Canopy water content (CWC) the amount of water stored in the vegetation canopy is typically determined by multiplying the leaf water content by the canopy leaf area index (LAI). This calculation incorporates information about the leaf water condition and the canopy structure [1]. CWC is a critical parameter for assessing vegetation growth and monitoring drought stress. It is influenced by soil water supply and atmospheric demand.

The Normalized Burn Ratio Index (NBR) uses the NIR and SWIR bands to emphasize burned areas while mitigating illumination and atmospheric effects. NBR = (NIR - SWIR) / (NIR+ SWIR)

Inverted Red-Edge Chlorophyll Index: The Inverted Red-Edge Chlorophyll Index algorithm incorporates the reflectance in four bands to estimate canopy chlorophyll content (Guyot and Baret 1988 Clevers et al. 2000). The 'red edge' is the name given to the abrupt reflectance change in the 680-740 nm region of vegetation spectra that is caused by the combined effects of strong chlorophyll absorption and leaf internal scattering. Increases in the amount of chlorophyll visible to the sensor either through an increase in leaf chlorophyll content or Leaf Area Index (LAI) result in a broadening of a major chlorophyll absorption feature centred around 680 nm. The effect is to cause a movement of the point of maximum slope termed the red edge position (REP). The position of the red edge has been used as an indicator of stress and senescence of vegetation (Collins1978 Horler et al. 1983 Rock et al. 1988 Boochs et al. 1990 Jago and Curran 1995). The IRECI results from the following (Sensor-dependent) equation: IRECI = (IR_factor * near_IR - red1_factor * red1) / (red2_factor * red2 / red3_factor * red3). For Sentinel-2 the formula is: (B7 - B4) / (B5 / B6) where (Central wavelength/Bandwidth): B7 = 783 nm (15 nm) B6 = 740 nm (15 nm) B5 = 705 nm (15 nm) B4 = 665 nm (30 nm)

Canopy water content (CWC) the amount of water stored in the vegetation canopy is typically determined by multiplying the leaf water content by the canopy leaf area index (LAI). This calculation incorporates information about the leaf water condition and the canopy structure [1]. CWC is a critical parameter for assessing vegetation growth and monitoring drought stress. It is influenced by soil water supply and atmospheric demand.

MCARI gives a measure of the depth of chlorophyll absorption and is very sensitive to variations in chlorophyll concentrations as well as variations in Leaf Area Index (LAI). MCARI values are not affected by illumination conditions the background reflectance from soil and other non-photosynthetic materials observed.

Enhanced vegetation index: In areas of dense canopy cover where leaf area index (LAI) is high the blue wavelengths can be used to improve the accuracy of NDVI as it corrects for soil background signals and atmospheric influences. Values description: The range of values for EVI is -1 to 1 with healthy vegetation generally around 0.20 to 0.80.

Brightness Index: This index is representing the average of the brightness of a satellite image. The Brightness Index algorithm is representing the average of the brightness of a satellite image. The result looks like a panchromatic image with the same resolution of the original image.This index is therefore sensitive to the brightness of soils which is highly correlated with the humidity and the presence of salts in surface (Escadafal 1989). The BI results from the following equation: BI = sqrt( ( (red_factor * red * red_factor * red) + (green_factor * green * green_factor * green) ) / 2 )

The Weighted Difference Vegetation Index algorithm was introduced by Clevers (1988). This has a relationship to PVI similar to the relationship IPVI has to NDVI. WDVI is a mathematically simpler version of PVI but it has an unrestricted range.Like PVI WDVI is very sensitive to atmospheric variations (Qi et al. 1994). The WDVI results from the following equation: WDVI = (IR_factor * near_IR - g * red_factor * red) where: g is the slope of the soil line.

The Modified Normalized Difference Water Index algorithm was developed by Xu 2006 and can enhance open water features while efficiently suppressing and even removing built-up land noise as well as vegetation and soil noise. The greater enhancement of water in the MNDWI-image will result in more accurate extraction of open water features as the built-up land soil and vegetation all negative values and thus are notably suppressed and even removed. The MNDWI results from the following equation: MNDWI = (green_factor * green - mir_factor * middle_IR) / (green_factor * green + mir_factor * middle_IR)

The Normalized Difference Vegetation Index (NDVI) is a measure of the amount and vigor of vegetation on the land surface and NDVI spatial composite images are developed to more easily distinguish green vegetation from bare soils. In general NDVI values range from -1.0 to 1.0 with negative values indicating clouds and water positive values near zero indicating bare soil and higher positive values of NDVI ranging from sparse vegetation (0.1 - 0.5) to dense green vegetation (0.6 and above).