MRS Lab Tools

A python based QGIS plugin

This plugin generates polarimetric descriptors (viz. vegetation indices, polarimetric decomposition parameters) from C3/T3/C2/T2 matrices obtained from PolSARpro The input data needs to be in PolSARpro/ENVI format (*.bin and *.hdr). It requires numpy, matplotlib python libraries pre-installed.

QGIS Python Plugin Repository: Click Here

Or access from QGIS Desktop > Plugin Manager > Search for ‘PolSAR tools’

Available Functionalities

  • Indices
    • Radar Vegetation Index (RVI) (Full-pol and dual-pol)
    • Generalized volume Radar Vegetation Index (GRVI)
    • Polarimetric Radar Vegetation Index (PRVI) (Full-pol and dual-pol)
    • Dual-pol Radar Vegetation Index (DpRVI)
    • Degree of Polarization (DOP) (Full-pol, dual-pol, and compact-pol)
    • Compact-pol Radar Vegetation Index (CpRVI)
  • Decompositions
    • Model free 3-Component decomposition for full-pol data (MF3CF)
    • Model free 4-Component decomposition for full-pol data (MF3CF)
    • Model free 3-Component decomposition for compact-pol data (MF3CC)
    • Improved S-Omega decomposition for compact-pol data (iS-Omega)

References

  • Chang, J.G., Shoshany, M. and Oh, Y., 2018. Polarimetric Radar Vegetation Index for Biomass Estimation in Desert Fringe Ecosystems. IEEE Transactions on Geoscience and Remote Sensing, 56(12), pp.7102-7108.
  • Ratha, D., Mandal, D., Kumar, V., McNairn, H., Bhattacharya, A. and Frery, A.C., 2019. A generalized volume scattering model-based vegetation index from polarimetric SAR data. IEEE Geoscience and Remote Sensing Letters, 16(11), pp.1791-1795.
  • Mandal, D., Kumar, V., Ratha, D., J. M. Lopez-Sanchez, A. Bhattacharya, H. McNairn, Y. S. Rao, and K. V. Ramana, 2020. Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data, Remote Sensing of Environment, 237: 111561.
  • Dey, S., Bhattacharya, A., Ratha, D., Mandal, D. and Frery, A.C., 2020. Target Characterization and Scattering Power Decomposition for Full and Compact Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing.
  • Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J.M., McNairn, H. and Rao, Y.S., 2020. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment, 247, p.111954.
  • Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y.S. and Frery, A.C., 2020. A Radar Vegetation Index for Crop Monitoring Using Compact Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 58 (9), pp. 6321-6335.
  • V. Kumar, D. Mandal, A. Bhattacharya, and Y. S. Rao, 2020. Crop Characterization Using an Improved Scattering Power Decomposition Technique for Compact Polarimetric SAR Data. International Journal of Applied Earth Observations and Geoinformation, 88: 102052.
  • S. Dey, A. Bhattacharya, A. C. Frery, C. Lopez-Martinez and Y. S. Rao, “A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.