SWIR-enhanced Near-Infrared Reflectance of Vegetation for NDPI - A spectral index for vegetation applications.

Used in crop monitoring.

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

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 sNIRvNDPI
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

N 850
R 650
S2 2190

Sensor-Specific Formulas

Most-used sensors — click to show code below

SensorProviderFormulaBand Mapping
USGS/NASA(B5 - (alpha * B4 + (1.0 - alpha) * B7))/(B5 + (alpha * B4 + (1.0 - alpha) * B7)) * B5N→B5, R→B4, S2→B7
ESA(B8 - (alpha * B4 + (1.0 - alpha) * B12))/(B8 + (alpha * B4 + (1.0 - alpha) * B12)) * B8N→B8, R→B4, S2→B12
MAXAR(NIR1 - (alpha * Red + (1.0 - alpha) * SWIR6))/(NIR1 + (alpha * Red + (1.0 - alpha) * SWIR6)) * NIR1N→NIR1, R→Red, S2→SWIR6

Spectral Band Visualization — Landsat 8/9

Code Examples

Adapted for Landsat 8/9 bands —

snirvndpi_landsat-8-9.py

Frequently Asked Questions

What is the sNIRvNDPI (SWIR-enhanced Near-Infrared Reflectance of Vegetation for NDPI) and when should I use it?

SWIR-enhanced Near-Infrared Reflectance of Vegetation for NDPI - A spectral index for vegetation applications. 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. sNIRvNDPI is particularly suited for vegetation. The general formula is (N - (alpha * R + (1.0 - alpha) * S2))/(N + (alpha * R + (1.0 - alpha) * S2)) * N, which requires N and R and S2 spectral bands.

Which satellite sensors can I use to calculate sNIRvNDPI?

sNIRvNDPI is supported by 3 satellite sensors in our database, including Landsat 8/9, Sentinel-2, WorldView 3. Each sensor uses different band designations — for example, Landsat 8/9 uses the formula (B5 - (alpha * B4 + (1.0 - alpha) * B7))/(B5 + (alpha * B4 + (1.0 - alpha) * B7)) * B5, while Sentinel-2 uses (B8 - (alpha * B4 + (1.0 - alpha) * B12))/(B8 + (alpha * B4 + (1.0 - alpha) * B12)) * B8. Select a sensor above to see its specific band mapping.

What spectral bands does sNIRvNDPI require and why?

sNIRvNDPI requires N (850), R (650), S2 (2190). 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 sNIRvNDPI 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 sNIRvNDPI compare to NDVI and other vegetation indices?

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

sNIRvNDPI 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

https://doi.org/10.1029/2024JG008240

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