###### Abstract
Although the revisit time of TerraSAR-X (11 days) and Sentinel-1 (six or 12 days) is shorter than that of RADARSAT-2, whose revisit period is 24 days, RADARSAT-2 can still observe a rice field at least four times during the typical cultivation cycle of rice [6; 9]. Because of the different cultivation management (e.g., transplanting time and fertilization) and rice varieties in individual fields, the pace of rice phenology evolution varies at field-by-field levels [11]. Thus, it is acceptable to observe the fields in each of the four principle phenological phases (i.e., transplanting, vegetative, reproductive, and maturity) once in 24 days or longer. Besides, RADARSAT-2 provides quad-polarization (VH, VV, HH, HV) data, and is very effective in retrieving rice canopy parameters and estimating biomass that are linked to the crop yield [30; 32]. As a result, the sensor has become an ideal data source for rice phenology monitoring. [PERSON] et al. [6] analyzed RADARSAT-2 data and proposed a retrieval algorithm with radar target decomposition and compact polarimetric (CP) variables. [PERSON] et al. [27] found that the combination of the alpha angle and beta angle derived from the Cloude-Pottier decomposition of RADARSAT-2 data provided satisfactory estimations of the crop phenology. [PERSON] et al. [9] used RADARSAT-2 data to simulate the CP SAR data for identifying seven phenological phases of rice fields. However, most of these studies require various indirect parameters like the CP and target decomposition parameters. The retrieval processes are generally complex including filtering and image classification that may create new uncertainties in data analysis. Of the SAR polarimetric parameters (i.e., amplitude and phase data), the backscattering coefficient is proportional to the square of amplitude and is the most basic one [5]. Many researches have demonstrated that the rice backscattering coefficient alone is sensitive to plant height, biomass, and leaf area index (LAI) [19; 20; 33; 34; 35; 36; 37]. These plant parameters are highly correlated with rice phenology development, showing a great potential to monitor rice phenology. However, the observed correlation between rice biophysical variables and certain polarizations [25; 30] can be temporally unstable. Thisis caused by changes of the electromagnetic interactions between radar waves and rice scattering components as the plant grows and different polarization responses to the changes [20]. [PERSON] et al. [31] used multi-temporal, quad-polarization RADARSAT-2 data to establish empirical regression models to link rice biophysical parameters to backscattering coefficients. The optimal polarization to estimate biophysical variables was a function of different stages, which suggests a piecewise fitting strategy using different polarizations at different growing stages. As rice plants develop during a growing season and biophysical variables change during different phenological phases, \(\sigma^{0}\) acquired at different phases should be clustered in the domains of polarizations and/or combinations of polarizations, making the decision tree strategy with particular thresholds theoretically feasible for the separation of phenological phases. Therefore, the objective of this study is to use only radar backscattering coefficients and their combinations derived from multi-temporal and full-polarized RADARSAT-2 datasets for rice phenology monitoring. The focus of the study is to utilize a simple decision tree model for the characterization of rice phenology in the feature space of backscattering coefficients of multi-temporal RADARSAT-2 datasets.