Agriculture Reference
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radiative transfer theory for optimized retrieval of vegetation properties
from satellite data (Verstraete and Pinty, 1996).
Ideally, optimized and distance-based VIs are superior to slope-based
indices because they attempt to minimize or remove atmosphere and soil
brightness noise that may limit a quantitative assessment of green vegeta-
tion; however, their robustness over global vegetation conditions remains
to be tested. The simplicity of sloped-based indices both in terms of nu-
merical results and interpretation has meant that such indices, such as the
well-known NDVI, can be used for vegetation monitoring and drought
detection at regional as well as global scales.
Re
lationship between NDVI and Crop Yield
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 Richard-
son, 1984; Asrar et al., 1985). Thus, the use of NDVI data for crop condi-
tion 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 re-
duces total dry matter accumulation and yields and results in lower NDVI
values (figure 5.5). In arid and semiarid areas, the rainfall is the princi-
pal 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. Na-
tional 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.
[65],
Line
——
-0.2
——
Norm
* PgEn
[65],
D
rought Monitoring Applications for Africa
Monitoring drought and crop production in Africa is not straightforward
in part because of the large footprint of the NOAA-NDVI data (
∼
4-8 km
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