115
Spatiotemporal Interactions among Ecohydrological Factors
modeling with computational intelligence techniques to retrieve spatiotemporal patterns is needed. Remote sensing methods can provide such indirect measurement
for extracting areal estimates of soil moisture. Long-term and large-scale dynamic
monitoring of soil moisture can be carried out with high reliability and is labor
saving, fast, and economical. Overall, the present study is designed to produce a
soil moisture estimation algorithm via a machine learning analysis and to generate
monthly soil moisture data from May 2005 to April 2006. Then, 1 year of enhanced
vegetation index (EVI) and ET data from May 2005 to April 2006 were collected on
a monthly basis from the moderate resolution imaging spectroradiometer (MODIS)
Terra and the geostationary operational environmental satellite (GOES) images,
respectively. It is followed by investigating the spatiotemporal interactions between
soil moisture, EVI, and ET at scales at least as small as 1 km in the Tampa Bay urban
region in Florida. The seasonal comparisons among soil moisture, EVI, and ET may
improve understanding of the water cycle, urban micrometeorology, and ecohydrology to ultimately aid in urban planning and management.
6.2 MATERIALS AND METHODS
6.2.1 eStiMation of enhanced vegetation index
Vegetation indices have been developed to qualitatively and quantitatively assess
vegetation covers using spectral measurements (Bannari et al. 1995). The first earth
resources satellite, Landsat 1, launched in 1972, was a remarkable effort to use electromagnetic spectral response to evaluate vegetation cover. The uses of the red and
near-infrared spectral bands of the sensors onboard satellites are very well suited
for assessing vegetation covers (Weier and Herring 2006). The green vegetation
strongly absorbs red light (Landsat band 3) through the photosynthetic pigments
such as chlorophyll a. In contrast, the near-infrared wavelengths are half reflected
by and half passed through the leaf tissue, regardless of their color. There are more
than 35 vegetation indices (Bannari et al. 1995); most use the red and the nearinfrared bands, whereas others incorporate additional parameters to compensate for
atmospheric and/or soil background corrections. Selecting the right vegetation index
might greatly affect the accuracy of change detection of vegetation cover.
The normalized difference vegetation index (NDVI) is a normalized ratio from
−1 to +1, calculated as the difference between the near-infrared (NIR) and red bands
(RED) by their sum:
NDVI
NIR RED
NIR RED
=
−
+
(
)
(
)
.
(6.1)
EVI is designed to enhance the vegetation signal with improved sensitivity to
avoid saturation issues in high biomass regions where NDVI cannot perform well.
Whereas NDVI is chlorophyll sensitive, EVI is more responsive to canopy structural variations, including canopy type, plant physiognomy, canopy architecture, and
improved vegetation monitoring through a decoupling of the canopy background
Spatiotemporal Interactions among Ecohydrological Factors
modeling with computational intelligence techniques to retrieve spatiotemporal patterns is needed. Remote sensing methods can provide such indirect measurement
for extracting areal estimates of soil moisture. Long-term and large-scale dynamic
monitoring of soil moisture can be carried out with high reliability and is labor
saving, fast, and economical. Overall, the present study is designed to produce a
soil moisture estimation algorithm via a machine learning analysis and to generate
monthly soil moisture data from May 2005 to April 2006. Then, 1 year of enhanced
vegetation index (EVI) and ET data from May 2005 to April 2006 were collected on
a monthly basis from the moderate resolution imaging spectroradiometer (MODIS)
Terra and the geostationary operational environmental satellite (GOES) images,
respectively. It is followed by investigating the spatiotemporal interactions between
soil moisture, EVI, and ET at scales at least as small as 1 km in the Tampa Bay urban
region in Florida. The seasonal comparisons among soil moisture, EVI, and ET may
improve understanding of the water cycle, urban micrometeorology, and ecohydrology to ultimately aid in urban planning and management.
6.2 MATERIALS AND METHODS
6.2.1 eStiMation of enhanced vegetation index
Vegetation indices have been developed to qualitatively and quantitatively assess
vegetation covers using spectral measurements (Bannari et al. 1995). The first earth
resources satellite, Landsat 1, launched in 1972, was a remarkable effort to use electromagnetic spectral response to evaluate vegetation cover. The uses of the red and
near-infrared spectral bands of the sensors onboard satellites are very well suited
for assessing vegetation covers (Weier and Herring 2006). The green vegetation
strongly absorbs red light (Landsat band 3) through the photosynthetic pigments
such as chlorophyll a. In contrast, the near-infrared wavelengths are half reflected
by and half passed through the leaf tissue, regardless of their color. There are more
than 35 vegetation indices (Bannari et al. 1995); most use the red and the nearinfrared bands, whereas others incorporate additional parameters to compensate for
atmospheric and/or soil background corrections. Selecting the right vegetation index
might greatly affect the accuracy of change detection of vegetation cover.
The normalized difference vegetation index (NDVI) is a normalized ratio from
−1 to +1, calculated as the difference between the near-infrared (NIR) and red bands
(RED) by their sum:
NDVI
NIR RED
NIR RED
=
−
+
(
)
(
)
.
(6.1)
EVI is designed to enhance the vegetation signal with improved sensitivity to
avoid saturation issues in high biomass regions where NDVI cannot perform well.
Whereas NDVI is chlorophyll sensitive, EVI is more responsive to canopy structural variations, including canopy type, plant physiognomy, canopy architecture, and
improved vegetation monitoring through a decoupling of the canopy background