Ground-penetrating radar-based automatic reconstruction of three-dimensional coarse root system architecture

Yuan Wu, Li Guo, Xihong Cui, Jin Chen, Xin Cao, Henry Lin

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Background and aims: The diverse functions of roots set requirements on specific root system architecture (RSA). Investigation on RSA holds potentials for studying the adaptation of plants to environmental stresses and to interspecific competitions. Ground-penetrating radar (GPR) has provided a non-invasive method for studying in situ RSA. However, previous GPR method relied on manually connecting root points detected between radargrams to restore each root branch, resulting in limited accuracy and efficiency of reconstructing RSA. The objective of this study is to improve the effectiveness of 3D RSA reconstruction using GPR root detection data.

Methods: A total of 213 coarse root sections (with diameter >0.5 cm) were extracted from a distribution map of a reference shrub (Arctostaphylos pungens) root system to simulate the coarse roots identified by GPR. An automatic method was established to trace each root point to its optimum growing source point. Connections between discrete root points recovered the topology of the reference RSA. A spline curve smoothing method was applied to restore the 3D morphology of the reference RSA. The proposed protocol was then tested to rebuild the 3D RSA of a shrub (Caragana microphylla) growing in the sandy soils after in situ GPR survey. The accuracy of RSA reconstruction was quantitatively evaluated by a relationship matrix method and qualitatively assessed by direct comparisons between the reconstructed and the actual RSAs after in situ excavation.

Results: For both simulated and field collected GPR detection datasets, the reconstructed RSAs strongly corresponded to the real topology of the actual root systems. When adapting the best strategy, 186 of the 213 (87.32 %) root points on the reference root system of A. pungens were interlinked with correct topology, and the relationship matrix method detected an overall similarity of 82.75 % between the reconstructed and the actual RSAs.

Conclusion: The proposed automatic RSA reconstruction method greatly enhances the interpretation of GPR detection data regarding coarse roots, making in situ non-invasive and long-term mapping and monitoring of RSA possible.

Original languageEnglish (US)
Pages (from-to)155-172
Number of pages18
JournalPlant and Soil
Issue number1-2
StatePublished - Oct 2014

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Plant Science


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