Rice Growth Monitoring by Means of X-Band Co-polar SAR: Feature Clustering and BBCH Scale

Yuzugullu O., Erten E., HAJNSEK I.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol.12, no.6, pp.1218-1222, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 6
  • Publication Date: 2015
  • Doi Number: 10.1109/lgrs.2015.2388953
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1218-1222
  • Keywords: Agriculture, Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale, feature-clustering, heterogeneity, monitoring, polarimetry, rice phenology, synthetic aperture radar (SAR), X-band
  • Istanbul Technical University Affiliated: Yes


Precision agriculture research, which aims to monitor agricultural fields and to manage agricultural practice by considering overall environmental impacts, has gained momentum with the recent improvements in the remote sensing area. The objective of this letter, as a part of precision farming, is to implement Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale assignment in plant growth monitoring by means of SAR. The proposed approach copes with structural heterogeneity in agricultural fields by grouping together similar morphologies. For this, densely cultivated paddy rice fields are analyzed using TerraSAR-X (TSX) co-polar SAR data. For generating structurally similar groups, K-means clustering is used in a polarimetric feature vector space, which is composed of backscattering intensities and polarimetric phase differences. This step is followed by a preliminary classification approach based on the temporal separability of the explanatory parameters. In the last step of the proposed methodology, assigned classes are updated based on the biological principles that are followed in rice cultivation. This letter provides the results of the proposed algorithm and compares them to the standard threshold-based approach in two independent agricultural areas. The results show the superiority of the feature-clustering-based classification compared with the standard approach in handling field heterogeneity.