TCARI/OSAVI Ratio (705 and 750 nm) 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 TCARIOSAVI705
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

RE2 730-745 nm
RE1 700-710 nm
G 520-600 nm

Sensor-Specific Formulas

Most-used sensors — click to show code below

SensorProviderFormulaBand Mapping
Wyvern(3 * ((Band 19 - Band 16) - 0.2 * (Band 19 - Band 6) * (Band 19 / Band 16))) / (1.16 * (Band 19 - Band 16) / (Band 19 + Band 16 + 0.16))RE2→Band 19, RE1→Band 16, G→Band 6
ESA(3 * ((B6 - B5) - 0.2 * (B6 - B3) * (B6 / B5))) / (1.16 * (B6 - B5) / (B6 + B5 + 0.16))RE2→B6, RE1→B5, G→B3

Spectral Band Visualization — Dragonette-1

Code Examples

Adapted for Dragonette-1 bands —

tcariosavi705_dragonette-001.py

Frequently Asked Questions

What is the TCARIOSAVI705 (TCARI/OSAVI Ratio (705 and 750 nm)) and when should I use it?

TCARI/OSAVI Ratio (705 and 750 nm) 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. TCARIOSAVI705 is particularly suited for vegetation. The general formula is (3 * ((RE2 - RE1) - 0.2 * (RE2 - G) * (RE2 / RE1))) / (1.16 * (RE2 - RE1) / (RE2 + RE1 + 0.16)), which requires RE2 and RE1 and G spectral bands.

Which satellite sensors can I use to calculate TCARIOSAVI705?

TCARIOSAVI705 is supported by 3 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Sentinel-2. Each sensor uses different band designations — for example, Dragonette-1 uses the formula (3 * ((Band 19 - Band 16) - 0.2 * (Band 19 - Band 6) * (Band 19 / Band 16))) / (1.16 * (Band 19 - Band 16) / (Band 19 + Band 16 + 0.16)), while Dragonette-2/3 uses (3 * ((Band23 - Band20) - 0.2 * (Band23 - Band10) * (Band23 / Band20))) / (1.16 * (Band23 - Band20) / (Band23 + Band20 + 0.16)). Select a sensor above to see its specific band mapping.

What spectral bands does TCARIOSAVI705 require and why?

TCARIOSAVI705 requires RE2 (730-745 nm), RE1 (700-710 nm), G (520-600 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 TCARIOSAVI705 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 TCARIOSAVI705 compare to NDVI and other vegetation indices?

While NDVI is the most common vegetation index, TCARIOSAVI705 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.

TCARIOSAVI705 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/10.1016/j.agrformet.2008.03.005

Need help choosing?

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