## 4 Discussion  
### Phenology Extraction Comparison between Backscattering Coefficients and Decomposition Parameters  
Compared with the most satisfactory retrieval accuracy given by previous studies, that is, [PERSON] et al. [6] used eigenvalue/vector decomposition parameters (entropy, anisotropy, and dominant alpha (\(\alpha_{1}\), alpha of the dominant scattering mechanism)) to achieve an overall success rate of 96%, our study achieved a lower overall accuracy of 86.2%. In order to investigate the retrieval accuracy of the method proposed by [PERSON] et al. [6] in this study area, three eigenvalue/vector decomposition parameters (entropy, anisotropy, and dominant alpha) were extracted using the Polarimetric SAR Data Processing and Educational Tool (PolSARpro) software ([[http://step.esa.int/main/download/](http://step.esa.int/main/download/)]([http://step.esa.int/main/download/](http://step.esa.int/main/download/))). The entropy (H) characterizes the randomness of the scattering processes. The anisotropy (A) characterizes the relative strengths of the second and third scattering mechanisms. The alpha angle (\(\alpha\)) defines the scattering mechanism for a given eigenvector [43; 44]. Dominant alpha (\(\alpha_{1}\)) is the alpha of the dominant scattering mechanism [6]. Figure 10: Evolution of observables (training dataset) provided by the eigenvalue/vector decomposition of the coherency matrix versus phenology, (**a**) Entropy, (**b**) Anisotropy, (**c**) Dominant alpha angle (\(\alpha_{1}\), alpha of the dominant scattering mechanism). Red lines represent possible division values to separate at least two interquartile ranges (grey part of boxes). Figure 9: Map of rice phenological phases spatial distribution on (**a**) 15 May, (**b**) 8 June, (**c**) 2 July, and (**d**) 26 July. Blue, green, yellow, and red colors, respectively, represent transplanting, vegetative, reproductive, and maturity phases of rice plants in the study area. For each extracted decomposition parameter, we used all pixels of a sample site for averaging. Again, all decomposition parameters were divided into four groups based on phenological phases identified in field observations (i.e., transplanting, vegetative, reproductive, and maturity). Three-quarters of each group were chosen for training. The rest in each group were used for validation, respectively. Distributions of decomposition parameters (training dataset) during each phenological phase are shown in the boxplots of Figure 10.  
A decision tree can be constructed using entropy, anisotropy, and dominant alpha (Figure 11), which is similar to the phenology retrieval algorithm designed by [PERSON] et al. (2016). The performance of the decision tree classifier is tested using the reserved validation data. A confusion table ([PERSON] et al., 2016) is built (Table 5). The method using eigenvalue/vector decomposition parameters achieved a higher overall accuracy (93.1%) than the method using backscattering coefficients (86.2%). Compared with the backscattering coefficients method, the decomposition parameters method had two fewer wrong estimates (Tables 4 and 5). The first case corresponded to #8 sample site on 8 June. The phenology identified in the ground observation was stage 32 (vegetative phase), but the backscattering coefficients method misclassified the rice plants at the transplanting phase. Plants at #8 sample site were the last to enter the transplanted phase. On 8 June, fillers and leaves were only partially developed, which resulted in relatively low VH backscattering coefficients (about \(-20\) dB). Scattering from the rough surface of the flooded ground made VV exhibit a relatively high value (about \(-12\) dB). Thus, rice plants at #8 sample site had similar VV/VH values with the transplanting ones. However, using decomposition parameters can correctly determine the phenology because low anisotropy (about 0.32) was observed due to the comparable contribution from surface scattering and double-bounce scattering, which were weaker than volume scattering during this time.