A Modified Triangle with SAR Target Parameters for Soil Texture Categorization Mapping

  • Shoba PeriasamyEmail author
  • Divya Senthil
  • Ramakrishnan S. Shanmugam
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


This research investigated soil texture information extraction in agricultural soil using SAR imagery of C band (5.36 GHz) frequency. The soil backscattering coefficient (\(\sigma_{soil}^{o}\)) could act as an effective estimator to the relative percentage of sand, silt, and clay when the influence of vegetation is considerably reduced from the total backscattering energy (\(\sigma_{total}^{o}\)). The contribution of vegetation in the SAR imageries of VV (\(\sigma_{vv}^{o}\)) and VH (\(\sigma_{vh}^{o}\)) polarization has been significantly reduced by Water Cloud Model, and Dual polarized SAR Vegetation Index. One of the target parameters, namely roughness (hrms), was derived from the cross-polarization ratio between \(\sigma_{vh - soil}^{o}\), and \(\sigma_{vv - soil}^{o}\) and Dielectric Constant (\(\varepsilon_{soil}^{{\prime }}\)) was obtained from the modified Dubois model. The extracted target parameter such as hrms is adequately correlated with in situ Sand texture measurements (R2 = 0.81) and, \(\varepsilon_{soil}^{{\prime }}\) was sufficiently correlated with in situ Clay measurements (R2 = 0.78). The positively correlated regions of the correlation coefficient (CC) analysis between hrms and \(\varepsilon_{soil}^{{\prime }}\) were extracted and thus represented the percentage of silt with reasonable accuracy (R2 = 0.77). From the soil triangle formed with three estimated parameters, we found that the Clay category shared around 36% of the total area followed by Sandy loam (24%) and loamy sand (19%).


Soil texture Soil backscattering Roughness Dielectric constant Correlation coefficient Soil triangle 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shoba Periasamy
    • 1
    Email author
  • Divya Senthil
    • 1
  • Ramakrishnan S. Shanmugam
    • 2
  1. 1.SRM Institute of Science and TechnologyKancheepuramIndia
  2. 2.Institute of Remote Sensing, Anna UniversityChennaiIndia

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