Review article
Land surface temperature and emissivity estimation
from passive sensor data: theory and practice; current trends
Prasanjit Dash1,
Frank-M. Göttsche, Folke-S. Olesen, and Herbert Fischer
Postfach 3640, D-76021 Karlsruhe, Germany
1Corresponding
author: E-Mail: prasanjit.dash@imk.fzk.de; Tel.: +49-7247-82-3822;
Fax:
+49-7247-82-4742; WWW: http://www.fzk.de/imk/imk2/isys
International Journal of Remote Sensing, vol. 23, issue 13, pp. 2563-2594
|
Abstract
Land Surface Temperature (LST) and emissivity for
large areas can only be derived from
surface-leaving radiation measured by satellite sensors. These measurements
represent the integrated effect of the surface and are, thus, for many
applications, superior to point measurements on the ground, e.g. in Earth’s
radiation budget and climate change detection. Over the years, a substantial
amount of research was dedicated to the estimation of LST and emissivity from
passive sensor data. This paper provides the theoretical basis and gives an
overview of the current status of this research. Sensors operating in the
visible, infrared, and microwave range onboard various meteorological
satellites are considered, e.g. Meteosat-MVIRI, NOAA-AVHRR, ERS-ATSR,
Terra-MODIS, Terra-ASTER, and DMSP-SSM/I. Atmospheric effects on measured
brightness temperatures are described and atmospheric corrections using
Radiative Transfer Models (RTM) are explained. The substitution of RTM with
Neural Networks (NN) for faster forward calculations is also discussed. The
reviewed methods for LST estimation are the single-channel method, the
Split-Window Techniques (SWT), and the multi-angle method, and, for emissivity
estimation, the Normalized Emissivity Method (NEM), the Thermal Infrared
Spectral Indices (TISI) method, the spectral ratio method, alpha residuals,
Normalized Difference Vegetation Index (NDVI)-based methods,
classification-based emissivity, and the Temperature Emissivity Separation
(TES) algorithm.
1. Introduction
Surface temperature is an important quantity for many
environmental models, e.g. (1) energy and water exchange between atmosphere and
surface, (2) numerical weather prediction, (3) global ocean circulation, (4)
climatic variability, etc. (Valor and Caselles 1996). Only by remote sensing
from satellites are measurements of surface temperature feasible on a regional
or global scale. Satellite sensors measure the surface-leaving radiance
modified by the atmosphere in different spectral channels; the corresponding
brightness temperatures are calculated by reversing Planck’s function. Various
algorithms exist to estimate Land Surface Temperature (LST) from brightness
temperatures and auxiliary data. LST is sensitive to vegetation and soil
moisture; hence it can be used to detect land surface changes, e.g. tendencies
towards desertification. Geographical Information Systems allow a combined
interpretation of satellite-derived LST and other geo-data, e.g. vegetation
cover and soil-type maps (Andersen 1997).
LST is the temperature measured at the Earth's surface
and is regarded as its skin temperature. However, the land surface is far from
being a skin or a homogenous two-dimensional entity: it is composed of
different materials with various geometries both of which complicate LST
estimation (Qin and Karnieli 1999). Additionally, the surface is commonly even
more inhomogeneous at low-resolution satellite spatial observations. Only for
homogeneous surfaces at thermal equilibrium LST can be defined unambiguously.
In remote sensing, LST is defined as the `surface radiometric temperature´
corresponding to the instantaneous field-of-view of the sensor (Prata et al.
1995) or, more precisely, as the `ensemble directional radiometric surface
temperature´ (Norman and Becker 1995). The term `ensemble´ depicts the bulk
contribution of an inhomogeneous pixel. For a given sensor viewing direction,
LST depends on the distribution of temperature and emissivity within a pixel
and the spectral channel of measurement (Becker and Li 1995). Thermodynamic
temperature is measured at surface/atmosphere/thermometer point-of-contact, and
is based on the `zeroth law of thermodynamics´, i.e. two systems in equilibrium
with a third system, e.g. a thermometer, are also in equilibrium with each
other. Here, the surface has to be clearly defined, i.e. it should be
isothermal and homogeneous (the sub-systems have the same thermodynamic
temperature). Only for homogeneous isothermal surfaces radiometric and
thermodynamic temperatures are equivalent; even for a small-scale ensemble of black
bodies at different temperatures there is no equivalent black body temperature
yielding the same distribution of spectral radiance (Norman and Becker 1995).
