NDVI (Normalized Difference Vegetation Index) is one of the most widely used vegetation indices, allowing researchers to assess plant condition and density. It is based on the contrast between high reflectance in the near-infrared (NIR) range and low reflectance in the red (RED) range—a characteristic feature of healthy, photosynthetically active vegetation. NDVI values increase with the intensity of greenness, indicating better plant health and greater biomass. Low NDVI values, on the other hand, correspond to areas with sparse vegetation or bare surfaces such as soil, sand, or water.
GNDVI (Green Normalized Difference Vegetation Index) is calculated in a similar way to NDVI, but it uses the green band instead of the red one. As a result, it is more sensitive to chlorophyll content in leaves and provides a better representation of a plant’s photosynthetic activity. GNDVI shows a strong correlation with biomass and the Leaf Area Index (LAI), making it a valuable tool for assessing crop health and monitoring growth over time.
SAVI (Soil Adjusted Vegetation Index) was developed to minimize the influence of soil brightness in vegetation analysis, particularly in areas where plants are sparse and the soil is partially exposed. This index includes an additional parameter, L, which allows its sensitivity to be adjusted based on vegetation density. For areas with dense vegetation, L takes on lower values, while for regions with limited plant cover, it is higher. SAVI is especially useful for monitoring arid regions, low-density crops, and ecosystems where traditional indices such as NDVI may be affected by background soil reflectance.
LCI (Leaf Chlorophyll Index) is used to assess the chlorophyll content in plant leaves. Chlorophyll is the main photosynthetic pigment responsible for absorbing light energy necessary for plant growth. Its concentration is directly linked to plant health and photosynthetic activity. LCI is based on the difference in reflectance between the green and near-infrared (NIR) bands. Plants with higher chlorophyll content reflect more strongly in the NIR range and less in the green range; therefore, higher LCI values indicate healthier plants with greater photosynthetic efficiency.
MCARI (Modified Chlorophyll Absorption in Reflectance Index) is designed to provide a precise assessment of chlorophyll content in plant leaves. It takes into account how chlorophyll absorbs light in the red spectral range, while simultaneously correcting for the effects of soil background and vegetation structure. MCARI combines information from the green, red, and near-infrared (NIR) bands in a way that allows for a more accurate determination of photosynthetic activity. This makes it particularly useful in environments where traditional indices such as NDVI may be less reliable.
NDRE (Normalized Difference Red Edge Index) is a vegetation index that utilizes the red-edge band, which is especially sensitive to variations in chlorophyll content and leaf structure. It is calculated using the difference and sum of reflectance in the near-infrared (NIR) and red-edge bands. As a result, NDRE enables precise assessment of plant health and photosynthetic activity, particularly in dense crop canopies, where the classic NDVI tends to saturate. This index is widely used in agricultural remote sensing, both in satellite imagery and drone-based data acquisition.
SIPI2 (Structure Insensitive Pigment Index 2) is an index developed to evaluate the pigment content in plants—primarily chlorophyll and carotenoids—while minimizing the influence of leaf structure and soil background on the measurements. These pigments are essential for photosynthesis, and their abundance serves as an important indicator of plant health and efficiency. SIPI2 analyzes reflectance in the near-infrared (NIR) range together with selected bands of visible light to detect subtle variations in pigment ratios. Higher SIPI2 values indicate greater concentrations of chlorophyll and carotenoids, which generally correspond to healthier plants and higher photosynthetic activity.
EVI (Enhanced Vegetation Index) was developed as an improvement over the classic NDVI, designed to better capture vegetation conditions in areas with dense leaf canopies. In such environments, NDVI often reaches saturation and becomes less sensitive to additional vegetation changes. EVI incorporates the blue spectral band to correct for atmospheric and soil effects, providing a more accurate representation of actual plant condition. This index maintains high sensitivity even in dense crops and forested regions, which makes it widely used for vegetation monitoring with both satellite and drone-based imagery.
