A vegetation index — 2 compatible sensors — Last revised June 1, 2025
PRI

Photochemical Reflectance Index

The Photochemical Reflectance Index (PRI) was developed by Gamon, Penuelas, and Field (1992) to track diurnal changes in photosynthetic efficiency. PRI detects changes in xanthophyll cycle pigments that occur during plant stress, providing a measure of light use efficiency and general ecosystem health through remote sensing.

Used in crop monitoring, forest monitoring, and water detection.

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
  • photosynthetic efficiency monitoring
  • plant stress detection

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 PRI
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

531 531
570 570

Sensor-Specific Formulas

Most-used sensors — click to show code below

SensorProviderFormulaBand Mapping
Dragonette-1
Wyvern(Band 4 - Band 6) / (Band 4 + Band 6)531→Band 4, 570→Band 6

Spectral Band Visualization — Dragonette-1

Code Examples

Adapted for Dragonette-1 bands —

pri_standard_dragonette-001.py

Frequently Asked Questions

What is the PRI (Photochemical Reflectance Index) and when should I use it?

The Photochemical Reflectance Index (PRI) was developed by Gamon, Penuelas, and Field (1992) to track diurnal changes in photosynthetic efficiency. PRI detects changes in xanthophyll cycle pigments that occur during plant stress, providing a measure of light use efficiency and general ecosystem health through remote sensing. 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. PRI is particularly suited for photosynthetic efficiency monitoring, plant stress detection, water stress assessment. The general formula is (531nm - 570nm) / (531nm + 570nm), which requires 531 and 570 spectral bands.

Which satellite sensors can I use to calculate PRI?

PRI is supported by 2 satellite sensors in our database, including Dragonette-1, Dragonette-2/3. Each sensor uses different band designations — for example, Dragonette-1 uses the formula (Band 4 - Band 6) / (Band 4 + Band 6), while Dragonette-2/3 uses (Band8 - Band10) / (Band8 + Band10). Select a sensor above to see its specific band mapping.

What spectral bands does PRI require and why?

PRI requires 531 (531), 570 (570). 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 PRI 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 PRI compare to NDVI and other vegetation indices?

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

PRI 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

ARI
Anthocyanin Reflectance Index
mARI
Modified Anthocyanin Reflectance Index
ARVI
Atmospherically Resistant Vegetation Index
ARVI2
Atmospherically Resistant Vegetation Index 2
BGI
Blue Green Pigment Index
BNDVI
Blue Normalized Difference Vegetation Index
bNIRv
Blue Near-Infrared Reflectance of Vegetation
BRI
Blue Red Pigment Index

References

Gamon, J.A., Penuelas, J., and Field, C.B. (1992) - A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35-44
Gamon, J.A., Serrano, L., and Surfus, J.S. (1997) - The photochemical reflectance index: an optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4), 492-501

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