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Analysis Results

NDVI Interpretation
Enter Reflectance Values
NDVI
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EVI
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SAVI
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This page explains how to calculate NDVI from near-infrared (NIR) and red bands. It gives the formula, quick examples, band numbers for common satellites, and tips to avoid errors.

NDVI formula

NDVI = (NIR − Red) ÷ (NIR + Red)

  • NIR = surface reflectance in the near-infrared band
  • Red = surface reflectance in the red band

NDVI ranges from −1 to +1. Healthy green vegetation tends to be closer to +1.

Band references (popular sensors)

  • Landsat 8/9 (OLI): Red = Band 4, NIR = Band 5
  • Landsat 5/7 (TM/ETM+): Red = Band 3, NIR = Band 4
  • Sentinel-2 (MSI): Red = Band 4, NIR = Band 8

How to calculate

  1. Use surface reflectance (unitless numbers between 0 and 1). If you have DN or TOA values, convert to reflectance first.
  2. Read the Red and NIR pixel values.
  3. Apply (NIR − Red) / (NIR + Red).
  4. If NIR + Red = 0, mark NDVI as no data to avoid division by zero.

Worked examples

Single pixel

NIR = 0.62, Red = 0.18 → NDVI = (0.62 − 0.18) / (0.62 + 0.18) = 0.44 / 0.80 = 0.55

Another pixel

NIR = 0.12, Red = 0.10 → NDVI = (0.12 − 0.10) / (0.22) = 0.02 / 0.22 ≈ 0.09

Interpreting NDVI (general guide)

  • ~ −0.1 to 0.1: water, snow, clouds, or very bright soil
  • 0.1 to 0.3: sparse vegetation, dry areas
  • 0.3 to 0.6: moderate vegetation
  • 0.6 to 0.9: dense, healthy vegetation

Thresholds vary by region, season, sensor, and processing method. Always validate with field data if possible.

Data prep tips

  • Mask clouds, cloud shadows, snow, and water before computing NDVI.
  • Prefer surface reflectance products (atmospheric correction applied).
  • Keep Red and NIR in the same scale (for example 0–1 or 0–10000) before the ratio.
  • Resample bands to the same resolution if needed (e.g., Sentinel-2 Red 10 m and NIR 10 m are already matched).

Common mistakes

  • Mixing up band order (using Red − NIR by accident). Always use NIR minus Red in the numerator.
  • Using radiance or DN values without converting to reflectance.
  • Comparing NDVI from different dates without accounting for clouds, soil moisture, or sun angle.
  • Ignoring sensor differences and bandpasses when comparing across satellites.

Related indices

  • EVI: better in high biomass and hazy conditions.
  • SAVI: adds a soil brightness correction, useful for sparse vegetation.
  • NDWI: water index, complements NDVI for separating water vs vegetation.

FAQ

Do I need reflectance or can I use raw DN?

Use surface reflectance for reliable NDVI. DN and radiance can bias results if illumination and atmosphere change.

Why is my NDVI negative?

Water, clouds, snow, or very bright non-vegetated surfaces can produce negative values.

Can I compare NDVI across sensors?

Yes, but be careful. Different bandpasses and processing can shift values. Use consistent products and validate thresholds.

What about seasonal effects?

NDVI drops in dry seasons or after harvest. Compare similar dates (same season) for change detection.

What resolution do I need?

Choose based on feature size. Sentinel-2 (10 m) is good for fields. Landsat (30 m) is fine for larger areas.

How do I scale integer reflectance?

If your data are scaled (e.g., 0–10000), divide by the scale factor so values are 0–1 before computing NDVI.

Is NDVI the same as greenness?

It correlates with green biomass and canopy density, but it is not a direct measure of yield or leaf nitrogen.

How do clouds affect NDVI?

Clouds and shadows distort Red/NIR. Always apply cloud masks or quality bands.

Can I use NDVI on RGB photos?

No. You need a near-infrared band. RGB alone cannot compute NDVI.

What are typical NDVI values for crops?

Early season 0.2–0.4, mid-season 0.5–0.8 for healthy canopies. Values depend on crop type and conditions.