Mapping Rice Planting Area Using Multi-Temporal Quad-Pol Radarsat-2 Datasets and Random Forest Algorithm

Accurate mapping of paddy rice planting area is important for rice yield prediction. Polarimetric synthetic aperture radar (PolSAR) data is suitable for rice growth monitoring in cloudy and foggy weather. The potential of using quad-pol PolSAR parameters for rice area mapping needs exploration in detail. In this study, multi-temporal C-band quad-pol RADARSAT-2 datasets and random forest classification algorithm were used to identify the rice planting area in Meishan City, China in 2016. The backscatter coefficients, polarimetric variables and decomposition parameters were extracted and divided into groups as the inputs of random forest classifier. The decomposition parameters derived from single-day PolSAR data during the rice reproductive period were found the most practicable for rice area mapping, with the overall accuracy of 95.94% and kappa coefficient of 0.92.