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 Red-Edge Inflection Point Index algorithm was developed for applications in biomass and nitrogen (N) uptake measurement/management in heterogeneous fields.- Guyot et al. (1988). Red edge as the inflection point of the strong red absorption to near infrared reflectance includes the information of both crop N and growth status. The reflectance around red edge is sensitive to wide range of crop chlorophyll content N content LAI and biomass (Hatfield et al. 2008 Mutanga and Skidmore 2007 Steele et al. 2008b). The REIP general formula is based on linear four-point interpolation technique and it uses four wavebands (670 700 740 and 780 nm) - Guyot and Baret (1988). The REIP results from the following (Sensor-dependent) equation: REIP = 700 + 40 * ((r670 + r780)/2 - r700) / (r740 - r700) - as general formula or: REIP = 700 + 40 * ( (red1_factor * red1 + IR_factor * near_IR)/2) - red2_factor * red2 ) / (red3_factor * red3 - red2_factor * red2) )

The Redness Index algorithm was developed to identify soil colour variations - Pouget et al.(1990). The RI results from the following equation: RI = (red_factor * red * red_factor * red) / (green_factor * green * green_factor * green * green_factor * green)

Cover fraction: FVC is used to separate vegetation and soil in energy balance processes including temperature and evapotranspiration. It is computed from the leaf area index and other canopy structural variables and does not depend on variables such as the geometry of illumination as compared to FAPAR

The Normalized Difference Water Index algorithm was developed by Gao (19964) being a measure of liquid water molecules in vegetation canopies that interacted with the incoming solar radiation. NDWI is sensitive to changes in liquid water content of vegetation canopies. It is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects therefore it should be considered as an independent vegetation index. It is complementary to not a substitute for NDVI. The NDWI results from the following equation: NDWI = (IR_factor * near_IR - mir_factor * middle_IR) / (IR_factor * near_IR + mir_factor * middle_IR)

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.

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)

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)

The Green Normalized Difference Vegetation Index algorithm was developed by Gitelson et al. (1996). The authors verified that GNDVI was more sensible than NDVI to identify different concentration rates of chlorophyll which is highly correlated at nitrogen. The use of green spectral band was more efficient than the red spectral band to discriminate nitrogen. The GNDVI results from the following equation: GNDVI = (IR_factor * near_IR - green_factor * green) / (IR_factor * near_IR + green_factor * green)

The Transformed Normalized Difference Vegetation Index algorithm indicates a relation between the amount of green biomass that is found in a pixel. (Senseman et.al. 1996). Transformed Normalised Difference Vegetation index (TNDVI) is the square root of the NDVI. It has higher coefficient of determination for the same variable and this is the difference between TNDVI and NDVI. The formula of TNDVI has always positive values and the variances of the ratio are proportional to mean values. The TNDVI results from the following equation: TNDVI = sqrt( (IR_factor * near_IR - red_factor * red) / (IR_factor * near_IR + red_factor * red) + 0.5)