# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 1 Introduction  
Rapidly acquiring global high temporal and spatial resolution ocean surface wind field has extremely significant in many fields. Spaceborne GNSS-R is a relatively new remote sensing technique with promising prospects, which receives reflected GNSS signals from the Earth's surface. With the GNSS-R receiver mounted on a low Earth orbit (LEO) microsatellite, it can form a spaceborne bistatic radar scatterometer to sense wind speed near the sea surface. The hardware instrument of the GNSS-R payload is lightweight and low in power, which can greatly reduce the deployment cost of this remote sensing technique. Through reasonable satellite constellation designing, continuous and rapid measurement of the global sea surface wind speed can be reached, which will effectively compensate for the shortcomings of the traditional monostatic scatterometer and radiometer. The idea of using the signals of opportunity from GNSS for geophysical parameter detection has been through nearly 30 years of development. The passive reflectometry and interferometry system (PARIS) mesoscale ocean altimetry concept was first proposed by [PERSON] in 1993 [1]. In 1996, [PERSON] conceived of receiving GNSS signals from ocean surface reflection using receivers mounted on LEO satellites to remotely sense ocean states and ocean surface physical parameters [2]. In 2000, [PERSON] and [PERSON] presented the GNSS-R bistatic radar scattering model [3], which is based on the geometric optical approximation of the Kirchhoff (KA-GO) model that describes the average scattering power of the GNSS signal reflected by the sea surface to the receiver direction over different time delays and Doppler frequency shifts. Subsequently, simulation based on the theoretical model [4; 5; 6] and the feasibility test of the real satellite-based GNSS-R mission have been gradually carried out. In 1998, [PERSON] and [PERSON] conducted the airborne GNSS-R experiment that preliminarily demonstrated the potential of GNSS-R to sense the ocean state [7; 8]. The first spaceborne GNSS-R experiment was implemented on Shuttle Imaging Radar with Payload C (SIR-C) carried on the space shuttle to verify the feasibility and determine the expected signal-to-noise (SNR) ratio on LEO [9]. The UK Disaster Monitoring Constellation (UK-DMC) was launched in 2003, and successfully verified that GPS signals could be received from the ice-ocean, snow, ocean water, and even in the case of land on the LEO [10]. On July 8, 2014, the UK Technology Demonstration Satellite-1 (TDS-1) satellite was successfully launched with a specific Spaceborne GNSS Receiver REmote Sensing Instrument (SGR-ReSI) payload specifically designed for generating Delay/Doppler map (DDM) in real-time [11]. On 15 December 2016, NASA launched the CYGNSS satellite constellation consisting of eight small satellites, which became the first microsatellite constellation dedicated to GNSS-R ocean wind remote sensing [12]. In addition, GNSS-R experiments on-board Soil Moisture Active and Passive (SMAP) [13] and GNSS Reflectometry, Radio Occultation, and Scatterometry Onboard the International Space Station (GEROS-ISS) [14] missions both aim to exploit the GNSS-R technique for geophysical parameters remote sensing. The onboard Delay-Doppler mapping instrument (DDMI) outputs DDM after a series of digital signal processing and precise calibration [15]. The scattering power in different DDM pixels is the average cross-correlation power of the sea surface reflected signal received by the nadir left-hand circular polarization (LHCP) antenna and a locally clear replica GNSS navigation code in the receiver. In most missions, this mode is called traditional satellite-based GNSS-R, abbreviated as cGNSS-R. There is another GNSS-R operation mode that directly uses the reflected and direct signals for correlation processing that is called iGNSS-R [16].