Remote Sensing Satellite Data and Spectral Indices

Over the years numerous spectral indices have been introduced by the scientific community to solve complex environmental (or other) issues. After a quite extensive survey we present you the most popular indices that be calculated by either of the following free data satellites:


  1. USGS/NASA Landsat program (Landsat 8 OLI, Landsat 5 TM)
  2. COPERNICUS/EC Sentinel-2 MSI

Advanced Vegetation Index (AVI)

Advanced Vegetation Index (AVI) is a numerical indicator, similar to NDVI, that uses the red and near-infrared spectral bands. Like NDVI, AVI is used in vegetation studies to monitor crop and forest variations over time. Through the multi-temporal combination of the AVI and the NDVI, users can discriminate different types of vegetation and extract phenology characteristics/parameters. To calculate the AVI with the following formulas (one for each satellite):
Learn how to calculate spectral indices

Bare Soil Index (BSI)

Bare Soil Index (BSI) is a numerical indicator that combines blue, red, near infrared and short wave infrared spectral bands to capture soil variations. These spectral bands are used in a normalized manner. The short wave infrared and the red spectral bands are used to quantify the soil mineral composition, while the blue and the near infrared spectral bands are used to enhance the presence of vegetation. BSI can be used in numerous remote sensing applications, like soil mapping, crop identification (in combination with NDVI) etc. To calculate the BSI with the following formulas (one for each satellite):

Shadow Index (SI)

The characteristics of canopy shadow are associated by the total spectral radiance that is reflected from the canopy. Canopy shadow provides essential information about trees and plants arrangement. As a remote sensing index, Shadow Index (SI) is calculated using the visible bands of the spectrum, in a way that simulates the amount energy not reflected back to the sensor. SI has main applications in forestry and crop monitoring. It is usually combined with AVI and BSI to help understand the status of vegetation. To calculate the SI with the following formulas (one for each satellite):

Normalized Difference Vegetation Index (NDVI)

The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the red and near-infrared spectral bands. NDVI is highly associated with vegetation content. High NDVI values correspond to areas that reflect more in the near-infrared spectrum. Higher reflectance in the near-infrared correspond to denser and healthier vegetation. NDVI can be used in numerous remote sensing applications, like crop phenology determination, crop type identification, crop health, forest monitoring etc. To calculate the NDVI with the following formulas (one for each satellite):

Normalized Difference Water Index (NDWI)

The Normalized Difference Water Index (NDWI) is a numerical indicator, derived from optical satellite images, using the near-infrared and short wave infrared spectral bands. The latter spectral band is highly associated with changes in vegetation water content and spongy mesophyll structure in the vegetation canopies. The near infrared spectral band response is correlated with the leaf internal structure and the leaf dry matter content, excluding water content. NDWI is useful in many remote sensing applications. Crop health monitoring, land/water boarding mapping, inland water discrimination from open sea water bodies, are just a few applications where NDWI is used. To calculate the NDWI with the following formulas (one for each satellite):

Normalized Difference Snow Index (NDSI)

Normalized Difference Snow Index (NDSI) is a numerical indicator that highlights snow cover over land areas. The green and short wave infrared spectral bands are used map the extend of snow cover. Snow and clouds reflect most of the incident radiation in the visible band. However, snow absorbs most of the incident radiation in the short wave infrared, while clouds do not. This enables the NDSI to distinguish snow from clouds. NDSI is commonly used in snow/ice cover mapping applications and can also be used, subsidiary, in glacier monitoring. To calculate the NDSI with the following formulas (one for each satellite):

Normalized Difference Glacier Index (NDGI)

Glaciers are an essential part of our living environment and especially the cryosphere. Although not everybody is familiar with glaciers, they are considered as very important natural regions that need to be preserved and monitored. Scientists analyzing glaciers can better model and understand the climate and it’s changes and dive into earth’s long forgotten climate history! Normalized Difference Glacier Index (NDGI) is a numerical indicator that helps to detect and monitor glaciers by using the green and red spectral bands. The main remote sensing applications that NDGI is used are glacier detection and monitoring (movement over time, continuity etc). To calculate the NDGI with the following formulas (one for each satellite):

Normalized Difference Moisture Index (NDMI)

The Normalized Difference Moisture Index (NDMI) is a numerical indicator, that is used in combination with other vegetation indexes (NDVI and/or AVI), which is associated with vegetation moisture. NDMI uses the near infrared and short wave infrared spectral bands to capture the variations of moisture in vegetated areas. Drought monitoring and subtle changes in vegetation moisture conditions are remote sensing applications where NDWI is applicable. NDMI can also be used to determine fuel moistures for wildfire hazard assessments. To calculate the NDMI with the following formulas (one for each satellite):

Normalized Burned Ratio Index (NBRI)

Forest fires are a severe manmade or natural phenomena that destroy natural resources, live stock, unbalances the local environments, release huge amount of Green House Gases etc. The scientific community has introduced the Normalized Burned Ratio Index (NBRI) to estimate the severity of fires, mainly in forested areas. NBRI takes advantage of the near infrared and short wave infrared spectral bands, which are sensitive in vegetation changes, to detect burned areas and monitor the recovery of the ecosystem. The NBRI must be used at least in pairs in order to extract information. One NBRI image before the fire event and one or more NBRI images after the fire event. The difference among these NBRI images will highlight the burned areas and can be used to monitor the behavior of the ecosystem as the time passes. To calculate the NBRI with the following formulas (one for each satellite):

Normalized Pigment Chlorophyll Ratio Index (NPCRI)

Crop/vegetation chlorophyll content is one of the parameters that is needed to determine several physiological parameters of plants. For example, crops with low Nitrogen content usually have a high carotenoid to chlorophyll ratio. Changes in such parameters can alter the spectral response of plants, thus making possible to quantify them using spectral indexes. The Normalized Pigment Chlorophyll Ratio Index (NPCRI) is a numerical indicator that is associated with the chlorophyll content and can find applications in precession agriculture. Using the red and blue spectral bands, NPCRI can capture the information needed to quantify chlorophyll and Nitrogen. To calculate the NPCRI with the following formulas (one for each satellite):

Article written by

Dimitris Sykas is a Remote Sensing and Surveying Engineer with a Ph.D. in hyperspectral remote sensing from the National Technical University of Athens (2016). He has spent fifteen years turning satellite data into operational products: at the NTUA Remote Sensing Laboratory, at Planetek Hellas as Senior EO Engineer, at Neuropublic building machine-learning models for smart farming and CAP monitoring, as a postdoctoral researcher on deep learning for agricultural Earth observation at the National Observatory of Athens, and as CTO of the cloudeo group. In 2024 he co-founded GOVADI, an Athens-based company building sovereign, on-premises AI systems for the Greek public sector and large enterprises, where he leads the technical direction of its products: Greek speech-to-text, retrieval-augmented generation with local models and embedding stores, document OCR and PII anonymisation. He has taken part in more than 25 funded EU and ESA projects, has authored over 23 peer-reviewed publications in remote sensing, and holds an international patent in hyperspectral data compression. He founded GEO University to bring hands-on GIS and Earth observation training online.

Dimitris Sykas

Remote Sensing Engineer, Ph.D. · Co-founder and Director, GOVADI · Founder, GEO University