellite [3]. Previously, the sensitivity of GPS reflections to surface water and soil moisture was demonstrated using aircraft experiments [4]. The launch of the NASA Cyclone GNSS (CYGNSS) mission in Dec 2016 marked a significant opportunity to further validate and develop GNSS-R land applications. The primary mission of the eight satellite CYGNSS constellation is to measure ocean near surface winds for hurricane research [5]. However, multiple studies have recently demonstrated the use of CYGNSS land observations in surface water monitoring applications, including flood inundation [6] and near surface soil moisture [7, 8]. The CYGNSS Level 1 ocean calibration is based on an inversion of the bistatic radar equation, and includes corrections for all instrument related and non-geophysical parameters affecting the received power levels [9, 10]. However, GNSS-R signals received from land surfaces require several non-trivial modifications to the existing ocean calibration algorithms to enable land parameter retrievals. These modifications are a result of the fundamental differences in the reflecting surface and the associated surface scattering processes between ocean and land observations. The ocean surface (to first order) can be approximated by a Gaussian distribution of directional surface wave slopes, which are indirectly related to the near surface wind speed [11]. By contrast, land surfaces exhibit more diverse and irregular distributions of reflecting surfaces, as land surfaces often do not lend themselves to simple surface distribution approximations (although exponential and other land slope approximations have shown some utility [12]). In addition, the typically large RMS heights of the ocean surface relative to the 19 cm electromagnetic wavelength result in an incoherent scattering process (with occasional exceptions in low wind/wave conditions). This allows the CYGNSS Level 1 ocean calibration to focus on incoherent scattering alone. In constrast, CYGNSS land observations exhibit both coherent reflection and incoherent scattering, due to the underlying variability in both topography and surface cover [6]. The presence of inland water bodies typically having very smooth surfaces also increases the frequency of very strong coherent or mixed reflection returns [13]. Previous studies on GNSS-R land retrievals, coherent and incoherent scattering and the impact of topography on GNSS-R land observations can be found in [14, 15, 16]. In addition to the fundamental differences in surface scattering mechanisms, the geolocation of the dominant scattering region on the Earth's surface is complicated by large scale variations in land surface topography. The existing CYGNSS ocean geolocation calculation [17] uses the WGS84 ellipsoid to define the Earth's surface with a small correction for the mean sea surface height. The geolocation of land reflections requires additional consideration of local terrain variations, which can significantly shift the dominant scattering region. In some instances (e.g., extreme mountain environments) a land geolocation can become infeasible or ambiguous.