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Spectral Bands: A Guide to Popular Index Formulas

2025-03-27
(Article updated: 2025-03-27)
~ 8 min

Supporting environmental decisions with satellite and remote sensing tools plays a key role in today’s world. Current challenges related to climate change, natural resource monitoring, and rapid urban development require precise analysis of large datasets. Spectral Indexes help detect and monitor changes on the Earth's surface.

Spectral Indexes allow accurate assessment of vegetation health, soil moisture, water levels, and snow cover. They are crucial for scientific research and the development of new technologies like OnGeo Intelligence, which provides access to satellite imagery for remote sensing analysis.

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Spectral bands and their role in remote sensing 

Satellites capture electromagnetic radiation in different spectral bands, each measuring radiation at specific wavelengths. Examples include: 

  • NIR – Near-Infrared 

  • RED – Red light 

  • BLUE – Blue light 

  • SWIR – Short-Wave Infrared

Most popular Spectral Indexes

To start, it’s worth looking at a base satellite image without any Spectral Indices applied. Such an image allows you to clearly see the difference between raw data and the results obtained through Spectral Index calculations.

image.png
Base image — satellite photo in natural colors, without applied spectral indices. Visible are agricultural fields, forests, water bodies, and urban areas.

It should be remembered that individual Spectral Indexes perform with varying effectiveness depending on the type of land analyzed. Some Spectral Indexes work better in urban environments, allowing clear distinction of built-up areas, while others are more useful for agricultural fields, forests, or water body analysis.

1. NDVI – Normalized Difference Vegetation Index
Application:

  • Monitoring plant health: NDVI is used in agriculture to assess crop health and predict yields. Farmers can use this index to determine which parts of their fields need additional fertilization or irrigation.
  • Tracking changes over time: Helps monitor how vegetation changes due to climate shifts or human activities like deforestation.
  • Environmental protection: Used to monitor forests, especially in protected areas such as nature reserves and national parks.
    Formula:
    NDVI = (NIR − RED) / (NIR + RED)

 

image.png
NDVI visualization. Dark green areas indicate healthy, dense vegetation. Lighter green shades show weaker vegetation or areas with sparse plant cover. Bright areas represent non-vegetated surfaces, built-up zones, or bare soil.

 

image.png
NDII visualization. Darker areas indicate higher vegetation and soil moisture, while brighter areas show dry zones susceptible to drought or water stress.

 

2.    NDII – Normalized Difference Infrared Index
Application:

  • Monitoring vegetation moisture: NDII helps assess vegetation moisture levels, making it useful for detecting drought and crop water stress.
  • Fire risk assessment: Identifies areas with low plant moisture that are more prone to wildfires, especially during dry seasons.
  • Water resource management: Used in hydrological analysis to assess water availability in soil and vegetation over large areas.
    Formula:
    NDII = (NIR − SWIR) / (NIR + SWIR)

 

3. SAVI – Soil-Adjusted Vegetation Index
Application:

  • Analyzing vegetation in low-cover areas: SAVI is ideal for desert and semi-desert regions where bare soil significantly influences measurement results.
  • Use in arid regions: Designed to monitor vegetation in areas with minimal plant cover but significant soil influence.
    Formula:
    SAVI = ((NIR − RED) / (NIR + RED + L)) × (1 + L)
    L – soil adjustment factor (commonly 0.5).

 

image.png
SAVI visualization. Dark green areas indicate dense, healthy vegetation, lighter green shades represent sparse vegetation or areas where bare soil influences the measurement. Bright areas correspond to non-vegetated surfaces or built-up zones.

 

image.png
NDWI visualization. Blue areas represent surface water bodies and high-moisture zones. Green shades indicate land surfaces — agricultural fields, forests, and built-up areas without water presence.

 

4. NDWI – Normalized Difference Water Index
Application:

  • Detection of surface water: Useful for identifying lakes, rivers, and wetlands.
  • Monitoring water content in vegetation: Applied in agriculture to manage water resources and monitor crop moisture.
    Formula:
    NDWI = (NIR − SWIR) / (NIR + SWIR)

 

5. MNDWI – Modified Normalized Difference Water Index
Application:

  • Accurate detection of surface water bodies: Effective for identifying lakes, rivers, flood zones, even in complex urban environments.
  • Monitoring water extent changes: Helps track seasonal and sudden changes in water coverage, useful in flood and drought analysis.
  • Urban surface analysis: Separates water bodies from urban structures and other artificial surfaces, where traditional NDWI may struggle.
    Formula:
    MNDWI = (GREEN − SWIR) / (GREEN + SWIR)

 

 

image.png
MNDWI visualization. Intense blue areas indicate surface water bodies. Light green and beige colors represent land surfaces — agricultural fields, forests, and artificial surfaces such as built-up areas.

 

image.png
NDMI visualization. Blue areas indicate high vegetation and soil moisture, while yellow and red zones highlight dry areas at risk of drought or water stress.

