Specialized vegetation index using mid-infrared and near-infrared bands. Particularly effective during strong atmospheric disturbances and for vegetation vitality assessment.

Used in crop monitoring, forest monitoring, mineral exploration, and urban mapping.

When to use

  • Time-series monitoring of crop health, growth stages, and stress detection
  • Land cover classification and vegetation type discrimination
  • Biomass estimation and net primary productivity studies
  • Drought impact assessment over agricultural and forest areas
  • Phenology tracking — green-up, peak season, and senescence
  • Vegetation Vitality Assessment
  • Atmospheric Disturbance Correction

Limitations

  • Saturates in dense canopies (LAI > 3) — values plateau and lose discrimination ability
  • Sensitive to atmospheric scattering, especially blue-band haze
  • Soil background contaminates measurements in sparsely vegetated areas
  • Sun-sensor geometry (BRDF effects) introduces variability across acquisitions
  • Cloud cover and shadows produce invalid pixels that need masking

What the values mean

-1 Water / Snow
-0.1 Bare ground / Built-up
0.1 Sparse / Stressed
0.3 Moderate vegetation
0.5 Healthy vegetation
0.7 Dense canopy
Surface typeTypical ND MIR/NIR
Open water, snow-0.3 to -0.1
Bare soil, urban-0.1 to 0.2
Sparse or stressed crops0.2 to 0.4
Healthy crops, grassland0.4 to 0.7
Dense forest, peak season0.7 to 0.9

General Formula

NIR 800 nm
MIR 1300-3000 nm

Sensor-Specific Formulas

Most-used sensors — click to show code below

SensorProviderFormulaBand Mapping
ESA(B12 - B7) / (B12 + B7)NIR→B7, MIR→B12
MAXAR(SWIR3 - NIR1) / (SWIR3 + NIR1)NIR→NIR1, MIR→SWIR3

Spectral Band Visualization — Sentinel-2

Code Examples

Adapted for Sentinel-2 bands —

nd_mir_nir_sentinel-2.py

Frequently Asked Questions

What is the ND MIR/NIR (Normalized Difference MIR/NIR Vegetation Index) and when should I use it?

Specialized vegetation index using mid-infrared and near-infrared bands. Particularly effective during strong atmospheric disturbances and for vegetation vitality assessment. Vegetation indices quantify plant health, biomass, and photosynthetic activity by exploiting the contrast between how plants absorb visible light for photosynthesis and reflect near-infrared radiation from their cellular structure. ND MIR/NIR is particularly suited for vegetation vitality assessment, atmospheric disturbance correction, forest health monitoring. The general formula is (MIR - NIR) / (MIR + NIR), which requires NIR and MIR spectral bands.

Which satellite sensors can I use to calculate ND MIR/NIR?

ND MIR/NIR is supported by 3 satellite sensors in our database, including Sentinel-2, SuperView-2, WorldView 3. Each sensor uses different band designations — for example, Sentinel-2 uses the formula (B12 - B7) / (B12 + B7), while SuperView-2 uses (SWIR - NIR1) / (SWIR + NIR1). Select a sensor above to see its specific band mapping.

What spectral bands does ND MIR/NIR require and why?

ND MIR/NIR requires NIR (800 nm), MIR (1300-3000 nm). Vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared light from its mesophyll cell structure, making this contrast a reliable indicator of plant vigour.

How do I calculate ND MIR/NIR in Python or R?

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

How does ND MIR/NIR compare to NDVI and other vegetation indices?

While NDVI is the most common vegetation index, ND MIR/NIR provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.

ND MIR/NIR vs other vegetation indices

IndexNameHow it differs
ARIAnthocyanin Reflectance IndexAlternative vegetation index — different band combination
mARIModified Anthocyanin Reflectance IndexRefined formulation for specific conditions
ARVIAtmospherically Resistant Vegetation IndexAtmospherically corrected version
ARVI2Atmospherically Resistant Vegetation Index 2Atmospherically corrected version

Related Vegetation Indices

References

Rock et al. (1988)

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