swectral.vegeind.vegeind_summary#

swectral.vegeind.vegeind_summary(spec_array, wavelength, axis=0)[source]#

Calculate a collection of common vegetation indices.

Parameters:

spec_array2D array_like, shape (n_samples, n_features): 2D array-like of 1D spectral data series.
wavelength1D array_like: Wavelengths in nanometer for the spectra data.
axisint, optional: Axis of spectral data series index, must be 0 or 1. If 0, each row represents a sample spectrum.
Lfloat or int: Soil influence adjustment parameter. The default is 0.5.

Returns:

pandas.DataFrame: Dataframe containing spectral indices of the samples.

Return type:

DataFrame

See also

ndvi: Normalized Difference Vegetation Index
sr: Simple ratio, simple ratio of R800 / R675
dvi: Difference Vegetation Index
evi: Enhanced Vegetation Index
gndvi: Green Normalized Difference Vegetation Index
cvi: Chlorophyll Vegetation Index
mcari: Modified Chlorophyll Absorption Reflectance Index
mtci: MERIS Terrestrial Chlorophyll Index
s2rep: Sentinel-2 Red Edge Position
tgi: Triangular Greenness Index
ndre: Normalized Difference Red Edge Index
lci: Leaf Chlorophyll Index
sr_nir_r: Simple Ratio NIR/Red
sr_nir_g: Simple Ratio NIR/Green
pssr: Pigment-specific Simple Ratio
pad: Derivative Pseudo-Absorption
padvi: Derivative Pseudo-Absorption Vegetation Indices
pri: Photochemical Reflectance Index
cci: Chlorophyll/Carotenoid Index
cri: Carotenoid Reflectance Indices
sipi: Structure Insensitive Pigment Index
wi: Water Index
nwi: Normalized Water Index
savis: Soil Adjusted Vegetation Indices
sasi1: Soil Adjusted Salinity Index 1
sasi2: Soil Adjusted Salinity Index 2
sasi3: Soil Adjusted Salinity Index 3
sasi4: Soil Adjusted Salinity Index 4
si: Salinity Indices
sr_salinity: Simple Ratio R750/R705
vog1: Vogelmann Red Edge Index
ari: Anthocyanin Reflectance Index
osavi: Optimized Soil Adjusted Salinity Index
msavi: Modified Soil Adjusted Salinity Index
swsi1: Salinity and Water Stress Index 1
swsi2: Salinity and Water Stress Index 2
swsi3: Salinity and Water Stress Index 3

Examples

Prepare demo spectral data:

>>> from specpipe.vegeind import create_vegeind_demo_data
>>> df_spec = create_vegeind_demo_data()

Summarize vegetation indices of the spectral data:

>>> vegeind_summary(df_spec, df_spec.columns)