Drought -- National Drought Mitigation Center


Date of this Version



Published in Remote Sensing of Drought: Innovative Monitoring Approaches, edited by Brian D. Wardlow, Martha C. Anderson, & James P. Verdin (CRC Press/Taylor & Francis, 2012), pp. 75–94


Copyright © 2012 by Taylor & Francis Group, LLC. Used by permission.


Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing–based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006), which in turn may help to address agricultural development and food security issues, as well as improve early warning systems.

Many studies have demonstrated the value of Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), calculated from satellite observations for assessing vegetation cover and conditions (Tucker et al., 1985; Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005), and such data have become a common source of information for vegetation monitoring. The term vegetation condition in this chapter refers to vegetation greenness or vegetation health, as inferred from canopy reflectance values measured by satellite observations (Mennis, 2001; Anyamba and Tucker, 2005). The vegetation greenness metric is commonly calculated from time-series NDVI (Reed et al., 1994) and represents the seasonal, time-integrated NDVI at a specific date, which has been shown to be representative of indicators of general vegetation health including net primary production (NPP) and green biomass (Tucker et al., 1985; Reed et al., 1996; Yang et al., 1998; Eklundh and Olsson, 2003; Hill and Donald, 2003). As a result, VIs and VI derivatives such as time-integrated VI can be used to characterize the temporal and spatial relationships between climate and vegetation and improve our understanding of the lagged relationship between climate (e.g., precipitation and temperature) and vegetation response (Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005; Camberlin et al., 2007; Groeneveld and Baugh, 2007). Quantitative descriptions of climate-vegetation response lags can then be used to identify and predict vegetation stress during drought.