## 5 Conclusions  
This study presents a comprehensive evaluation and demonstration of crop growth monitoring and crop type classification over an agricultural area in Southwestern Ontario, Canada, based on time series of polarimetric RADARSAT-2 C-band images acquired in the same beam mode across the full growing season. A set of 27 representative polarimetric observables was selected and analyzed. Temporal evolution studies showed that the backscattering coefficients in cross-pol (HV), Pauli second channel (HH-VV), and HV/VV ratio, polarimetric decomposition parameters, RVI were found to be the most sensitive observables to crop growth. By using the well-known Random Forest (RF) algorithm, the classification results using single groups of polarimetric observables showed that polarimetric decomposition methods (ND, FD, and CP), backscattering coefficients in Pauli and linear polarimetric channels and correlation coefficients produced the best classification accuracies, with OAs greater than 87%. The forward selection procedure for pursuing optimal classification accuracy from different perspectives was expanded to find optimal combinations of polarimetric observables, or SAR images, or both. The resultsshowed that fewer polarimetric observables, or fewer images on certain critical dates, could produce better overall accuracies for crop classification. The best result was achieved using an optimal combination of polarimetric observables and SAR images (including eight groups and six images), with an OA of 94.04%. This suggests that an optimal combination considering both perspectives (i.e., numbers of images and polarimetric observables) is valuable and could serve as a guideline, which is transferable for crop classification in practice. The following are available online at [[https://www.mdpi.com/article/10.3390/rs13071394/s1](https://www.mdpi.com/article/10.3390/rs13071394/s1)]([https://www.mdpi.com/article/10.3390/rs13071394/s1](https://www.mdpi.com/article/10.3390/rs13071394/s1)), Table S1: The values of three main crops (corn, soybean, and winter wheat) presented in Figure 5; Table S2: The values of three main crops (corn, soybean, and winter wheat) presented in Figure 6; Table S3: The values of three main crops (corn, soybean, and winter wheat) presented in Figure 7; Table S4: The values of three main crops (corn, soybean, and winter wheat) presented in Figure 8.  
Conceptualization, Q.X. and K.L. ; methodology, Q.X. and K.L. ; software, Q.X. and K.L. ; validation, Q.X. and K.L. ; formal analysis, Q.X., J.W. and H.F.; investigation, Q.X., K.L. and J.W. ; resources, J.W. ; data curation, Q.X., J.W., C.L. and J.S. ; writing--original draft preparation, [PERSON] and K.L. ; writing--review and editing, [PERSON], [PERSON]-S., [PERSON], [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]-S. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Natural Science Foundation of China (Grant No. 41,804,004, 41,820,104,005, 41,531,068, 41,904,004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P.  
Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. RADARSAT-2 Data and Products (c) MacDonald, Dettwiler and Associates Ltd. (2015)--All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency. The authors would like to thank the Canadian Space Agency for providing the RADARSAT-2 data, and [PERSON], [PERSON], [PERSON] from GITA lab, UWO for their help with field work.