 

6. NDMI – Normalized Difference Moisture Index
Application:

  • Monitoring vegetation moisture content: NDMI helps assess water content in plants, relevant for agriculture and crop management.
  • Fire risk assessment: Identifies areas more prone to fires due to low vegetation moisture.
    Formula:
    NDMI = (NIR − SWIR) / (NIR + SWIR)

 

 

7. NDSI – Normalized Difference Snow Index
Application:

  • Monitoring snow cover: Used to assess snow-covered areas, important in climate change research and glacier monitoring.
  • Observing snow cover changes: Tracks seasonal and climate-induced variations in snow coverage.
    Formula:
    NDSI = (GREEN − SWIR) / (GREEN + SWIR)

 

2024-01-08-00_00_2024-01-08-23_59_Sentinel-2_L2A_NDSI.jpg
. NDSI visualization. Light blue areas represent snow-covered surfaces, while dark tones indicate snow-free land, vegetation, or built-up areas.

 

image.png
BSI visualization. Red areas represent bare soil surfaces, degraded land, or dry zones. Green areas indicate vegetation cover or healthy plant presence.

 

8. BSI – Bare Soil Index
Application:

  • Soil erosion and land degradation studies: BSI is used in research focused on soil erosion and desertification, especially in agricultural and dry regions.
  • Monitoring soil conditions: Tracks changes in soil state in arid regions.
    Formula:
    BSI = ((SWIR + RED) + (NIR + BLUE)) / ((SWIR + RED) − (NIR + BLUE))

 

9. MNDBI – Modified Normalized Difference Built-up Index
Application:

  • Detection of built-up areas: MNDBI is used to identify and monitor urban zones, effectively distinguishing artificial surfaces like concrete and asphalt from natural terrain.
  • Urban development analysis: Helps track city expansion, infrastructure changes, and urban growth over time.
  • Spatial planning support: Used by urban planners and analysts to assess urban density and its environmental impact.
    Formula:
    MNDBI = (SWIR − NIR) / (SWIR + NIR)

 

image.png
Red and dark orange areas indicate built-up zones. Yellow and orange colors show surfaces that resemble buildings or artificial structures. Green areas represent vegetation or natural land cover.

 

image.png
Dark orange and orange areas indicate built-up zones or artificial surfaces. Light orange and cream tones show surfaces resembling buildings or hardened ground. Very light, almost white areas represent natural land, vegetation, or water bodies.

 

10. BAEI – Built-up Area Extraction Index
Application:

  • Extraction of built-up surfaces: BAEI is used to identify artificial surfaces such as roads, buildings, and other urban infrastructure, based on the contrast between the blue band and near-infrared.
  • Urbanization monitoring: Helps analyze infrastructure development and land cover changes, particularly in rapidly growing cities.
  • Separating built-up areas from vegetation and water: BAEI effectively distinguishes artificial surfaces from green areas and water bodies, with high sensitivity in urban environments.
    Formula:
    BAEI = (RED + SWIR) / (NIR + BLUE)

 

 

Comparative table of spectral indices

Index NameApplicationFormula
NDVI – Normalized Difference Vegetation IndexMonitoring plant health, yield prediction, vegetation change assessment, forest and nature reserve monitoring(NIR − RED) / (NIR + RED)
NDII – Normalized Difference Infrared IndexAssessing vegetation moisture, drought detection, identifying fire-prone areas, hydrological studies(NIR − SWIR) / (NIR + SWIR)
SAVI – Soil-Adjusted Vegetation IndexVegetation analysis in low-cover regions, monitoring in arid areas(NIR − RED) / (NIR + RED + L) × (1 + L), L = 0.5
NDWI – Normalized Difference Water IndexDetection of surface water bodies, assessing water content in plants(NIR − SWIR) / (NIR + SWIR)
MNDWI – Modified Normalized Difference Water IndexPrecise detection of water bodies, monitoring floods and droughts, analysis of urban water surfaces(GREEN − SWIR) / (GREEN + SWIR)
NDMI – Normalized Difference Moisture IndexMonitoring plant water content, fire risk assessment(NIR − SWIR) / (NIR + SWIR)
NDSI – Normalized Difference Snow IndexSnow and glacier monitoring, assessing seasonal snow cover changes(GREEN − SWIR) / (GREEN + SWIR)
BSI – Bare Soil IndexSoil erosion and land degradation studies, soil condition analysis((SWIR + RED) − (NIR + BLUE)) / ((SWIR + RED) + (NIR + BLUE))
MNDBI – Modified Normalized Difference Built-up IndexDetection and monitoring of built-up areas, urban development analysis, spatial planning(SWIR − NIR) / (SWIR + NIR)
BAEI – Built-up Area Extraction IndexExtraction of built-up areas, urbanization monitoring, separating built-up surfaces from vegetation and water(RED + SWIR) / (NIR + BLUE)

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Comparative table of spectral indices

Spectral Indices are extremely useful tools in remote sensing analysis, enabling precise monitoring of various environmental aspects - from plant health to changes in snow cover. Each Spectral Index has its strengths and limitations, which should be considered when choosing the right one. Understanding these differences allows for the effective use of Spectral Indices depending on the specifics of the project.
If you want to fully harness the potential of satellite data in your analyses, OnGeo Intelligence offers comprehensive solutions for GIS and remote sensing experts. 

Contact us to learn how our tools can support your projects!

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