LST represents the integrated effect of the whole 'ensemble' within a pixel;
thus, LST is not confined to homogeneous isothermal surfaces. However, the
definition of surface depends on the acquisition device, e.g. its
scale/resolution, and should match the scale of the model (Becker and Li 1995).
The definition of LST given above is probably the best for Earth's radiation
budget and for canopy temperature estimation.
In order to obtain LST from space radiometry, three
main effects have to be considered and corrected for: atmospheric, angular, and
emissivity effects. The three major effects of the atmosphere are absorption,
upward atmospheric emission, and the downward atmospheric irradiance reflected
from the surface (Franca and Cracknell 1994). In the 8-12 mm infrared
(IR)-window region, aerosol absorption and scattering are negligible and
generally ignored (Prata et al 1995); water vapour is principally responsible
for atmospheric effects. Other gases, e.g. ionospheric O3 and CO2
also influence atmospheric transmission but unlike water vapour, O3
and CO2 vary slowly. Furthermore, CO2 is evenly distributed
in the atmosphere, and tropospheric O3 is of local importance only.
Hence, water vapour, which is poorly mixed and varies on short time-scales, is
the most relevant gas. Consequently, frequent information about the state of
the atmosphere, especially the temperature and water-vapour profiles, are
needed. Radiosonde data provide such information and yield results which, at
the location of the radiosonde, are more accurate than any other method for the
troposphere, but the soundings have to be synchronous and co-located with the
satellite measurements. Therefore, information from vertical sounders, e.g.
TIROS Operational Vertical Sounder (TOVS) onboard National Oceanic and
Atmospheric Administration's (NOAA) polar-orbiters, are better suited for large-scale
studies (Reutter et al. 1994, Lakshmi and Susskind 2000). Satellite sensors observe the land surface at
different viewing geometries and, therefore, estimated brightness temperatures
must be compensated for the zenith angle (Kimes et al. 1980, 1983, Ignatov and
Dergileva 1994). LST algorithms must account for this effect in order to
provide results that are independent of observation geometry. Generally, four
different atmospheric correction methods are practised (Becker and Li 1990a),
all of which require a priori
emissivity information:
the single-channel method, the Split-Window Technique
(SWT), the multi-angle method, and combinations of SWT and multi-angle method,
e.g. for Along Track Scanning Radiometer (ATSR) onboard European Remote Sensing
Satellite (ERS)-1. The emissivity of land surfaces, unlike oceans, can differ
significantly from unity and varies with vegetation, surface moisture, and
roughness (Nerry et al. 1988, Salisbury and D’ Aria 1992).
Currently, LST cannot be determined from a single
satellite system with the high temporal and spatial resolution necessary for
many applications. Combining NOAA Advanced Very High Resolution Radiometer
(AVHRR) and Meteosat Visible and Infrared Imager (MVIRI) IR data yields
measurements with the temporal resolution of Meteosat (~30 min) and a spatial
resolution close to that of AVHRR (~2.5 km) (Olesen et al. 1995). The Spinning
Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat Second Generation,
scheduled for launch in 2002, will provide data with an unprecedented
combination of spatial, temporal, and spectral resolution from geostationary
orbit (Cihlar et al. 1999). SEVIRI will improve sufficiently upon MVIRI's
capabilities to estimate LST as well as emissivity (Dash et al. 2001). Some
sensors provide multispectral IR data with an even higher spectral resolution,
e.g. the airborne Thermal Infrared Multispectral Scanner (TIMS, 6 channels
between 8-12.5 mm) on the National Aeronautics and
Space Administration's (NASA) C-130 aircraft (Schmugge et al. 1998), Advanced
Spaceborne Thermal Emission Reflectance Radiometer (ASTER, channels 10-14
between 8.1-11.6 mm) (Gillespie et al. 1996, 1998,
1999) and the Moderate Resolution Imaging Spectrometer (MODIS, 4 `land´
channels between 8.4-13.5 mm) (Wan and Li 1997,
Wan 1999), both onboard NASA’s Terra-spacecraft. Passive microwave data from
the Special Sensor Microwave/Imager (SSM/I) onboard Defense Meteorological
Satellite Program (DMSP) spacecraft is also used for LST determination (Xiang
and Smith 1997). en 0.6 and
1.0 (also sea surface emissivity) and depends on surface conditions,
polarization, and frequency. For frequencies from 1 to 100 GHz atmospheric
transmissivity t ranges from 0.90 to 0.99. It is more easily
determined than emissivity by using line-by-line codes and spectroscopic data,
e.g. from the High Resolution Transmittance (HITRAN) database (Rothman et al.
