xrspatial.multispectral.gci#
- xrspatial.multispectral.gci(nir_agg: xarray.core.dataarray.DataArray, green_agg: xarray.core.dataarray.DataArray, name='gci')[source]#
Computes Green Chlorophyll Index. Used to estimate the content of leaf chorophyll and predict the physiological state of vegetation and plant health.
- Parameters
nir_agg (xr.DataArray) – 2D array of near-infrared band data.
green_agg (xr.DataArray) – 2D array of green band data.
name (str, default='gci') – Name of output DataArray.
- Returns
gci_agg – 2D array of gci values. All other input attributes are preserved.
- Return type
xarray.DataArray of the same type as inputs
References
Examples
>>> from xrspatial.datasets import get_data >>> data = get_data('sentinel-2') # Open Example Data >>> nir = data['NIR'] >>> green = data['Green'] >>> from xrspatial.multispectral import gci >>> # Generate GCI Aggregate Array >>> gci_agg = gci(nir_agg=nir, green_agg=green) >>> nir.plot(aspect=2, size=4) >>> green.plot(aspect=2, size=4) >>> gci_agg.plot(aspect=2, size=4)
>>> y1, x1, y2, x2 = 100, 100, 103, 104 >>> print(nir[y1:y2, x1:x2].data) [[1519. 1504. 1530. 1589.] [1491. 1473. 1542. 1609.] [1479. 1461. 1592. 1653.]] >>> print(green[y1:y2, x1:x2].data]) [[1120. 1130. 1157. 1191.] [1111. 1137. 1190. 1221.] [1097. 1139. 1228. 1216.]] >>> print(gci_agg[y1:y2, x1:x2].data) [[0.35625 0.33097345 0.3223855 0.33417296] [0.3420342 0.29551452 0.29579833 0.31777233] [0.34822243 0.28270411 0.29641694 0.359375 ]]