xrspatial.multispectral.sipi

xrspatial.multispectral.sipi(nir_agg: xarray.core.dataarray.DataArray, red_agg: xarray.core.dataarray.DataArray, blue_agg: xarray.core.dataarray.DataArray, name='sipi')

Computes Structure Insensitive Pigment Index which helpful in early disease detection in vegetation.

Parameters

• nir_agg (xr.DataArray) – 2D array of near-infrared band data.

• red_agg (xr.DataArray) – 2D array of red band data.

• blue_agg (xr.DataArray) – 2D array of blue band data.

• name (str, default='sipi') – Name of output DataArray.

Returns

sipi_agg – 2D array of sipi values. All other input attributes are preserved.

Return type

xr.DataArray of same type as inputs

References

• Wikipedia: https://en.wikipedia.org/wiki/Enhanced_vegetation_index

Examples

>>> from xrspatial.datasets import get_data
>>> data = get_data('sentinel-2')  # Open Example Data
>>> nir = data['NIR']
>>> red = data['Red']
>>> blue = data['Blue']
>>> from xrspatial.multispectral import sipi
>>> # Generate SIPI Aggregate Array
>>> sipi_agg = sipi(nir_agg=nir, red_agg=red, blue_agg=blue)
>>> nir.plot(cmap='Greys', aspect=2, size=4)
>>> red.plot(aspect=2, size=4)
>>> blue.plot(aspect=2, size=4)
>>> sipi_agg.plot(aspect=2, size=4)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

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>>> 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(red[y1:y2, x1:x2].data)
[[1327. 1329. 1363. 1392.]
 [1309. 1331. 1423. 1424.]
 [1293. 1337. 1455. 1414.]]
>>> print(blue[y1:y2, x1:x2].data)
[[1281. 1270. 1254. 1297.]
 [1241. 1249. 1280. 1309.]
 [1239. 1257. 1322. 1329.]]
>>> print(sipi_agg[y1:y2, x1:x2].data)
[[1.2395834 1.3371428 1.6526946 1.4822335]
 [1.3736264 1.5774648 2.2016807 1.6216216]
 [1.2903225 1.6451613 1.9708029 1.3556485]]

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