1998; http://www.hitran.com). Hence, e(n) is the most critical parameter for accurate estimates
of LST in the microwave region. Assuming that changes in t are small and using a method similar to IR SWT, Givri
(1997) derived the following microwave SWT:
Conclusions
Thermodynamic point measurements are of limited use
for LST estimation: on large scales, i.e. continental, only satellite
measurements are applicable. Since a global in
situ LST database at satellite sensor spatial resolution is unfeasible,
LST/LSE algorithms are usually validated with field measurements of major
vegetation and soil types. The estimation of LST and LSE from passive sensor
data is an important and ongoing field of research. Owing to its complex and
underdetermined nature, the problem is not fully solved with the accuracy and
generality desired by many researchers. In the near future, global LST maps
with an accuracy of ± 1°C and LSE maps with an accuracy
of ±0.005 will be available for many surface types, e.g.
from Terra measurements.
Starting from the physics of radiative transfer, this
article presented the theoretical basis of LST and LSE extraction from radiance
measurements. Within the atmospheric windows, the TOA radiance is dominated by
surface-leaving radiance. Therefore, LST can be estimated from satellite based
IR measurements. However, for accurate LST estimations three atmospheric
contributions have to be accounted for: a) attenuation (absorption) of
Earth-emitted radiance by the atmosphere, b) upwelling atmospheric radiance, c)
reflected downwelling atmospheric irradiance (LSE must be known). The
contributions of LST and LSE to at-ground radiance have to be decoupled;
therefore, the determination of LST and LSE is closely linked and is reviewed
together. A
single `best´ method for LST/LSE
determination does not exist. This review helps to select the most appropriate
method for a given application and the available information.
The
single IR channel method for atmospheric correction (Price 1983) is accurate and globally applicable; for satellites
with a single IR channel, e.g. Meteosat, it is also the only applicable method. However, accurate information about the state of the
atmosphere and LSE for each IR measurement is needed
and the computational cost is high. Substituting the RTM with a NN allows fast
and accurate LST determination with the single-channel method (Göttsche and
Olesen 2001).
The
SWT for sea surfaces (McMillin 1975) can be applied to land surfaces, if LSE is
known a priori. Once the coefficients are calculated, SWT formulations are fast and easy to use. However,
the coefficients are only valid for the datasets used to derive them. François
and Ottlé (1996) demonstrated that the classical linear split-window equation
is invalid on a larger scale and showed that quadratic formulations of SWT,
which account for LSE and water vapour, yield better results. The choice of a
SWT formulation depends on the availability of further information: the SWT
formulations of Price (1984) and Becker and Li (1990) account only for LSE
variations, while the coefficients of Sobrino et al. (1991) and Becker and Li
(1995) also depend on the state of the atmosphere. The algorithm of Kerr et al. (1992) classifies surfaces either as bare soil or as vegetated area and
combines their contributions to obtain effective LST.
Multi-angle
methods, as proposed by Chedin et al.
(1982), can be applied if simultaneous measurements of the same area for different
viewing angles are available. The method
assumes spatial uniformity of the atmospheric column and the observations must
have a significant path difference, otherwise the algorithm looses stability
(Prata 1993). The directional variation of emissivity also has to be known.
If no a priori knowledge is available, the Physics-based algorithm of Wan and Li (1997), developed
specifically for MODIS data, can be used for combined retrievals of LST and LSE. The method needs day/night pairs of observations
in at least 7 channels and assumes that LSE is temporally invariant. Unlike the TISI day/night approach (Li and Becker 1993), Wan and
Li's method does not rely on assumed BRDF and atmospheric information.
Methods
for LSE determination either aim at relative or absolute emissivity: the `relative´ methods retrieve the spectral
shape/ratio of emissivities, while the `absolute´ methods depend on critical
assumptions (problem of the missing equation). TISI (Becker and Li 1990b) is a
robust method for estimating relative emissivity. In order to obtain absolute
LSE using the TISI day/night method, the BRDF is needed and LSE is assumed to
be temporally invariable. If the LSE of bare ground and vegetation as well as the vegetation structure and distribution is known, NDVI
based methods can be used (Valor and
Caselles 1996).
