# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 1 Introduction  
Knowledge of sea ice coverage and thickness of the polar region is very important for ship routing [1], oil and gas exploration [2], and global weather studies [3]. Although in situ monitoring may provide continuous sea ice information, the spatial sampling is typically insufficient to provide information on global sea ice extent. Estimating global sea ice coverage with adequate temporal resolution can only be achieved using remote sensing technology. Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) has been investigated for many years as an innovative remote sensing technique using GNSS signals reflected off the Earth's surface [4]. GNSS-R was originally suggested and applied to ocean altimetry in 1993 as a multistatic radar technique [5]. With the availability of GNSS signals from GPS, GLONASS, Galileo and BeiDou satellite constellations, this technique shows great promising. In the following years, applications of GNSS-R were extended to the remote sensing of sea surface roughness [6, 7], wind speed and direction retrieval [8, 9, 10, 11], target detection and positioning [12], soil moisture determination [13, 14, 15, 16, 17], forest biomass [18, 19], oil slick detection [20], tsunami parameter estimation and tsunami detection [21, 22], sea surface altimetry [23, 24], ocean topography [25, 26], coastal and continental water altimetry [27]and snow depth estimation [28]. The potential of GNSS-R Delay-Doppler Maps (DDMs) for sea ice distribution detection is studied in this paper. GNSS-R-based sea ice detection was first investigated by [PERSON] et al. [29] by comparing the waveform peak power of reflected signals over ice obtained from an airborne GNSS receiver with RADARSAT back-scattering echoes. The feasibility of ice sheet sensing using GNSS-R was exploited in [30], which proposed a theoretical model of ice scattering. Then the shape of reflected GPS waveforms was studied in [31] to retrieve the permittivity and roughness of different types of sea ice. The United Kingdom-Disaster Monitoring Constellation (UK-DMC) launched in 2003 provided spaceborne GNSS-R data for sea ice detection for the first time, which captured the coherent reflection component of signals reflected from sea ice [32]. Unfortunately, the relationship between sea ice parameters and waveform peak power has not been established. In addition, airborne and ground-based studies using reflected GNSS signals were also performed to explore sea ice concentration [33]. After the launch of the UK TechDemoSat-1 (TDS-1), a huge number of DDMs are available from the Measurement of Earth Reflected Radio-navigation Signals by Satellite (MERRByS) website at www.merrbys.co.uk [34]. TDS-1 DDM data have been used by many researchers for different applications, including wind speed retrieval [35], ocean wave characteristic inversion [36], Tsunami detection and parameter estimation [37], soil moisture estimation [38], sea surface height estimation [23; 39] and sea ice detection [40; 41]. The first result of sea ice detection using TDS-1 GNSS-R DDMs was presented in [40], which proposed a pixel number based sea ice detection method. Another sea ice detection algorithm based on the similarity of received reflected waveform or DDM, which performs well on sea ice detection, was proposed by [PERSON], et al. [42]. The goal of the paper presented here is to explore a new approach through estimating the degree of difference between adjacent DDMs with the capability of distinguishing between sea ice and water. A new GNSS-R differential DDM (dDDM) observable derived from adjacent DDMs is proposed firstly. Then, two different methods are proposed to characterize the dDDMs, which are power-summation (PS) and pixel-number (PN) based methods. The different transition types (water-water, water-ice, ice-ice and ice-water) can be classified according to the characteristics of dDDMs.