The current release available from the Amini Data Platform is released on .

Use the Datapoint row to know what data to request from the Get Polygons API endpoint.

Datapoint	`ccci`
Name	Canopy Chlorophyll Content Index (CCCI)
Description	The Canopy Chlorophyll Content Index (CCCI) is a vegetation index that combines information from both red-edge and near-infrared (NIR) spectral bands to assess the chlorophyll content of plant canopies. It is particularly useful in precision agriculture for monitoring crop health, diagnosing nutrient deficiencies, and optimizing fertilization strategies. CCCI is sensitive to variations in chlorophyll content, making it an effective tool for managing crop nutrition and improving yield prediction.
Formula	CCCI = [(NDRE - NDRE_min) / (NDRE_max - NDRE_min)] × [(NDVI - NDVI_min) / (NDVI_max - NDVI_min)], Where: NDRE = (NIR - Red Edge) / (NIR + Red Edge)
Data interpretation	Higher values generally indicate higher chlorophyll content in plant canopies. Lower values suggest lower chlorophyll content or other factors affecting vegetation health.
Data format	`float` with range from `-1.00` to `1.00`

Common uses include:

Precision agriculture: To monitor crop chlorophyll content, assess nutrient levels, and optimize fertilization. It helps in diagnosing nutrient deficiencies, improving yield predictions, and managing crop variability within fields.
CCCI is also used in research studies focusing on plant physiology and stress responses.

Other related datapoints:

Normalized Difference Vegetation Index (NDVI): For general vegetation health assessment.
Green Normalized Difference Vegetation Index (GNDVI): For chlorophyll content analysis.
Normalized Difference Red Edge Index (NDRE): For assessing plant vigour and stress.

Related step-by-step workflow:

Crop type mapping

Datapoint	`evi`
Name	Enhanced Vegetation Index (EVI)
Description	The Enhanced Vegetation Index (EVI) is a satellite-derived index designed to optimize the vegetation signal by reducing the influence of atmospheric conditions and canopy background signals, particularly in high biomass regions. EVI is more sensitive than NDVI to variations in dense vegetation and includes a correction for canopy background noise and atmospheric interference.
Formula	EVI = G * ((NIR - Red) / (NIR + C1 * Red - C2 * Blue + L))
Data interpretation	Similar to NDVI, but less sensitive to atmospheric effects and soil background. Higher values generally indicate healthier vegetation.
Data format	`float` with range from `-1.00` to `1.00`

Common uses include:

Agricultural monitoring: Assessing crop health, yield estimation, and identifying stress conditions.
Forestry management: Monitoring Forest health, detecting deforestation, and assessing forest carbon stocks.
Environmental studies: Evaluating vegetation cover, land use change, and ecosystem health.
Disaster response: Assessing the impact of natural disasters on vegetation and monitoring vegetation recovery.

Other related datapoints:

Normalized Difference Vegetation Index (NDVI): for general vegetation assessment.
Soil Adjusted Vegetation Index (SAVI): for areas with significant soil influence.
Green Normalized Difference Vegetation Index (GNDVI): for a more chlorophyll-specific evaluation.

Related step-by-step workflow:

Crop type mapping

Datapoint	`gndvi`
Name	Green Normalized Difference Vegetation Index (GNDVI)
Description	The Green Normalized Difference Vegetation Index (GNDVI) is a variant of the NDVI that uses the green and near-infrared (NIR) bands to assess vegetation health. It is particularly sensitive to the amount of chlorophyll present in plant leaves, making it useful for detecting water stress and chlorophyll content in vegetation. GNDVI is commonly used in precision agriculture and environmental monitoring.
Formula	GNDVI = (NIR - Green) / (NIR + Green)
Data interpretation	Specifically measures green vegetation. Higher values indicate more green vegetation, while lower values suggest other vegetation types or bare soil.
Data format	`float` with range from `-1.00` to `1.00`

Common uses include:

Precision agriculture: For monitoring crop health, assessing chlorophyll content, and detecting water stress.
Environmental monitoring: to study vegetation dynamics, phenology, and overall ecosystem health.

Other related datapoints:

Normalized Difference Vegetation Index (NDVI): Assesses overall vegetation health using red and NIR bands.
Enhanced Vegetation Index (EVI): Which provides improved sensitivity in high biomass regions.

