# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 1 Introduction
Combing this ASCAT parameter and passive microwave (SSM/I) data, a Bayesian multi-sensor approach was proposed by the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) to operationally produce global sea ice edge products [19]. The Royal Netherlands Meteorological Institute (KNMI) developed a Bayesian approach for sea ice detection using radar backscatter measurements from the C-band scatterometers on board the ERS or MetOp satellites [20]; this approach employs geophysical model functions (GMFs) of sea surface wind and sea ice [21; 22]. [PERSON] et al. extended the Bayesian sea ice detection algorithm to QuikSCAT data [23], and the new results provided valuable information for the characterization of sea ice during the melting season. By comparison, the RL-N algorithm uses a 4-D hyperspace of measurement vectors, while the KNMI algorithm uses geophysical model functions of the sea ice and open seawater in the original space (i.e., the radar backscatter measurements at different polarization and azimuths). In addition, there have been increasing studies applying various machine learning algorithms to polar sea ice monitoring in recent years [24; 25; 26]. Corresponding algorithms are trained based on multi-parameter vectors and models are built for distinguishing sea ice from open seawater. The results all validate that machine learning has a promising ability to detect sea ice. To date, the space-borne scatterometers operate at either the C-band or the Ku-band. The viewing geometry (e.g., incidence angle, looking azimuth) of the scatterometer is also a major consideration in the application of sea ice monitoring. The C- and Ku-band scatterometers have their own advantages and shortcomings in monitoring polar sea ice. However, previous studies have used either C-band or Ku-band space-borne scatterometer measurements. The performance of sea ice and water discrimination carried out using multiple sources of active microwave measurements remains unexplored. Our study combinesa diversity of active microwave measurements in terms of radar frequency (both C and Ku), polarization, azimuth, and incidence angle. It is worth mentioning that the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided the unique opportunity to acquire a benchmark data set of multifrequency Ka-, Ku-, X-, C- and L-band in situ microwave scatterometer data ([[https://www.eumetsat.int/MOSAiC](https://www.eumetsat.int/MOSAiC)]([https://www.eumetsat.int/MOSAiC](https://www.eumetsat.int/MOSAiC)), accessed on 10 March 2023). The MOSAiC expedition aims to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. In this study, the radar backscatter measurements from multisource satellite scatterers, including the C-band ASCAT scatterometer on board the MetOp-A, MetOp-B, and MetOp-C satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT) will be jointly used in the detection of polar sea ice extent. This manuscript is structured as follows: In Section 2, the multisource scatterometer data and preprocessing are introduced. In Section 3, the selection and calculation of the characteristic parameters based on the multisource scatterometer data are presented. Then, the sea ice extent mapping and validation are presented in Section 4. Lastly, conclusions are drawn in Section 5.