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)

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.

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)

Chlorophyll content in the leaf: corresponds to the content of chlorophyll a chlorophyll b and carotenoids per unit of leaf area.

The Normalized Difference Pond Index algorithm was developed by J.P Lacaux et al. (2006).The NDPI makes it possible not only to distinguish small ponds and water bodies (down to 0.01 ha) but also to differentiate vegetation inside ponds from that in their surroundings The NDPI results from the following equation: NDPI = (mir_factor * middle_IR - green_factor * green) / (mir_factor * middle_IR + green_factor * green)

The Normalized Difference Pond Index algorithm was developed by J.P Lacaux et al. (2006).The NDPI makes it possible not only to distinguish small ponds and water bodies (down to 0.01 ha) but also to differentiate vegetation inside ponds from that in their surroundings The NDPI results from the following equation: NDPI = (mir_factor * middle_IR - green_factor * green) / (mir_factor * middle_IR + 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

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

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.

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)