Analyzing the Accuracy of Mapping Paddy Rice Using Polarimetric Decomposition Parameters and Backscatter Intensity

Polarimetric decomposition parameters are crucial for characterizing ground objects by their unique scattering mechanisms, particularly in crop monitoring and specifically for mapping paddy rice fields. This study evaluates their impact on mapping accuracy by deriving entropy (H), anisotropy (A), and mean alpha angle (a) from a multi-temporal Sentinel-1 dataset. These parameters served as input features for training a random forest classifier to delineate rice-growing areas. Additionally, another random forest classifier was trained using backscatter intensity from the VH (Vertical Transmit, Horizontal Receive) polarization channel to provide a comparative map, enhancing the evaluation of the study outcomes. Adjustments to the temporal resolution of the input dataset were necessary due to the computational constraints of Google Earth Engine (GEE), the cloud-based platform used in this study. Despite the reduced temporal resolution, the classifier trained with polarimetric decomposition parameters performed comparably to the classifier using the full temporal resolution of backscatter intensity. The classifier utilizing backscatter intensity reported slightly higher overall accuracy and kappa coefficient, with values of 0.8460 and 0.6920, respectively. The classifier using polarimetric decomposition parameters achieved an overall accuracy of 0.8250 and a kappa coefficient of 0.6499. The performance of the two classifiers varied with respect to the calculated producer's and user's accuracies, though the differences were not significant. These results suggest that entropy (H), anisotropy (A), and mean alpha angle (a) effectively capture the distinct scattering properties of rice throughout its growth stages, as well as those of other crops and land cover types, enabling reliable rice classification. Future research could explore additional parameters and their combinations to further enhance mapping accuracy.