Transformed Chlorophyll Absorption Ratio
An index designed to estimate vegetation chlorophyll content while minimizing the effects of leaf area index. TCARI is particularly useful for precision agriculture and crop health monitoring.
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
- Chlorophyll content estimation
- Crop health monitoring
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
| Surface type | Typical TCARI |
|---|---|
| Open water, snow | -0.3 to -0.1 |
| Bare soil, urban | -0.1 to 0.2 |
| Sparse or stressed crops | 0.2 to 0.4 |
| Healthy crops, grassland | 0.4 to 0.7 |
| Dense forest, peak season | 0.7 to 0.9 |
General Formula
Sensor-Specific Formulas
Most-used sensors — click to show code below
| Sensor | Provider | Formula | Band Mapping |
|---|---|---|---|
| Wyvern | 3 * ((Band 16 - Band 13) - 0.2 * (Band 16 - Band 5) * (Band 16/Band 13)) | green→Band 5, red→Band 13, re1→Band 16 | |
| ESA | 3 * ((B5 - B4) - 0.2 * (B5 - B3) * (B5/B4)) | green→B3, red→B4, re1→B5 | |
| MAXAR | 3 * ((Red Edge - Red) - 0.2 * (Red Edge - Green) * (Red Edge/Red)) | green→Green, red→Red, re1→Red Edge | |
| MAXAR | 3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge/Red)) | green→Green, red→Red, re1→Red_Edge |
Spectral Band Visualization — Dragonette-1
Code Examples
Adapted for Dragonette-1 bands —
Frequently Asked Questions
What is the TCARI (Transformed Chlorophyll Absorption Ratio) and when should I use it?
An index designed to estimate vegetation chlorophyll content while minimizing the effects of leaf area index. TCARI is particularly useful for precision agriculture and crop health monitoring. 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. TCARI is particularly suited for chlorophyll content estimation, crop health monitoring, precision agriculture. The general formula is 3 * ((RE1 - Red) - 0.2 * (RE1 - Green) * (RE1/Red)), which requires green and red and re1 spectral bands.
Which satellite sensors can I use to calculate TCARI?
TCARI is supported by 13 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Gaofen-1, Gaofen-2, Göktürk-1 and 8 more. Each sensor uses different band designations — for example, Dragonette-1 uses the formula 3 * ((Band 16 - Band 13) - 0.2 * (Band 16 - Band 5) * (Band 16/Band 13)), while Dragonette-2/3 uses 3 * ((Band20 - Band17) - 0.2 * (Band20 - Band9) * (Band20/Band17)). Select a sensor above to see its specific band mapping.
What spectral bands does TCARI require and why?
TCARI requires green (550), red (670), re1 (700). 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 TCARI 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 TCARI compare to NDVI and other vegetation indices?
While NDVI is the most common vegetation index, TCARI provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.
TCARI vs other vegetation indices
| Index | Name | How it differs |
|---|---|---|
| ARI | Anthocyanin Reflectance Index | Alternative vegetation index — different band combination |
| mARI | Modified Anthocyanin Reflectance Index | Refined formulation for specific conditions |
| ARVI | Atmospherically Resistant Vegetation Index | Atmospherically corrected version |
| ARVI2 | Atmospherically Resistant Vegetation Index 2 | Atmospherically corrected version |
Related Vegetation Indices
References
Need help choosing?
Ask our AI assistant for sensor recommendations, code examples, or how TCARI compares to other indices for your specific use case.