Clouds typically have a patchy warm and cool pattern and scattered rain showers produce a
pattern of streaks. A heavy overcast layer reduces thermal contrast between terrain objects
because of re-radiation of energy between the terrain and the cloud cover. Clouds consist of tiny
divided particles of ice and water that have the same temperature as the surrounding air. Energy
radiated from the earth surface does not penetrate clouds but is absorbed and reradiated. Smoke
plumes in contrast, consist of ash particles and other combustion products so fine that they are
penetrated by long wavelengths of thermal radiation. Furthermore, smoke plumes are most often
warmer than clouds.
Thermal images of vegetation should be interpreted with care because evapotranspiration from
vegetation is an active process controlled by the vegetation or crop itself. During the day,
transpiration of water lowers the leaf temperature, causing vegetation to have a cool signature
relative to the surrounding soil. At night the insulating effect of leafy foliage and the high water
content retain heat, which results in warm nighttime temperatures. Consequently, if there is a
water shortage for the vegetation, it cannot evaporate and its temperature will rise.
The energy balance of the earth’s surface forms the basis of interpretation of thermal images.
The energy balance of bare soil, rock and vegetated surfaces makes it possible to model the
thermal behaviour which helps to interpret images. In the same way, thermal images are useful
to set up the thermal balance of a surface. The heat balance of the earth’s surface can be
described in terms of incoming and outgoing fluxes:
Q = LE + H + G
Q:
Net radiation [W.m
-2
];
LE: Latent heat flux [W.m
-2
];
H:
Sensible heat flux [W.m
-2
];
G:
Heat flux into the soil [W.m
-2
].
The term Q consists of a short-wave and a long wave component. The short-wave component is
the energy radiated by the sun, long wave radiation is emitted by the objects at the surface. Q
can be described as (Jackson, 1985):
Q = (1-
α
) R
s
+
ε
(R
l
-
σ
T
s
4
)
α
:
Albedo;
R
s
: Short-wave incoming radiation flux [W.m
-2
];
R
l
: Long wave sky radiation flux [W.m
-2
];
ε
:
Emission coefficient;
σ
:
Stefan Boltzmann constant [5.67*10
-8
W.m
-2
.K
-4
];
T
s
: Temperature of the surface [K].
This
preview
has intentionally blurred sections.
Sign up to view the full version.
61
Remote sensing imagery can be useful to estimate some of the components of the energy
balance. Remote sensing is especially useful to assess the spatial and temporal variability of
these variables. Albedo can be estimated from the visible and near infrared channels and surface
temperature can be estimated from bands in either or both of the thermal atmospheric windows.
Examples and detailed descriptions of surface energy balance studies can be found in Menenti
(1993; 1984), Nieuwenhuis et al. (1985) and Nieuwenhuis (1993).

This is the end of the preview.
Sign up
to
access the rest of the document.
- Winter '12
- JOHN
- Remote Sensing, Electromagnetic spectrum, µm, Infrared
-
Click to edit the document details