Rice Paddy Fields Identification Based on Backscatter Features of Quad-Pol RADARSAT-2 Data and Simple Decision Tree Method

Microwave remote sensing is an important substitute for rice growth monitoring in cloudy area. Fields identification is the most basic task for tracking rice crop cultivation. A series of sophisticated classification methods such as machine learning techniques have been applied to mapping crop planting area. However, these methods are weak in interpreting the physical mechanism of identifying crop fields, and hardly illustrate the backscattering difference between rice fields and other ground objects. Besides, enormous training samples and computational expense are demanded to achieve high recognition accuracy, which limited their application to rapid and large-area agricultural monitoring. In this study, C-band quad-pol backscattering coefficients of different land cover types were extracted from RADARSAT-2 PolSAR (Polarimetric Synthetic Aperture Radar) data during the reproductive period of rice fields. The backscatter statistical characteristics of rice fields and other ground types in different polarimetric bands were visualized and analyzed using boxplots. Based on the scatter performance of the five interested ground objects, a simple decision tree scheme was proposed to identify rice paddy fields. The overall accuracy is 88.65% and Kappa coefficient is 0.77. Meanwhile, a land cover classification map of the study area was obtained though the hierarchical judgement of decision tree strategy.