Advertisement

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)

Abstract

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%).

Keywords

Soil texture Soil backscattering Roughness Dielectric constant Correlation coefficient Soil triangle 

References

  1. 1.
    Wang, D.-C., Zhang, G.-L., Zhao, M.-S., Pan, X.-Z., Zhao, Y.-G., Li, D.-C., Macmillan, B.: Retrieval and mapping of soil texture based on land surface diurnal temperature range data from MODIS. PLoS One 10(6), 1–14 (2007)Google Scholar
  2. 2.
    Zhang, X., Zhang, X., Li, G.: The effect of texture and irrigation on the soil moisture vertical-temporal variability in an urban artificial landscape: A case study of Olympic Forest Park in Beijing. Front. Environ. Sci. Eng. 9, 269–278 (2015)CrossRefGoogle Scholar
  3. 3.
    Prevot, L., Dechambre, M., Taconet, O., Vidal-Madjar, D., Normand, M., Gallej, S.: Estimating the characteristics of vegetation canopies with airborne radar measurements. Int. J. Remote Sens. 14(15), 2803–2818 (1993)CrossRefGoogle Scholar
  4. 4.
    Dubois, P.C., Van Zyl, J., Engman, T.: Measuring soil moisture with imaging radar. IEEE Trans. Geosci. Remote Sens. 33(4), 915–926 (1995)CrossRefGoogle Scholar

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

Personalised recommendations