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)

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.

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.

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)

LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. It determines the size of the interface for exchange of energy (including radiation) and mass between the canopy and the atmosphere. This is an intrinsic canopy primary variable that should not depend on observation conditions. LAI is strongly non linearly related to reflectance. Therefore its estimation from remote sensing observations will be strongly scale dependent (Garrigues et al. 2006a Weiss et al. 2000). Note that vegetation LAI as estimated from remote sensing will include all the green contributors i.e. including understory when existing under forests canopies.

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 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 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)