Geography Reference
In-Depth Information
temperature range accounts for effects of cloudiness and is
generally correlated with vapour pressure deficits and wind
speed. It is widely used in data-short situations and has been
evaluated against measured data in multiple studies.
The Penman equation estimates E
p
on the basis of data
on net radiation, air temperature, atmospheric humidity and
wind speed. The Priestley and Taylor equation is a simpli-
fied form of the Penman equation: it also needs data on net
radiation and air temperature but not wind speed. The
Penman
uncertainty factor of 10
30%, which is within the range of
error or uncertainty of the ground evaporation measure-
ment methods by which they are validated (Courault et al.,
2005
).
Vegetation index methods combine vegetation indices
(VI) from satellites with ground measurements of actual
evaporation (E) and meteorological data to project evapor-
ation over a wide range of biome types and scales of
measurement, from local to global estimates. The majority
of these indices use time series imagery from MODIS on
the Terra satellite to project E over seasons and years.
Vegetation indices are usually estimated from combin-
ations of the signals in visible and near infrared bands.
However, VI methods cannot estimate bare soil evapor-
ation or differences in stomatal conductance among species
and as affected by environmental factors, and these must
be approximated from ground data or additional remote
sensing data. Coefficients of determination between mod-
elled E and measured E are in the range of 0.45
-
Monteith equation is an adaptation of the Penman
equation that accounts for the effects of evaporation taking
place from vegetated surfaces, resulting in a correction to
the E
p
estimates based on the resistance of the plant canopy
(stomatal resistance) to diffusion of water fluxes. In this
way,
-
Monteith equation can be used as a
model for evaporation directly, or alternatively, if the sto-
matal resistance is taken at its minimum value, it can be
used to estimate E
p
as well.
Note that, throughout the topic, the term evaporation (E)
is used to describe evaporation from free water surfaces,
soils and plant surfaces as well as transpiration from
vegetation.
the Penman
-
-
0.95, and
root mean square errors are in the range of 10
30% of
mean E values across biomes, similar to methods that use
thermal infrared bands to estimate E and within the range
of accuracy of the ground measurements by which they are
calibrated or validated (Glenn et al.,
2010
).
-
3.4.5 Remotely sensed data for calculating actual
evaporation
At the global scale remote sensing data is an important tool
to derive estimates of evaporation patterns, E. There are
three broad approaches to remote sensing of E: direct
empirical methods (Glenn et al.,
2007
), residual methods
(Kalma et al.,
2008
) and methods based on vegetation
indices (Glenn et al.,
2010
).
Direct methods are based on semi-empirical relation-
ships between E and surface features that can be observed
with remote sensing approaches. A widely used example is
the relationship between E and the temperature difference
between vegetated and non-vegetated areas. These tem-
perature differences are observable using thermal infrared
imaging.
Residual methods are based upon computing the energy
budget for the land surface using a combination of empir-
ical relationships and modelled assumptions. Widely used
operational models such as SEBAL, S-SEBI and ALEXI
are examples of this approach. The Surface Energy Bal-
ance Algorithm for Land (SEBAL) of Bastiaanssen et al.
(
1998
) requires spatially distributed, visible, near-infrared
and thermal-infrared data, which can be taken from Land-
sat Thematic Mapper. Although approaches differ meth-
odologically, several of these methods have been validated
by comparison with moisture flux tower stations in a
variety of landscapes and are considered operational (e.g.,
Bastiaanssen and Chandrapala,
2003
; Kustas and Ander-
son,
2009
). Residual methods generally have an error or
3.4.6 Remote sensing of soil moisture and basin storage
In general, two types of soil moisture data are available
(Grayson et al.,
2002
). At the point scale, in-situ measure-
ments based on sensors at different soil depths are avail-
able. The representative area of the sensors is very small
(in the range of centimetres or metres). At the global and
regional scales remotely sensed patterns of soil moisture
are available. Global estimates of soil moisture are cur-
rently made from space by several sensors on-board satel-
lites. Soil moisture retrieval has been the subject of many
studies and measurement campaigns. Various sensors are
currently operational that can provide estimates of soil
moisture. Most of these sensors operate in the microwave
domain, and can be active (radar) or passive (radiometers).
Advantages of the microwave domain are its independence
of solar illumination (day and night capability), and its lack
of cloud cover sensitivity. Lower frequencies (longer
wavelengths) have the additional advantages of a relatively
high sensitivity to soil water content, a deeper soil penetra-
tion, and less disturbance by vegetation and atmosphere
(Hurkmans et al.,
2004
). However, in spite of these advan-
tages there are still many challenges in reliably obtaining
soil moisture estimates, especially in densely vegetated or
inhabited areas (radio-frequency interference). In addition,
only the soil moisture content of the top few centimetres of
the soil profile is typically estimated by this technology
(this has to be converted to root zone moisture) and,
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