By Harshal Sunil Kamble (Shivaji University Student)
1. What is MNDWI?
It is a remote sensing index used primarily to detect water bodies in satellite imagery. MNDWI enhances open water features while suppressing built-up land, vegetation, and soil noise.
2. How it works:
🌊 MNDWI highlights water bodies and suppresses noise from buildings/vegetation.
🛰️ Uses Green and SWIR bands (e.g., Landsat: Band 3 & Band 6).
✅ Better than NDWI for detecting water in urban areas.
3. Formula
MNDWI= G – SWIR
G + SWIR
Where:
G = Reflectance in Green band
SWIR = Reflectance in Short-Wave Infrared band
4. Advantages
✅Enhances Water Detection: Clearly highlights rivers, lakes, wetlands, and flooded areas.
✅Reduces Urban Noise: Performs better than NDWI in built-up areas, avoiding false positives.
5. Applications
🗺️ Water body extraction
🌊 Flood mapping
🌿 Wetland monitoring
🏙️ Urban water detection
2. NDMI (Normalized Difference Moisture Index)
1. What is NDMI?
It is a remote sensing index used to monitor vegetation water content and plant health, especially in agricultural and forestry applications. It is particularly useful for detecting drought stress, wildfire risk, and crop conditions.
2. How it works:
NDMI (Normalized Difference Moisture Index) measures vegetation moisture by comparing reflected near-infrared (NIR) and short-wave infrared (SWIR) light. Healthy, moist plants reflect more NIR and less SWIR, resulting in higher NDMI values (close to +1). Dry or stressed vegetation reflects more SWIR, lowering NDMI (near zero or negative). It’s used to monitor plant water content, drought, and vegetation health.
3. Formula
NDMI = (NIR – SWIR1)
(NIR + SWIR1)
Where:
NIR = Near-Infrared band (e.g., 0.76–0.90 µm)
SWIR = Short-Wave Infrared band (e.g., 1.55–1.75 µm)
4.Advantages
✅NDMI effectively detects changes in plant water content because it uses the SWIR band, which responds to moisture levels.
✅Values range from –1 to +1, making it easy to compare results across different areas and times.
✅The combination of NIR and SWIR bands helps minimize interference from soil reflectance.
5. Applications
Drought Monitoring: Detects moisture stress in vegetation to identify drought-affected areas.
Crop Health Assessment: Monitors crop water status and helps manage irrigation effectively.
Wildfire Risk Assessment: Identifies dry vegetation areas that are more prone to wildfires.
3. NDSI (Normalized Difference Snow Index)
1. What is NDSI?
It’s a remote sensing index used to detect snow cover in satellite images.
2.How it works:
- Snow reflects strongly in the Green band
- Snow absorbs strongly in the SWIR band
- So, snow areas have high NDSI values (close to +1)
- Other surfaces (like vegetation, soil, water) have lower or negative NDSI values.
3. NDSI Formula
NDSI= RGreen – RSWIR
RGreen + RSWIR
Where,
RGreen= Reflectance in the Green band
RSWIR= Reflectance in the Short-Wave Infrared band
4. Advantages
✅ 1. Accurate Snow Detection: NDSI effectively distinguishes snow from other bright surfaces (e.g., clouds or sand) because snow reflects strongly in the visible green band but absorbs strongly in the SWIR band.
✅ 2. Minimal Atmospheric Interference: Compared to simple reflectance values, NDSI is less sensitive to atmospheric effects like haze or varying illumination, since it is a ratio-based index.
5. Applications
- Mapping snow cover extent
- Monitoring snowmelt and seasonal changes
- Hydrological modeling
Research Contributors:
Harshal Sunil Kamble (Shivaji University Student)
