Total dry matter of plant or crop yield are related to APAR (Kumar and Monteith, 1982; Monteith, 1977), and the NDVI is highly correlated with APAR (Daughtry et al. 1983; Hatfield et al., 1984; Wiegand and Richardson, 1984; Asrar et al., 1985). Thus, the use of NDVI data for crop condition assessment, yield estimation, and hence drought monitoring has been intensively analyzed (Aase and Siddoway, 1980; Tucker, 1980b; Tucker et al., 1981; Weigand and Richardson, 1984; Boken and Shaykewich, 2002). The NDVI has also been related to many vegetation canopy characteristics, including leaf area index (LAI), green biomass, and percent cover (Wiegand and Richardson, 1987).
Droughts reduce photosynthesis on account of low rainfall, which reduces total dry matter accumulation and yields and results in lower NDVI values (figure 5.5). In arid and semiarid areas, the rainfall is the principal determinant of primary production and has been found to be highly correlated with the NDVI, although this correlation differs slightly across various climatic regimes (figure 5.6; Malo and Nicholson, 1990; Nicholson et al., 1990; Tucker and Nicholson, 1999).
Droughts usually begin unnoticed and develop cumulatively with their impacts that are not immediately observable by ground data (Kogan, 1997; Kogan, 2002). The key to using the NDVI to monitor and assess droughts is thus to have accurate time series satellite data over long periods. This has been achieved with intercalibrated data from the multiple series of AVHRR sensors on board the polar-orbiting meteorological satellites of the U.S. National Oceanic and Atmospheric Administration (NOAA). Below-normal NDVI values would indicate the occurrence of droughts. Below are some examples of using NDVI time series data derived from NOAA-AVHRR to study drought patterns and their impacts on agricultural production for Africa.
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