Potentiality of Satellite Based NDVI for Drought MonitoringДevelopment of remotely sensed vegetation indices have been an ongoing process. With the NDVI playing an important role for agricultural drought monitoring, it is amply felt that a review of NDVI would help provide more insight into how NDVI can be used as an indicator of drought. The VCI is NDVI normalized for each pixel on the basis of maximum statistical range over the historical record of available satellite imagery (Peters et al., 2002). This NDVI statistical range was then used to develop the Vegetation Condition Index (VCI) which is an indicator of environmental stress. Kogan (1990) states that available moisture and natural resources determine the NDVI minimum while other values, including the historical maximum are determined by the weather (Peters et al., 2002). Kogan (1990), suggested an approach to vegetation condition monitoring based on minimum and maximum NDVI values compiled per pixel over time (Peters et al., 2002). NDVI data has been extensively used for more than a decade in classifying vegetation condition. While NDVI has proven useful for timely estimation of vegetation condition, however, it does not allow for relative comparison at a pixel location or time period (Burgan and Hartford, 1993 Kogan, 1995 Peters et al., 2002). The severity of drought also varies depending on precipitation deficit, spatial extent and duration making it difficult to compare one drought with another (Peters et al., 2002). However, it is observed that drought monitoring is always a challenge as it is a slow onset natural disaster developing over months and years. It has been extensively used for assessing and monitoring vegetation dynamics, biomass production, changes in vegetation conditions, soil moisture stress (droughts), carbon sequestration and many more. The VI was later called the Normalized Difference Vegetation Index (NDVI) which have become the most widely used vegetation index today. They found the RATIO, the VI, and the TVI to be the most useful normalization techniques, with preference for the VI. These transformations were later called vegetation indices that includes the Band Ratio (RATIO), Band Difference (DVI), Band Sum (SUM), the Vegetation Index (VI), and the Transformed Vegetation Index (TVI). They used five linear combinations of the red and infrared radiance data as radiance normalization techniques to compensate for this variability (Payero et al., 2003). The photographic infrared (NIR) radiance, on the other hand, increased with time and LAI. (1979) found that the red (RED) radiance measured above corn and soybean crops decreased as the season progressed, due to increased chlorophyll absorption by increased leaf area index (LAI). Through the years, a number of vegetation indices have been evolved with simple to the most complex band combinations (Perry and Lautenschlager, 1984 Bannari et al.

These indices are used to extract information related to surface characteristics by understanding the spectral differences in the reflectance patterns between green vegetation and other surfaces. In other words, they are mathematical transformations usually ratios or linear combinations of reflectance measurements in different spectral bands lying within the visible and near infrared bands (Payero et al., 2003).

It is basically computed from the spectral bands that are sensitive to biomass and vigor.

In remote sensing technology, vegetation indices are considered as a single number that quantifies vegetation biomass for each individual pixel in a remote sensing image. Technical Specialist (Department of Climate Change & Climate Risk Management), Asian Disaster Preparedness Center, Samsen Nai, Phayathai, Bangkok, Thailand The present study attempts to review the satellite based Normalized Difference Vegetation Index (NDVI) as an indicator of drought and provide insight into the findings from various research studies that have used NDVI for drought monitoring, detection and assessment

Fews into red review