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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
soil moisture (Figure 6.6c) showed a relatively consistent cyclic pattern. The varia6.6c) showed a relatively consistent cyclic pattern. The varia.6c) showed a relatively consistent cyclic pattern. The variation of EVI and soil moisture showed a full cycle of sinusoidal wave shape, which
reflects a high degree of correlation between EVI and soil moisture. For an example,
the highest and lowest values for both EVI and soil moisture appear in June and
February, respectively. However, this could also be partially because soil moisture
was estimated using EVI and LST in a GP model.
Overall, time series data analyses for pairwise ET–soil moisture, ET–EVI, and
soil moisture–EVI comparisons reveal that soil moisture more closely follows the
same temporal trend as EVI in the context of urban microscale ecohydrologic assessment. Such an ecohydrologic assessment can support urban landscape management
to further reflect the dynamics of urban LULC as well as measure and analyze sustainability through metrics applied at various spatial scales, from neighborhood to
regional, in the urban regions.
6.4 CONCLUSIONS
It has long been recognized that soil moisture at 1–2 m below ground level regulates atmospheric energy exchange at land surface and affects all aspects of urban
ecosystems. The urban patch can exchange heat by convection (sensible heat flux),
conduction (contact with soil), evaporation (latent heat flux), radiation (long- and
shortwave), and respiration (latent and sensible). Specifically, water loss rates
influence ecosystem productivity by controlling water availability and through
feedback on temperature via latent heat transfer (evaporative cooling). Yet, water
use efficiency varies among patches, plant species, and especially location on the
urban gradient. In addition, local interactions of human and biophysical processes
affect the landscape patterns associated with a number of ecohydrologic factors
such as precipitation, ET, soil moisture, and vegetation cover. To explore such biophysical conditions, this study showed the multitemporal ecohydrologic assessment of urban water storage and ecosystem dynamics in connection to possible
seasonality effects.
Soil moisture as a useful drought index has vital significance in the fields of
climate, hydrology, ecology, and agriculture. ET is a key component of global
climate systems, looping the water cycle, energy cycle, and carbon cycle. Both
parameters play important roles on urban hydrology, and both are closely related
to vegetation cover and land use dynamics in urban regions. In this study, soil
moisture was estimated using EVI and LST, both of which are products from
MODIS Terra with a 1 km × 1 km resolution in 1 year (May 2005–April 2006).
A GP model was designed to produce the soil moisture model by linking MODIS
measurements to ground-truth soil moisture measurements. ET data derived by
GOES data were included for comparative analysis. Overall, the time series ET
data showed a multipeak pattern with salient oscillations driven by the heterogeneity of LULC and LST. There was a high positive correlation between EVI and
soil moisture. Such findings may offer reference basis for urban planning and
design in the future.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
soil moisture (Figure 6.6c) showed a relatively consistent cyclic pattern. The varia6.6c) showed a relatively consistent cyclic pattern. The varia.6c) showed a relatively consistent cyclic pattern. The variation of EVI and soil moisture showed a full cycle of sinusoidal wave shape, which
reflects a high degree of correlation between EVI and soil moisture. For an example,
the highest and lowest values for both EVI and soil moisture appear in June and
February, respectively. However, this could also be partially because soil moisture
was estimated using EVI and LST in a GP model.
Overall, time series data analyses for pairwise ET–soil moisture, ET–EVI, and
soil moisture–EVI comparisons reveal that soil moisture more closely follows the
same temporal trend as EVI in the context of urban microscale ecohydrologic assessment. Such an ecohydrologic assessment can support urban landscape management
to further reflect the dynamics of urban LULC as well as measure and analyze sustainability through metrics applied at various spatial scales, from neighborhood to
regional, in the urban regions.
6.4 CONCLUSIONS
It has long been recognized that soil moisture at 1–2 m below ground level regulates atmospheric energy exchange at land surface and affects all aspects of urban
ecosystems. The urban patch can exchange heat by convection (sensible heat flux),
conduction (contact with soil), evaporation (latent heat flux), radiation (long- and
shortwave), and respiration (latent and sensible). Specifically, water loss rates
influence ecosystem productivity by controlling water availability and through
feedback on temperature via latent heat transfer (evaporative cooling). Yet, water
use efficiency varies among patches, plant species, and especially location on the
urban gradient. In addition, local interactions of human and biophysical processes
affect the landscape patterns associated with a number of ecohydrologic factors
such as precipitation, ET, soil moisture, and vegetation cover. To explore such biophysical conditions, this study showed the multitemporal ecohydrologic assessment of urban water storage and ecosystem dynamics in connection to possible
seasonality effects.
Soil moisture as a useful drought index has vital significance in the fields of
climate, hydrology, ecology, and agriculture. ET is a key component of global
climate systems, looping the water cycle, energy cycle, and carbon cycle. Both
parameters play important roles on urban hydrology, and both are closely related
to vegetation cover and land use dynamics in urban regions. In this study, soil
moisture was estimated using EVI and LST, both of which are products from
MODIS Terra with a 1 km × 1 km resolution in 1 year (May 2005–April 2006).
A GP model was designed to produce the soil moisture model by linking MODIS
measurements to ground-truth soil moisture measurements. ET data derived by
GOES data were included for comparative analysis. Overall, the time series ET
data showed a multipeak pattern with salient oscillations driven by the heterogeneity of LULC and LST. There was a high positive correlation between EVI and
soil moisture. Such findings may offer reference basis for urban planning and
design in the future.