A modified simple ratio vegetation index optimized for boreal forest applications. It normalizes the difference between NIR and red reflectance, providing improved LAI estimation in forest environments.

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

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
  • Boreal forest monitoring
  • LAI estimation in forests

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 MSR670
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

red 670
nir 800

Sensor-Specific Formulas

Most-used sensors — click to show code below

SensorProviderFormulaBand Mapping
21AT(NIR - Red) / (NIR + Red)red→Red, nir→NIR
CG Satellite(NIR - Red) / (NIR + Red)red→Red, nir→NIR
ESA(B7 - B4) / (B7 + B4)red→B4, nir→B7
MAXAR(NIR1 - Red) / (NIR1 + Red)red→Red, nir→NIR1
MAXAR(NIR1 - Red) / (NIR1 + Red)red→Red, nir→NIR1

Spectral Band Visualization — BJ3A

Code Examples

Adapted for BJ3A bands —

msr670_bj3a.py

Frequently Asked Questions

What is the MSR670 (Modified Simple Ratio 670,800) and when should I use it?

A modified simple ratio vegetation index optimized for boreal forest applications. It normalizes the difference between NIR and red reflectance, providing improved LAI estimation in forest environments. 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. MSR670 is particularly suited for boreal forest monitoring, lai estimation in forests, forest canopy analysis. The general formula is (nir - red) / (nir + red), which requires red and nir spectral bands.

Which satellite sensors can I use to calculate MSR670?

MSR670 is supported by 21 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-1, Dragonette-2/3, Gaofen-1 and 16 more. Each sensor uses different band designations — for example, BJ3A uses the formula (NIR - Red) / (NIR + Red), while BJ3N uses (NIR - Red) / (NIR + Red). Select a sensor above to see its specific band mapping.

What spectral bands does MSR670 require and why?

MSR670 requires red (670), nir (800). 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 MSR670 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 MSR670 compare to NDVI and other vegetation indices?

While NDVI is the most common vegetation index, MSR670 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.

MSR670 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

Chen (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications.
Haboudane et al. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies.
Wu et al. (2008). Estimating chlorophyll content from hyperspectral vegetation indices.

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