# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 1 Introduction  
Polar sea ice is a sensitive indicator and an important driver of global climate change [1]. For instance, the presence of sea ice affects sea surface albedo and reduces the amount of solar radiation absorbed by the Earth's surface [2]. The spatial and temporal distributions of polar sea ice derived from remote sensing data are important for monitoring global climate change, weather forecasting, and marine transportation [3]. Studies have shown that the sea ice extent is declining in the Arctic, while the trend in the Antarctic is not so clear [4]. In recent years, the study of polar sea ice based on spaceborne active/passive microwave measurements has been receiving increasing attention. Sea ice extent is a parameter used to describe sea ice conditions and is usually determined on the basis of data from two types of sensors, namely passive microwave radiometers [5] and active microwave scatterometers [6]. Compared to optical remote sensing, microwave sensors are an important means, because they are not seriously affected by sunlight and the atmosphere. The radiometers observe the natural emission from the surface in the range of microwave frequencies, while the scatterometers observe the energy reflected from a transmitted pulse. Microwave scatterometer backscattering characteristics are closely related to the structural properties and composition of sea ice, includingdielectric constant, sea ice thickness, temperature, snow thickness, humidity, and salinity, etc. These parameters are related to the type and age of sea ice and external climate change, and also to the type of incident electromagnetic waves (polarization, wavelength, angle of incidence, and azimuth), and these differences are used to distinguish sea ice from open seawater to obtain sea ice coverage and sea ice boundaries. To date, several sources of sea ice product (e.g., sea ice extent, sea ice type) have been developed that are based on space-borne microwave scatterometer data [7; 8; 9; 10]. In 1997, [PERSON] et al. found that the scattering characteristics at the Ku-band could be used to distinguish sea ice from open seawater [11]. Shortly afterward, [PERSON] and [PERSON] developed an algorithm for the automatic identification of sea ice and open water using Ku-band NSCAT (NASA Scatterometer) backscatter measurements [12; 13]. This technique is widely known as the Remund/Long-NSCAT (RL-N) algorithm, and it utilizes the backscatter differences in polarization, azimuth, and temporal variation to detect and classify sea ice [14]. However, the accuracy of the RL-N algorithm may be affected by strong winds and summer sea ice melt [15]. In 2014, [PERSON] and [PERSON] imporved the RL-N algorithm for Ku-band QuikSCAT/SeaWinds scatterometer data for the classification of sea ice and water. The adapted RL-N algorithm uses an iterative maximum-likelihood classifier with fixed thresholds, and the residual misclassifications are corrected using an image processing technique with the consideration of the patterns of sea ice growth and melting [7]. The radar backscatter data from C-band scatterometers (e.g., scatterometer on board the European Remote Sensing (ERS) satellites or MetOp series satellites) are also widely used in the detection of sea ice extent. For instance, researchers from the French Research Institute for Exploitation of the Sea (IFREMER) used the ERS scatterometer measurements for sea ice detection [16; 17]; they proposed two characteristic parameters, which are based on the fact that the scattering anisotropy of sea ice is stronger than that of open seawater, and sea ice backscattering varies less with the incidence angle than does open seawater backscattering. For ice/water discrimination, [PERSON] et al. proposed a new characteristic parameter utilizing the MetOp-A/ASCAT antenna configuration, which provides three different look angles for the same surface spot [18].