Related step-by-step workflow:

Crop type mapping

Datapoint	`msavi`
Name	Modified Soil-Adjusted Vegetation Index (MSAVI)
Description	The Modified Soil-Adjusted Vegetation Index (MSAVI) is an enhanced vegetation index that improves upon the Soil-Adjusted Vegetation Index (SAVI) by dynamically adjusting the soil brightness correction factor (L) based on vegetation density. MSAVI reduces soil noise more effectively, particularly in regions with low to moderate vegetation cover, making it a reliable tool for vegetation monitoring in arid and semi-arid environments.
Formula	MSAVI = (2×NIR+1−√((2×NIR+1)2−8×(NIR−Red)))/2(2 × NIR + 1 − √((2 × NIR + 1)² − 8 × (NIR − Red))) / 2(2×NIR+1−√((2×NIR+1)2−8×(NIR−Red)))/2
Data interpretation	Similar to SAVI, but with a wider dynamic range. Higher values indicate healthier vegetation.
Data format	`float` with range from `-1.00` to `1.00`

Common uses include: Commonly used in agricultural monitoring, land degradation studies, and environmental assessments in arid and semi-arid regions It is particularly effective in scenarios where vegetation is sparse, and soil exposure significantly impacts other vegetation indices like NDVI and SAVI.

Other related datapoints:

Normalized Difference Vegetation Index (NDVI): for general vegetation assessment.
Soil Adjusted Vegetation Index (SAVI): for areas with significant soil influence.
Green Normalized Difference Vegetation Index (GNDVI): for a more chlorophyll-specific evaluation.

Related step-by-step workflow:

Crop type mapping

Datapoint	`nbr`
Name	Normalized Difference Moisture Index (NDMI)
Description	he Normalized Burn Ratio (NBR) is a remote sensing index designed to identify burned areas and assess fire severity. It utilizes the near-infrared (NIR) and shortwave infrared (SWIR) bands to detect changes in vegetation and soil conditions caused by fire. NBR is particularly useful in post-fire assessments and monitoring ecosystem recovery over time.
Formula	NBR = (NIR - SWIR) / (NIR + SWIR)
Data interpretation	Values closer to 1 indicate burned areas. Values closer to -1 indicate unburned areas. Values near 0 suggest mixed areas with both burned and unburned regions.
Data format	`float` with range from `-1.00` to `1.00`

Other related datapoints:

Differenced Normalized Burn Ratio (dNBR): Which directly compares pre- and post-fire NBR values to assess fire severity.

Common uses include:

Wildfire management: For mapping burn scars, assessing fire severity, and monitoring post-fire vegetation recovery.
Ecological studies: To understand the impacts of fire on different ecosystems and in land management for planning restoration efforts.

Datapoint	`ndmi`
Name	Normalized Difference Moisture Index (NDMI)
Description	The Normalized Difference Moisture Index (NDMI) is a remote sensing index used to assess vegetation moisture content. It is particularly useful for monitoring plant water stress, drought conditions, and forest health. NDMI is calculated using the near-infrared (NIR) and shortwave infrared (SWIR) reflectance values, where SWIR is sensitive to vegetation moisture content.
Formula	NDMI = (NIR - SWIR) / (NIR + SWIR)
Data interpretation	Values closer to 1 indicate areas with high moisture content (e.g., wetlands, dense vegetation). Values closer to -1 indicate dry areas. Values near 0 suggest moderate moisture content.
Data format	`float` with range from `-1.00` to `1.00`

Other related datapoints:

Normalized Difference Vegetation Index (NDVI): Focuses on vegetation greenness.
Enhanced Vegetation Index (EVI): Improves sensitivity in high biomass regions.
Normalized Difference Water Index (NDWI): Focuses on detecting water bodies and moisture content in broader landscapes.

Common uses include: Used in agricultural monitoring to assess crop water stress and optimize irrigation strategies. It is also utilized in drought monitoring, forest health assessments, and fire risk analysis, as it provides insights into vegetation moisture content, which is critical for understanding plant stress and potential fire hazards.

Datapoint	`ndvi`
Name	Normalized Difference Vegetation Index (NDVI)
Description	NDVI is a widely used vegetation index in remote sensing that provides a quantitative measure of the greenness and health of vegetation. It is calculated using the reflectance of near-infrared (NIR) and red wavelengths of light.
Formula	NDVI = (NIR – Red) / (NIR +Red)
Data interpretation	Values closer to 1 indicate healthy, dense vegetation (e.g., green forests). Values closer to -1 indicate bare soil or water bodies. Values near 0 suggest sparse or unhealthy vegetation.
Data format	`float` with range from `-1.00` to `1.00`

Other related datapoints:

Enhanced Vegetation Index (EVI): for improved sensitivity in high biomass regions.
Soil Adjusted Vegetation Index (SAVI): for minimizing soil brightness influences.
Green Normalized Difference Vegetation Index (GNDVI): for a more chlorophyll-specific assessment.

