SPOT HRV XS-based Redness Index 4
SPOT HRV XS-based Redness Index 4 for soil applications
When to use
- Soil mapping in agricultural and arid environments
- Salinity monitoring in irrigated areas
- Erosion hazard assessment on bare ground
- Organic matter and fertility surveys
- Soil moisture inference (combined with other indices)
Limitations
- Vegetation cover blocks the soil signal — most reliable on bare or sparse ground
- Surface roughness and tilling state affect reflectance
- Soil moisture varies daily and biases brightness measurements
- Cannot directly measure subsurface properties
- Mineral composition variability affects index calibration across regions
General Formula
Sensor-Specific Formulas
Most-used sensors — click to show code below
| Sensor | Provider | Formula | Band Mapping |
|---|---|---|---|
| 21AT | (Red**2.0)/(Green**4.0) | R→Red, G→Green | |
| CG Satellite | (Red**2.0)/(Green**4.0) | R→Red, G→Green | |
| USGS/NASA | (B4**2.0)/(B3**4.0) | R→B4, G→B3 | |
| USDA | (Red**2.0)/(Green**4.0) | R→Red, G→Green | |
| ESA | (B4**2.0)/(B3**4.0) | R→B4, G→B3 | |
| MAXAR | (Red**2.0)/(Green**4.0) | R→Red, G→Green | |
| MAXAR | (Red**2.0)/(Green**4.0) | R→Red, G→Green |
Spectral Band Visualization — BJ3A
Code Examples
Adapted for BJ3A bands —
Frequently Asked Questions
What is the RI4XS (SPOT HRV XS-based Redness Index 4) and when should I use it?
SPOT HRV XS-based Redness Index 4 for soil applications Soil indices characterise surface properties including brightness, moisture content, organic matter, and salinity. They work best on bare or sparsely vegetated ground where the soil spectral signal is not obscured. RI4XS is particularly suited for soil. The general formula is (R**2.0)/(G**4.0), which requires R and G spectral bands.
Which satellite sensors can I use to calculate RI4XS?
RI4XS is supported by 23 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-1, Dragonette-2/3, Gaofen-1 and 18 more. Each sensor uses different band designations — for example, BJ3A uses the formula (Red**2.0)/(Green**4.0), while BJ3N uses (Red**2.0)/(Green**4.0). Select a sensor above to see its specific band mapping.
What spectral bands does RI4XS require and why?
RI4XS requires R (630-690 nm), G (520-600 nm). These wavelength regions target the specific spectral features that this index is designed to measure.
How do I calculate RI4XS 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.
RI4XS vs other soil indices
| Index | Name | How it differs |
|---|---|---|
| BI | Brightness Index | Alternative soil index — different band combination |
| BI2 | Second Brightness Index | Alternative soil index — different band combination |
| CI | Coloration Index | Alternative soil index — different band combination |
| NSDS | Normalized Shortwave Infrared Difference Soil-Moisture | Alternative soil index — different band combination |
Related Soil Indices
References
Need help choosing?
Ask our AI assistant for sensor recommendations, code examples, or how RI4XS compares to other indices for your specific use case.