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.

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 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)

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.

Difference Vegetation Index: This index is sensitive to the amount of vegetation. The Difference Vegetation Index algorithm is sensitive to the amount of vegetation. This is the simplest vegetation index: - Sensitive to the amount of vegetation - Distinguishes between soil and vegetation - Does NOT deal with the difference between reflectance and radiance caused by the atmosphere or shadows. The DVI results from the following equation: DVI = (IR_factor * near_IR - red_factor * red). In the area of red the incoming solar radiation won't be extensively absorbed by the pigments of the mesophyll inside the folios primarily by the chlorophyll. In the area of the near infrared in contrast the bigger part of the incoming radiation was reflected.

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.

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.

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.

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 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)