Crop Early Warning Index: provides four alert levels based on the combination of VCI (Vegetation Condition Index) TAI (Temperature Anomaly Index) and PrI (Precipitation Index) anomalies. In particular: CREW = 1 if only VCI anomaly is present. CREW = 2 if VCI and TAI anomalies are present. CREW = 3 if VCI and PrI anomalies are present. CREW = 4 if VCI TAI and PrI anomalies are present.

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 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 Difference Vegetation Index (NDVI) is a measure of the amount and vigor of vegetation on the land surface and NDVI spatial composite images are developed to more easily distinguish green vegetation from bare soils. In general NDVI values range from -1.0 to 1.0 with negative values indicating clouds and water positive values near zero indicating bare soil and higher positive values of NDVI ranging from sparse vegetation (0.1 - 0.5) to dense green vegetation (0.6 and above).

The Pigment Specific Simple Ratio (chlorophyll index) algorithm was developed by Blackburn (1998). It investigates the potential of a range of spectral approaches for quantifying pigments at the scale of the whole plant canopy. When applying existing narrow-band pigment indices the PSSR algorithms have the strongest and most linear relationships with canopy concentration per unit area of Chl a (Chlorophyll a) Chl b (Chlorophyll b) and Cars (carotenoids). The PSSRa results from the following equation: PSSRa = (IR_factor * near_IR) / (red_factor * red)

The Pigment Specific Simple Ratio (chlorophyll index) algorithm was developed by Blackburn (1998). It investigates the potential of a range of spectral approaches for quantifying pigments at the scale of the whole plant canopy. When applying existing narrow-band pigment indices the PSSR algorithms have the strongest and most linear relationships with canopy concentration per unit area of Chl a (Chlorophyll a) Chl b (Chlorophyll b) and Cars (carotenoids). The PSSRa results from the following equation: PSSRa = (IR_factor * near_IR) / (red_factor * red)

The Pigment Specific Simple Ratio (chlorophyll index) algorithm was developed by Blackburn (1998). It investigates the potential of a range of spectral approaches for quantifying pigments at the scale of the whole plant canopy. When applying existing narrow-band pigment indices the PSSR algorithms have the strongest and most linear relationships with canopy concentration per unit area of Chl a (Chlorophyll a) Chl b (Chlorophyll b) and Cars (carotenoids). The PSSRa results from the following equation: PSSRa = (IR_factor * near_IR) / (red_factor * red)

The Pigment Specific Simple Ratio (chlorophyll index) algorithm was developed by Blackburn (1998). It investigates the potential of a range of spectral approaches for quantifying pigments at the scale of the whole plant canopy. When applying existing narrow-band pigment indices the PSSR algorithms have the strongest and most linear relationships with canopy concentration per unit area of Chl a (Chlorophyll a) Chl b (Chlorophyll b) and Cars (carotenoids). The PSSRa results from the following equation: PSSRa = (IR_factor * near_IR) / (red_factor * red)

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 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)