grasslands. In summary, the model was satisfactorily able to recreate LE variation in
2011 with trivial bias (À2 Wm
À2 - +4 Wm
À2 ). As aforementioned in the previous
section, validation of satellite-based and ground-based EF is again hampered by the
issues with respect to the footprint mismatch, model simplification, and errors in
NDVI, T S , and T a datasets.
15.4 Agricultural Drought
Identification of drought conditions based primarily on satellite data stems from the
fact that large-scale (e.g., state-level) changes in vegetation condition on the Earth
surface is controlled by weather (e.g., temperature and precipitation), and its impacts
on vegetation can be precisely estimated from space. Droughts slow down crucial
plant activities such as photosynthesis, respiration, and transpiration to maintain its
growth and green color, and thus satellite data and methods can help identify and
quantify these trivial changes in vegetation condition (Tucker and Choudhury 1987).
The underlying premise in this method is that large-scale decline in vegetation
greenness as measured in NDVI units in comparison to the historical average can be
associated with droughts since drought is a large-scale natural hazard and often
results in decrease in the photosynthetic rate of plants, thereby NDVI. As a result,
satellite remote sensing data and methods assist in mapping important drought
characteristics such as spatial extent, intensity, onset, and duration (Wilhite 2005)
by taking advantage of frequent revisit capability of moderate-resolution instruments
such as AVHRR, MODIS, and VIIRS (e.g., two images per day from one instrument) which provide daily spatially continuous observations across the Earth space.
15.4.1 Normalized Difference Vegetation Index (NDVI)
NDVI has been a standard measure of vegetation greenness or health readily
available from all the moderate-resolution polar-orbiting sensors such as AVHRR,
MODIS, and VIIRS that are suitable for monitoring of regional vegetation systems
(Jarlan et al. 2005; Funk and Brown 2006; Ji et al. 2008). Green healthy vegetation
tends to have 16-day 1 km MODIS-NDVI products along with quality layers were
retrieved from the LPDAAC website (https://lpdaac.usgs.gov/). Because these products are also used in the ET research, preprocessing of products and their features has
been previously explained in great detail in the III.II section of this chapter.
312
A. L. Yagci and M. T. Yilmaz
2011 with trivial bias (À2 Wm
À2 - +4 Wm
À2 ). As aforementioned in the previous
section, validation of satellite-based and ground-based EF is again hampered by the
issues with respect to the footprint mismatch, model simplification, and errors in
NDVI, T S , and T a datasets.
15.4 Agricultural Drought
Identification of drought conditions based primarily on satellite data stems from the
fact that large-scale (e.g., state-level) changes in vegetation condition on the Earth
surface is controlled by weather (e.g., temperature and precipitation), and its impacts
on vegetation can be precisely estimated from space. Droughts slow down crucial
plant activities such as photosynthesis, respiration, and transpiration to maintain its
growth and green color, and thus satellite data and methods can help identify and
quantify these trivial changes in vegetation condition (Tucker and Choudhury 1987).
The underlying premise in this method is that large-scale decline in vegetation
greenness as measured in NDVI units in comparison to the historical average can be
associated with droughts since drought is a large-scale natural hazard and often
results in decrease in the photosynthetic rate of plants, thereby NDVI. As a result,
satellite remote sensing data and methods assist in mapping important drought
characteristics such as spatial extent, intensity, onset, and duration (Wilhite 2005)
by taking advantage of frequent revisit capability of moderate-resolution instruments
such as AVHRR, MODIS, and VIIRS (e.g., two images per day from one instrument) which provide daily spatially continuous observations across the Earth space.
15.4.1 Normalized Difference Vegetation Index (NDVI)
NDVI has been a standard measure of vegetation greenness or health readily
available from all the moderate-resolution polar-orbiting sensors such as AVHRR,
MODIS, and VIIRS that are suitable for monitoring of regional vegetation systems
(Jarlan et al. 2005; Funk and Brown 2006; Ji et al. 2008). Green healthy vegetation
tends to have 16-day 1 km MODIS-NDVI products along with quality layers were
retrieved from the LPDAAC website (https://lpdaac.usgs.gov/). Because these products are also used in the ET research, preprocessing of products and their features has
been previously explained in great detail in the III.II section of this chapter.
312
A. L. Yagci and M. T. Yilmaz