Random Forest Classification of Rice Planting Area Using Multi-Temporal Polarimetric Radarsat-2 Data

Rice is one of the most important crops in the world. Rice usually grows in tropical and subtropical areas where are usually rainy and cloudy. Under this weather condition, optical remote sensing has a great limitation, but the synthetic aperture radar (SAR) runs well. This paper utilized C band multi-temporal Radarsat-2 data, which is acquired in 2016, to extract quad-polarized backscattering coefficients, Cloude-Pottier and Freeman-Durden decomposition parameters for establishing classification features. Random forest (RF) algorithm was employed as the classifier. The Overall Accuracy (OA) is 82.9% when only the data on May 15th was used. After adding the data on July 26th, the OA increased to 88.8%. Then, the variable importance was estimated by using out-of-bag (OOB) data. The results indicate that using RF would effectively identify the ground objects in the rice planting area.