Vegetation indices use various combinations of multi-spectral satellite data to produce a single image representing vegetative vigour/stress/drought. Crops have a characteristic spectral response pattern in which visible blue and red energy is absorbed strongly, visible green light is reflected weakly (thus a green colour) and near-infrared energy is very strongly reflected. Because of this characteristic spectral response pattern, many of the Vegetation Index Models use only the red and near-infrared imagery bands. In the past remote sensing based drought indices relied on few optical bands as provided by NOAA AVHRR or Landsat TM sensors, a newer generation tries to use multi-band capabilities e.g. the MODIS sensor onboard terra/aqua satellites1.
Enhanced Vegetation Index – EVI 2,3,4
The Enhanced Vegetation Index (EVI) is calculated similarly to the popular Normalized Difference Vegetation Index (NDVI), which is used to monitor spectral characteristics of vegetation. The EVI is meant to produce outputs of higher quality than the NDVI since it corrects for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. Also, the EVI data product does not become saturated as easily as the NDVI when viewing rainforests and other areas of the Earth with large amounts of chlorophyll. The EVI nonetheless does not cancel out the entire noise in the imagery. Most importantly, the EVI does not take the topographic effect into consideration, which is defined as the variation in the radiance that accompanies a change in orientation from a horizontal to an inclined surface, in response to a change in light source and sensor position.
An EVI time-series can be used to identify temporal changes in vegetation by reflecting seasonal and inter-annual variations of climatic parameters such as irradiance, temperature, and rainfall. Phenology and greenness metrics (PGMs) based on EVI considered in the EviDENz projects include the start of growing season (SOS), the end of growing season (EOS), peak of the season (PEAK), and position of peak day (POP).
Vegetation Condition Index – VCI 6,7
The VCI compares the current Enhanced Vegetation Index (EVI) to the range of values observed in the same period in previous years. The EVI is expressed in percentages, which allows it to situate the currently observed value on a range between minimum and maximum based on the values of previous years.
Low values indicate bad vegetation conditions, while high values indicate good vegetation conditions. The VCIj values between 50% to 100 % indicate optimal or above-normal conditions. At a VCIj value of 100%, the EVIj is equal to the maximal EVI value measured (EVImax). Different degrees of a drought severity is indicated by VCIj values.
Integration into the SENDAI Framework for Disaster Risk Reduction
In order to meet the targets set in the SENDAI Framework for Disaster Risk Reduction (SFDRR), a variety of indicators are included in the EviDENz projects according to the Technical Guidance for Monitoring and Reporting on Progress in Achieving the Global Targets:
Target B: Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared to 2005-2015.
Target C: Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030.
 Wang, L. and Qu, J.J. 2007. NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing. Geophys. Res. Left., 34, L20405
 Huete A.R., Justice C. 1999. MODIS vegetation index (MOD13) algorithm theoretical basis document. Ver. 3.
 Kogan F.N. 1990. Remote Sensing of Weather Impacts on Vegetation in Homogeneous Areas. Int. J. Remote sensing, 11:1405-1419
 Forkel M, Wutzler T (2015) greenbrown - land surface phenology and trend analysis. A package for the R software. Version 2.2, 2015-04-15, http://greenbrown.r-forge.r-project.org/
 Kogan F.N. 1995. Application of Vegetation Index and Brightness Temperature for Drought Detection. Adv. Space Research, 11:91-100