###### Abstract  
Multimemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel \(\rho_{\rm{HHVV}}\), and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.0%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research. 1  
<ccs2021> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept> <concept <concept> <concept <concept> <concept <concept> <concept <concept> <concept <concept> <concept <concept> <concept <concept <concept> <concept <concept <concept <concept <<concept <concept <<concept <distribution of crops, and their temporal variation throughout the growing season, plays an essential role in the sustainable management and development of agricultural practice, crop biophysical and biochemical variable estimation, crop yield prediction, evaluation of ecosystem services and food security [3; 4; 5; 6]. The ground survey method to obtain this vital information is usually time-consuming, labour intensive and expensive [7]. Furthermore, the collected data often show inconsistencies between regions, or even countries, and intercomparison is hard due to the different ground field survey methods adopted [8]. As known, land coverage in agricultural areas usually experiences apparent variations even within relatively short time intervals due to multiple factors, such as climate conditions, soil properties and farmer's decisions [9]. Therefore, traditional field surveys are hard to fulfill the growing demand of routine crop monitoring. As a cost-effective and advanced method of supplementing or even substituting field surveys, remote sensing has proven its ability to capture agricultural land use and crop growth dynamics with high spatial and spectral resolution over large areas in a timely and time-dependent fashion [10].