Colour Index: The Colour Index algorithm was developed to differentiate soils in the field. Low valued CIs have been shown to be correlated with the presence of a high concentration of carbonates or sulfates and higher values to be correlated with crusted soils and sands in arid regions (Escadfal 1989). In most cases the CI gives complementary information with the BI and the NDVI. Used for diachronic analyses they help for a better understanding of the evolution of soil surfaces. The CI results from the following equation: CI = (red_factor * red - green_factor * green) / (red_factor * red + green_factor * green)

Colour Index: The Colour Index algorithm was developed to differentiate soils in the field. Low valued CIs have been shown to be correlated with the presence of a high concentration of carbonates or sulfates and higher values to be correlated with crusted soils and sands in arid regions (Escadfal 1989). In most cases the CI gives complementary information with the BI and the NDVI. Used for diachronic analyses they help for a better understanding of the evolution of soil surfaces. The CI results from the following equation: CI = (red_factor * red - green_factor * green) / (red_factor * red + green_factor * green)

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 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)

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 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)

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.

Atmospherically Resistant Vegetation Index: This index takes advantage of the different scattering responses from the blue and red band to retrieve information regarding the atmosphere opacity. The Atmospherically Resistant Vegetation Index algorithm was introduced by Kaufman and Tanre (1992). The resistance of the ARVI to atmospheric effects (in comparison to the NDVI) is accomplished by a self-correction process for the atmospheric effect on the red channel. This is done using the difference in the radiance between the blue and the red channels to correct the radiance in the red channel. Compared to the red band the blue band is much more easily scattered by the atmosphere particles. This explains why the sky is usually perceived as being blue. Thus the ARVI index takes advantage of the different scattering responses from the blue and red band to retrieve information regarding the atmosphere opacity. Simulations using radiative transfer computations on arithmetic and natural surface spectra for various atmospheric conditions show that ARVI has a similar dynamic range to the NDVI but is on average four times less sensitive to atmospheric effects than the NDVI. The ARVI results from the following equation:ARVI = (IR_factor * near_IR - rb) / (IR_factor * near_IR + rb) where: rb = (red_factor * red) - gamma * (blue_factor * blue - red_factor * red) with gamma = 1. The main reason why the blue band is more susceptible to atmospheric scattering than the red band is because its wavelength is shorter. Generally the shorter wavelength has stronger scattering. It's very similar to the way sea waves behave over oceans. When a large wave strikes an object such as a ferryboat it is more capable of continuing on its path by going around the object. On the other hand it is dispersed more easily when the waves are smaller in size. Consequently by obtaining the difference between the reflectance of the highly sensitive blue band and the less sensitive red band (blue - red) it serves like an indicator of what the atmospheric conditions were like. Here gamma serves as a weighting function for the difference reflectance of the two bands. Various values can be chosen for it which mainly depends on the type of aerosol size. According to Kaufaman and Tanre's statement in 1992 it is best to select a gamma value of 1 when information on the aerosol type is not available. Consequently the main purpose of the above rb equation is to decrease the influence brought forth from the atmosphere where a more accurate assessment of the value of the red reflectance can be obtained.

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 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)