Common uses include:

Agricultural monitoring: Assessing crop health, yield estimation, and identifying stress conditions.
Forestry management: Monitoring Forest health, detecting deforestation, and assessing forest carbon stocks.
Environmental studies: Evaluating vegetation cover, land use change, and ecosystem health.
Disaster response: Assessing the impact of natural disasters on vegetation and monitoring vegetation recovery.

Related step-by-step workflow:

Crop type mapping

Datapoint	`ndwi`
Name	Normalized Difference Water Index (NDWI)
Description	The Normalized Difference Water Index (NDWI) is a remote sensing index used to delineate open water features and monitor changes in water content of water bodies. It is calculated using green and near-infrared (NIR) reflectance values, where water bodies typically reflect more green light and absorb more NIR light, resulting in positive NDWI values.
Formula	NDWI = (Green - NIR) / (Green + NIR)
Data interpretation	Values closer to 1 indicate water bodies (e.g., lakes, rivers). Values closer to -1 indicate land areas. Values near 0 suggest mixed land-water areas.
Data format	`float` with range from `-1.00` to `1.00`

Other related datapoints:

Modified Normalized Difference Water Index (MNDWI): improves water detection in areas with built-up land and soil.
Normalized Difference Moisture Index (NDMI): assesses vegetation moisture content. Each index serves different purposes depending on the specific water-related analysis.

Common uses include: NDWI is widely used in hydrology and environmental monitoring for delineating and mapping water bodies, monitoring wetland areas, assessing flood extents, and analysing changes in surface water over time.

Datapoint	`savi`
Name	Soil-Adjusted Vegetation Index (SAVI)
Description	The Soil-Adjusted Vegetation Index (SAVI) is a modified vegetation index that corrects for the influence of soil brightness in areas with sparse or moderate vegetation cover. SAVI is particularly useful in arid and semi-arid regions where the presence of exposed soil can affect the accuracy of vegetation indices like NDVI. It introduces a soil brightness correction factor (L) to minimize soil influence.
Formula	SAVI = (NIR−Red)/(NIR+Red+L)(NIR - Red) / (NIR + Red + L)(NIR−Red)/(NIR+Red+L) * (1 + L)
Data interpretation	Similar to NDVI but adjusted for soil background effects. Higher values indicate healthier vegetation, even in areas with bare soil.
Data format	`float` with range from `-1.00` to `1.00`

Other related datapoints:

Normalized Difference Vegetation Index (NDVI): Effective in areas with dense vegetation cover.
Enhanced Vegetation Index (EVI): Designed for high biomass regions.

Common uses include:

Agricultural monitoring: Especially in arid and semi-arid regions, for assessing vegetation health where soil background noise may affect other indices like NDVI.
Land degradation studies, desertification monitoring, and environmental assessments: In regions with low to moderate vegetation cover.

Supported areas of interest

Region	Countries Available
Africa	Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Republic of the Congo, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe
Southeast Asia	Brunei, Cambodia, East Timor, Indonesia, Laos, Malaysia, Myanmar, Philippines, Thailand, Vietnam
Latin America & Caribbean	Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela

Supported time period

Time Period	What’s supported
Data available from	The beginning date of data availability from providers
Most recent date supported	Up to 7 days prior to query

Dataset requests

Request a dataset by opening a Dataset request GitHub issue. And, vote on the current set of requests by adding a thumbs-up reaction to the issue.

We are open for data partnerships and would appreciate any dataset contributions to info@amini.ai.

Citations

Please include the following citation when using amini-datasets for a paper, in addition to any citation specific to the used datasets. In this example, we show how you might cite the Forest Suitability Dataset from Amini — indicating “n.d.” or “no date” when retrieving the dataset.

”Forest Suitability Dataset.” Amini Data Catalog, n.d., https://lite.amini.ai.

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Supported Datapoints

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Get Started

API Reference

Reference Guides

How-to Guides

Contact Us

​Supported areas of interest

​Supported time period

​Dataset requests

​Citations

Supported areas of interest

Supported time period

Dataset requests

Citations