###### Abstract
For a long time, optical remote sensing has been widely used, and is still very active and promising for crop growth monitoring and crop type classification. However, it is heavily limited by weather conditions, such as rain, cloud cover, haze and solar illumination. In particular, two-third of the Earth's surface is often covered by clouds throughout the year [11]. In addition, a lot of studies indicate that optical imagery can only characterize crops at critical crop growing stages [12; 13; 14], which further increases the risk of weather effects in practical applications. In contrast, synthetic aperture radar (SAR) operating with an active sensor at longer wavelengths that can penetrate cloud, haze and light rain, has the capability of working in all the time and almost all weather. Different from optical image measuring reflectance, SAR image obtains radar backscattering signals from the targets, which are very sensitive to structural attributes and dielectric properties. In agricultural areas, the geometric structures of crops, dielectric properties of the crop canopy and underlying background soil may vary significantly at varying crop phenological stages [15; 16]. This kind of crop-growth dynamic is crop-dependent. Additional dynamics happen during the periods of preparation and postharvest, depending on the farmers' management practices [5]. These dynamics cause challenges for single-date SAR applications, but provide unique information that may be helpful to improve results [5]. As a consequence, SAR time-series data are increasingly used for crop growth monitoring and crop type classification [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Since SAR polarization is very sensitive to structure and dielectric properties of the target, SAR backscattering is also polarization-dependent [28; 29]. In agricultural studies, HH polarization shows good capability in monitoring surface soil properties, VV polarization is better for observing information of vertical vegetation structure, and the cross polarizations (HV and VH) are good for capturing information of total canopy volume and plant biomass [7; 30]. Compared with single or dual-polarization SAR and polarimetric SAR (PolSAR) can provide richer information and are more sensitive to scattering mechanisms [28; 29]. With full-polarimetric SAR data, a lot of polarimetric features can be extracted, which can provide unique and helpful information for agricultural applications. For example, the polarimetric decomposition method can provide parameters highly related to scattering mechanisms, which were commonly used in previous studies. Seasonal patterns of these polarimetric features strongly depend on crop type and phenological stages, which provide very useful information for crop growth monitoring and crop type classification. As a result, analyses of temporal evolution in polarimetric parameters for different crop types are frequently used for crop monitoring studies [15; 31; 32; 33; 34]. In addition, time-series polarimetric parameters extracted by multitemporal PolSAR images are widely studied and favored for improving crop classification accuracy. Given these known benefits, however, the use of PolSAR time-series data have often been limited in real-world applications due to several reasons. On the one hand, the acquisition of a full growing season of PolSAR data is expensive and not always achievable. A large body of studies have been restricted to the use of single or dual-polarization modes for part of the growing season. On the other hand, the data in some cases are acquired using different beam modes with different spatial resolutions and incidence angles (e.g., RADARSAT-2 launched by the Canadian Space Agency). In particular, incidence angle has shown to affect the polarimetric parameters and classification performance [35; 36; 37].