Snow Water Index
Snow Water Index for snow applications
When to use
- Long-term environmental monitoring
- Cryosphere and water resource tracking
- Climate-sensitive area assessment
- Multi-temporal change detection
- Integration with climate models
- Snow
Limitations
- Spatial resolution may be insufficient for small features
- Mixed pixels at boundaries require sub-pixel methods
- Atmospheric and bidirectional effects need correction
- Validation data may be sparse for remote regions
- Phenological timing affects detection accuracy
General Formula
Sensor-Specific Formulas
Most-used sensors — click to show code below
| Sensor | Provider | Formula | Band Mapping |
|---|---|---|---|
| USGS/NASA | (B3 * (B5 - B6)) / ((B3 + B5) * (B5 + B6)) | G→B3, N→B5, S1→B6 | |
| ESA | (B3 * (B8 - B11)) / ((B3 + B8) * (B8 + B11)) | G→B3, N→B8, S1→B11 | |
| MAXAR | (Green * (NIR1 - SWIR3)) / ((Green + NIR1) * (NIR1 + SWIR3)) | G→Green, N→NIR1, S1→SWIR3 |
Spectral Band Visualization — Landsat 8/9
Code Examples
Adapted for Landsat 8/9 bands —
Frequently Asked Questions
What is the SWI (Snow Water Index) and when should I use it?
Snow Water Index for snow applications This index belongs to a specialized category of spectral analysis. SWI is particularly suited for snow. The general formula is (G * (N - S1)) / ((G + N) * (N + S1)), which requires G and N and S1 spectral bands.
Which satellite sensors can I use to calculate SWI?
SWI is supported by 4 satellite sensors in our database, including Landsat 8/9, Sentinel-2, SuperView-2, WorldView 3. Each sensor uses different band designations — for example, Landsat 8/9 uses the formula (B3 * (B5 - B6)) / ((B3 + B5) * (B5 + B6)), while Sentinel-2 uses (B3 * (B8 - B11)) / ((B3 + B8) * (B8 + B11)). Select a sensor above to see its specific band mapping.
What spectral bands does SWI require and why?
SWI requires G (520-600 nm), N (770-900 nm), S1 (1550-1750 nm). These wavelength regions target the specific spectral features that this index is designed to measure.
How do I calculate SWI 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.
SWI vs other environmental indices
| Index | Name | How it differs |
|---|---|---|
| S3 | S3 Snow Index | Alternative environmental index — different band combination |
Related Environmental Indices
References
Need help choosing?
Ask our AI assistant for sensor recommendations, code examples, or how SWI compares to other indices for your specific use case.