Compact-pol Radar Vegetation Index (cprvi)#

polsartools.cprvi(infolder, chi_in=45, psi_in=0, window_size=1, outType='tif', cog_flag=False, cog_overviews=[2, 4, 8, 16], write_flag=True, max_workers=None, block_size=(512, 512), progress_callback=None)[source]

Compute compact-pol Radar Vegetation Index (CpRVI) from C2 matrix data.

This function processes compact-polarimetric SAR data to generate the CP-RVI, which is useful for vegetation monitoring and biomass estimation using compact-pol SAR systems. The processing is done in parallel blocks for improved performance.

Examples

>>> # Basic usage with default parameters (right circular transmission)
>>> cprvi("/path/to/cp_data")

>>> # Custom parameters for left circular transmission
>>> cprvi(
...     infolder="/path/to/cp_data",
...     chi_in=-45,
...     psi_in=0,
...     window_size=3,
...     outType="tif",
...     cog_flag=True
... )
Parameters:

  • infolder (str) – Path to the input folder containing compact-pol C2 matrix files.

  • chi_in (float, default=45) – Ellipticity angle chi of the transmitted wave in degrees. For circular polarization, chi = 45° (right circular) or -45° (left circular).

  • psi_in (float, default=0) – Orientation angle psi of the transmitted wave in degrees. For circular polarization, typically 0°.

  • window_size (int, default=1) – Size of the spatial averaging window. Larger windows reduce speckle noise but decrease spatial resolution.

  • outType ({'tif', 'bin'}, default='tif') – Output file format: - ‘tif’: GeoTIFF format with georeferencing information - ‘bin’: Raw binary format

  • cog_flag (bool, default=False) – If True, creates a Cloud Optimized GeoTIFF (COG) with internal tiling and overviews for efficient web access.

  • cog_overviews (list[int], default=[2, 4, 8, 16]) – Overview levels for COG creation. Each number represents the decimation factor for that overview level.

  • write_flag (bool, default=True) – If True, writes results to disk. If False, only processes data in memory.

  • max_workers (int | None, default=None) – Maximum number of parallel processing workers. If None, uses CPU count - 1 workers.

  • block_size (tuple[int, int], default=(512, 512)) – Size of processing blocks (rows, cols) for parallel computation. Larger blocks use more memory but may be more efficient.

Returns:

Results are written to disk as either ‘cprvi.tif’ or ‘cprvi.bin’ in the input folder.

Return type:

None

The formulation of the CpRVI is as follows:

\[\begin{split}\text{CpRVI}=\left(1-\dfrac{3}{2}\text{GD}_{\text{ID}}\right)\Big(\frac{p}{q}\Big)^{2(\frac{3}{2}\text{GD}_{\text{ID}})}\\ p=\text{min\{SC,OC\}},q=\text{max\{SC,OC\}}\\ \text{SC}=\frac{S_0-S_3}{2};\qquad{}\text{OC}=\frac{S_0+S_3}{2};\\ S_0=\text{C11+C22};\qquad{}S_1=\text{C11-C22};\\ S_2=\text{C12+C21};\qquad{}S_3=\pm\text{j(C12-C21)}\\\end{split}\]

where, \(\text{GD}_\text{ID}\) is the geodesic distance between Kennaugh matrices (\(\mathbf{K}\)) of the observed and the ideal depolarizer, \(p, q\) are minimum and maximum values of \(\text{SC}\) and \(\text{OC}\) which are functions of stocks parameters (\(S_0\), \(S_1\), \(S_2\), and \(S_3\)). A detailed explanation of CpRVI is available in [[6]](#6).

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