ndmi¶

ndmi(nir, swir1)[source]¶

Normalized Difference Moisture Index (NDMI)

NDMI = (NIR - SWIR1) / (NIR + SWIR1)

Parameters:

nir (numpy.ndarray) – Near-infrared band.
swir1 (numpy.ndarray) – Short-wave infrared 1 band.

Returns:

NDMI values (-1 .. 1).

Return type:

numpy.ndarray

Overview¶

ndmi computes the Normalized Difference Moisture Index:

\[NDMI = \frac{NIR - SWIR1}{NIR + SWIR1}\]

It highlights canopy / vegetation moisture content. Values typically range from -1 to 1: - Near -1: very dry / bare areas - Around 0: moderate moisture or mixed surfaces - Higher positive values (> 0.2–0.4): moist, healthy vegetation / canopy water content

Usage¶

import numpy as np
from eo_processor import ndmi

nir   = np.array([0.52, 0.61, 0.44])
swir1 = np.array([0.28, 0.33, 0.30])

out = ndmi(nir, swir1)
print(out)  # element-wise (nir - swir1)/(nir + swir1)

Shapes & Dtypes¶

Supports 1D and 2D arrays in the public Python API (internally higher ranks may be dispatched via the normalized difference primitive but are not guaranteed).
Inputs may be any numeric dtype (int/uint/float); coerced to float64 internally.
Shapes of nir and swir1 must match exactly; mismatch raises ValueError.

Numerical Stability¶

Very small denominators are guarded with an EPSILON (1e-10). When nir + swir1 is ~0 the output is set to 0.0 to avoid instability.

Interpretation Notes¶

For time-series analysis, increases in NDMI can indicate post-rainfall recovery or phenological changes related to canopy moisture. Combine with NDVI/NBR to contextualize disturbance or recovery signals.