ARVI (Atmospherically Resistant Vegetation Index) was developed to minimize the impact of atmospheric conditions on vegetation measurements. Aerosols such as dust, smoke, and water vapor scatter and absorb light, which can lead to inaccuracies in traditional indices like NDVI. ARVI incorporates the blue spectral band to perform atmospheric correction, allowing for more reliable vegetation data even under variable visibility conditions. Higher ARVI values indicate healthier, more photosynthetically active vegetation.
REIP (Red Edge Inflection Point) is an index based on the analysis of the “red edge”—a narrow spectral region between red and near-infrared light where plant reflectance rises sharply. This part of the spectrum is particularly sensitive to changes in chlorophyll content and leaf structure. As chlorophyll levels increase, the red-edge inflection point shifts toward longer wavelengths, signaling healthier and more photosynthetically active vegetation. Because of its high sensitivity, REIP is widely used in hyperspectral imaging for assessing crop condition and monitoring subtle changes in plant health over time.
GCI (Green Chlorophyll Index) is used to assess the chlorophyll content in plants. Similar to LCI and CIgreen, it analyzes reflectance in the near-infrared (NIR) and green bands, but it relies on their ratio, which allows for a more precise detection of variations in chlorophyll concentration. GCI is particularly useful for monitoring plant health and nutrient status, as chlorophyll levels directly reflect a plant’s physiological condition and photosynthetic efficiency. Higher GCI values indicate healthier, more vigorous vegetation.
OSAVI (Optimized Soil Adjusted Vegetation Index) is an enhanced version of the SAVI index, designed to more accurately compensate for the influence of soil on vegetation measurements. It performs especially well in areas with sparse or low vegetation cover, where soil reflectance can strongly distort readings. OSAVI uses an optimized correction factor that automatically adapts to surface conditions, providing a more accurate representation of actual plant status. In practice, it is particularly useful for analyzing early crop growth stages and semi-arid regions, where soil is largely exposed.
VARI (Visible Atmospherically Resistant Index) is designed to estimate vegetation greenness using only visible (RGB) bands. It is an ideal index for digital cameras and drones that lack near-infrared sensors. VARI minimizes the effects of atmospheric conditions and lighting, enabling reliable assessments of plant health even from standard RGB images. It is particularly useful for quickly mapping areas of vegetation, detecting dry or damaged patches, and analyzing urban green spaces or biologically active surfaces.
NDMI (Normalized Difference Moisture Index), also known as NDWI (Normalized Difference Water Index), is used to assess water content in plants or soil. This index relies on reflectance in the near-infrared (NIR) and shortwave infrared (SWIR) bands, since water strongly absorbs radiation in the SWIR range. As a result, NDMI allows for monitoring plant water stress, soil moisture, and surface water presence. A decrease in NDMI values may indicate drought, water deficiency, or environmental stress, while higher values correspond to well-hydrated, photosynthetically active vegetation.
PRI (Photochemical Reflectance Index) is an index developed to assess photosynthetic efficiency and light stress in plants. It measures subtle changes in light reflectance within the green–yellow spectral range, which are linked to the activity of carotenoid pigments involved in protecting plants from excessive light. PRI is particularly sensitive to variations in the xanthophyll cycle, a key photoprotective process that helps plants dissipate excess energy. Because of this sensitivity, PRI can detect signs of plant stress at a very early stage—long before any visible symptoms appear.
ARI (Anthocyanin Reflectance Index) is designed to estimate the anthocyanin content in plants—pigments responsible for the red, purple, and violet colors observed in leaves, fruits, and flowers. Anthocyanins play a crucial protective role, helping plants reduce oxidative stress, UV radiation damage, and injuries caused by drought or low temperatures. ARI is based on differences in light reflectance between the green and near-infrared (NIR) bands. Higher ARI values indicate greater anthocyanin concentration, which may reflect an active defense response or the natural ripening process of leaves and fruits. In practice, ARI is highly valuable for monitoring plant stress, assessing crop maturity, and analyzing seasonal color variations in vegetation.