DERIVING REMOTE SENSING INDICES FROM SENTINEL 2 SATELLITE IMAGERY IN GOOGLE EARTH ENGINE

5 min readJul 14, 2023

Google Earth Engine Cloud Computing Platform

Indices derived from satellite imagery are mathematical formulas that combine spectral bands from satellite sensors to provide information about specific features or characteristics on the Earth’s surface. These indices are widely used in remote sensing and geographic analysis to extract valuable information about vegetation health, land cover, water quality, urbanization, and more.

Sentinel 2 Key Information:

Sentinel-2 is an important satellite mission within the Copernicus program, consisting of two identical satellites, Sentinel-2A and Sentinel-2B. These satellites are equipped with multispectral sensors that capture imagery of the Earth’s surface in multiple spectral bands. With a spatial resolution of 10 meters for visible and near-infrared bands (bands 2–4) and 20 meters for red-edge and shortwave infrared bands (bands 5–12), Sentinel-2 provides high-resolution data for detailed analysis and monitoring.

One of the key features of Sentinel-2 is its wide swath coverage, spanning 290 kilometers. This wide coverage allows for large-scale mapping and monitoring of land areas, making it useful for various applications such as agriculture, forestry, land cover mapping, and environmental monitoring. The satellites revisit the same area approximately every 5 days (temporal resolution) at the Equator, ensuring frequent data acquisition and the ability to monitor changes over time.

Sentinel-2 data is freely accessible through the Copernicus Open Access Hub and other Earth observation data platforms, enabling researchers, scientists, and users worldwide to access and utilize the data for their specific needs. This open data policy promotes global scientific research, environmental monitoring, and the development of value-added applications. In this tutorial i will be using Google Earth Engine.

The multispectral imagery from Sentinel-2 is employed in diverse fields, including crop monitoring, deforestation analysis, land-use change detection, disaster management, urban planning, and water resource management. The rich spectral information captured by Sentinel-2’s sensors facilitates the identification of vegetation health, land cover types, water bodies, and other Earth surface characteristics, aiding in understanding and managing our planet’s resources and environment.

The GEE code with which you can follow along can be accessed via the following link:

https://code.earthengine.google.com/306ed90eed36c68b76f0ae02efa0457e

We will use Sentinel 2 in this case and first thing will be first loading our study area then fetching the Satellite data (Sentinel 2) in Google Earth Engine’s Cloud Platform:

Let us now have a deep dive to get to understand how we get these indices and of what use/ analysis they can be to us:

NDVI (Normalized Difference Vegetation Index): NDVI is used to assess vegetation health and density. It can be applied in agriculture to monitor crop health, identify areas of drought stress or disease, and guide irrigation and fertilization practices.

2. NDWI (Normalized Difference Water Index): NDWI is an index that measures the presence and abundance of water. It is derived by comparing the reflectance values of green and near-infrared (NIR) bands. NDWI values close to 1 indicate high water content, while values close to -1 indicate little or no water. NDWI is commonly used in various applications related to water bodies and hydrological processes, Water Resources Management, Drought Monitoring, Flood mapping, ecological studies , etc.

3. NDTI (Normalized Difference Turbidity Index): NDTI is used to assess water turbidity, which indicates the presence of suspended particles. It can be used in environmental monitoring to detect changes in water quality, identify pollution sources, and assess the impacts of human activities on aquatic ecosystems.

4. SAVI (Soil Adjusted Vegetation Index): SAVI is used to account for soil brightness when assessing vegetation. It can be applied in agricultural monitoring to evaluate vegetation cover and health in areas with varying soil brightness, helping to distinguish between bare soil and vegetation.

5. MSAVI (Modified Soil-Adjusted Vegetation Index): MSAVI is an index that provides an enhanced measure of vegetation greenness while compensating for variations in soil brightness. It is a modified version of the Soil-Adjusted Vegetation Index (SAVI) that reduces the sensitivity to soil brightness effects.

6. GCI (Green Chlorophyll Index): GCI is used to estimate chlorophyll content in vegetation. It can be utilized in agriculture to assess plant health, detect nutrient deficiencies or stress, and guide fertilizer application.

7. EVI (Enhanced Vegetation Index): EVI is an improved version of NDVI that reduces atmospheric influences. It can be used in ecological and land cover studies to monitor vegetation dynamics, assess forest health, and track changes in vegetation cover over time.

8. NDBI (Normalized Difference Built-Up Index): NDBI is used to identify built-up areas or urban environments. It can be applied in urban planning, land cover mapping, and infrastructure development to detect and monitor changes in built-up areas and assess urban expansion.