xrspatial.multispectral.ndmi#
- xrspatial.multispectral.ndmi(nir_agg: xarray.core.dataarray.DataArray, swir1_agg: xarray.core.dataarray.DataArray, name='ndmi')[source]#
Computes Normalized Difference Moisture Index. Used to determine vegetation water content.
- Parameters
nir_agg (xr.DataArray) – 2D array of near-infrared band data. (Landsat 4-7: Band 4) (Landsat 8: Band 5)
swir1_agg (xr.DataArray) – 2D array of shortwave infrared band. (Landsat 4-7: Band 5) (Landsat 8: Band 6)
name (str, default='ndmi') – Name of output DataArray.
- Returns
ndmi_agg – 2D array of ndmi values. All other input attributes are preserved.
- Return type
xr.DataArray of same type as inputs
References
Examples
>>> from xrspatial.datasets import get_data >>> data = get_data('sentinel-2') # Open Example Data >>> nir = data['NIR'] >>> swir1 = data['SWIR1'] >>> from xrspatial.multispectral import ndmi >>> # Generate NDMI Aggregate Array >>> ndmi_agg = ndmi(nir_agg=nir, swir1_agg=swir1) >>> nir.plot(aspect=2, size=4) >>> swir1.plot(aspect=2, size=4) >>> ndmi_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(swir1[y1:y2, x1:x2].data) [[2092. 2242. 2333. 2382.] [2017. 2150. 2303. 2344.] [2124. 2244. 2367. 2452.]] >>> print(ndmi_agg[y1:y2, x1:x2].data) [[-0.15868181 -0.19701014 -0.20786953 -0.1996978 ] [-0.149943 -0.18686172 -0.19791937 -0.18593474] [-0.17901748 -0.21133603 -0.19575651 -0.19464068]]