A water index — 0 compatible sensors — Last revised June 1, 2025
LWVI-1
Leaf Water Vegetation Index 1
An index designed to estimate leaf water content using NIR wavelengths. This NDWI variant is particularly sensitive to changes in leaf water status and can help monitor plant water stress.
Used in crop monitoring, and water detection.
When to use
- Permanent and seasonal water body delineation
- Flood mapping and emergency response
- Wetland inventory and change detection
- Reservoir and lake water level monitoring
- Coastal shoreline change analysis
- Leaf water content estimation
- Plant water stress monitoring
Limitations
- Dark surfaces (shadows, asphalt, dark soils) can produce false positives
- Suspended sediments and algae alter spectral response in shallow water
- Mixed pixels at water boundaries reduce edge accuracy
- Atmospheric correction quality directly impacts threshold selection
- Sun glint over open water can saturate sensors and bias values
What the values mean
-1 Definitely not water
-0.3 Dry / built-up surface
0 Possible moisture / wet soil
0.3 Open water
0.6 Deep / clear water
| Surface type | Typical LWVI-1 |
|---|---|
| Built-up, asphalt | -0.5 to -0.2 |
| Bare soil, vegetation | -0.2 to 0 |
| Wet soil, flooded fields | 0 to 0.3 |
| Open water, lakes | 0.3 to 0.7 |
General Formula
nir_893 893
nir_1094 1094
Sensor-Specific Formulas
Most-used sensors — click to show code below
| Sensor | Provider | Formula | Band Mapping |
|---|
Code Examples
lwvi1_generic.py
LWVI-1 vs other water indices
| Index | Name | How it differs |
|---|---|---|
| LSWI | Land Surface Water Index | Alternative water index — different band combination |
| LWVI-2 | Leaf Water Vegetation Index 2 | Alternative water index — different band combination |
| MNDWI | Modified Normalized Difference Water Index | Refined formulation for specific conditions |
| NDMI | Normalized Difference Moisture Index | Alternative water index — different band combination |
Related Water Indices
References
Galvão et al. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data.
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