Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images

Kucuk C., TASKIN G., Erten E.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol.9, no.6, pp.2509-2519, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 6
  • Publication Date: 2016
  • Doi Number: 10.1109/jstars.2016.2547843
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2509-2519
  • Keywords: Agriculture, classification, support vector machines (SVM), synthetic aperture radar (SAR), TerraSAR-X, RESOLUTION SATELLITE SAR, BACKSCATTERING COEFFICIENTS, BIOPHYSICAL VARIABLES, TIME-SERIES, VEGETATION, SELECTION
  • Istanbul Technical University Affiliated: Yes


Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Satellites with synthetic aperture radar (SAR) sensor are highly preferred on such studies because of not only their day/night and all weather acquisition capabilities but also their ability to detect small morphological changes in monitored target, regarding the wavelength of signals. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. This paper focused on building a feasible phenology classification schema for paddy-rice using multitemporal co-polar TerraSAR-X images. Phenology classification was conducted with support vector machines (SVM) with linear and nonlinear kernel, k-nearest neighbors (kNN), and decision trees (DT). The key implementation challenges such as the number of classes, the identification of the boundaries of the classes, and the selection of textural and polarimetric features were deeply analyzed. According to all the evaluations conducted, the classification schema was finalized to be used for obtaining thematic maps for two independent rice-cultivated agricultural areas located in Spain and Turkey. The results of these experiments enable one to draw a conclusion about feasibility of machine learning (ML) algorithms in operational phenology monitoring.