The classification based emissivity method (Snyder et
al. 1998) needs information about cover type and the amount of vegetation. The TES method of
Gillespie et al. (1996, 1998) can be applied if atmospheric information and a
sufficient number of IR channels are available. However, the accuracy of the downwelling atmospheric irradiance
is a critical issue.
Algorithms
developed for multispectral data show promising results, e.g. as highlighted by the algorithms developed for
sensors onboard Terra, e.g. TES for ASTER data, which combines several methods.
Combining IR and microwave data also should prove to be beneficial, especially
for the tropics, where cloud-cover is a major limitation for IR-based methods.
The
AAC (Gu et al. 2000) facilitates atmospheric correction of hyperspectral data
from SEBASS without additional information about the state of the atmosphere.
However, the extraction of LST and LSE from at-ground radiance has to be
performed separately. Despite the increased possibilities offered by
multispectral and hyperspectral data, the `classical´ methods are still relevant for long-term (historic)
datasets and form a base for the development of new algorithms.
List of abbreviations
|
Acronym
LST
NOAA
AVHRR
NASA
DMSP
SSM/I
ERS
ASTER
MODIS
ATSR
TIMS
MVIRI
SEVIRI
TOVS
TIROS
VAS
RTM
RTE
MODTRAN
LOWTRAN
HITRAN
ECMWF
IR
TOA
SWT
LSE
NEM
TISI
NDVI
TES
BRDF
NN
|
Meaning
Land
Surface Temperature
National Oceanic and Atmospheric Administration
Advanced Very High Resolution Radiometer
National Aeronautics and Space Administration
Defense Meteorological Satellite Program
Special Sensor Microwave/Imager
European Remote Sensing Satellites
Advanced Spaceborne Thermal Emission Reflectance
Radiometer
Moderate Resolution Imaging Spectrometer
Along Track Scanning Radiometer
Thermal Infrared Multispectral Scanner
Meteosat Visible and Infrared Imager
Spinning Enhanced Visible and Infrared Imager
TIROS Operational Vertical Sounder
Television and Infrared Observational Satellite
VISSR (Visible and Infrared Spin Scan Radiometer)
Atmospheric Sounder
Radiative
Transfer Model
Radiative
Transfer Equation
Moderate
Resolution Transmittance (code)
Low Resolution Transmittance (code)
High
Resolution Transmittance (database)
European Centre for Medium Range Weather Forecasts
Infrared (0.7 – 300 mm)
Top
Of the Atmosphere
Split
Window Technique
Land Surface Emissivity
Normalized
Emissivity Method
Thermal Infrared Spectral Indices
Normalized
Difference Vegetation Index
Temperature Emissivity Separation
Bidirectional
Reflectance Distribution Function
Neural Networks
|
List of variables/constants
|
Variable/
constant
l, lm(T)
l1, l2
T, TS, TA
TB,
Tn, Tf,
B(l,T)
R(l,T)
c1=2phc2
c2=hc/k
i,
r
Li(T)
S
sat
t
, G, s, V
F,
G, F´
P
p,
ps
q
f
x
fi
e(l), ei
t(l), ti
a(l),ares
ai, ni, Ci
a, b
a, b, X, Y
x,
y, l
M, N, Q
gi
W
ki
a0, a1, a2,
Pn
b
n
|
Unit
mm
mm
K
K
Wm-2mm-1sr-1
Wm-2mm-1sr-1
3.7418×10 –16 Wm2
1.4388×10 –2 mK
index
Wm-2mm-1
Wm-2mm-1sr-1
index (subscript)
index (superscript)
index (subscript)
index
[-]
HPa
deg
deg
deg
[-]
[-]
[-]
g×cm-2
[%]
[-]
K
[-]
Hz
|
Meaning
wavelength, wavelength at peak of radiance (Wien’s
law)
lower and upper limits(wavelength) of a
spectral-range
temperature, surface temperature, mean atmospheric T
brightness temperature, TB in nadir and
forward view (ATSR)
spectral radiance emitted from a black body
spectral radiance emitted from any surface
constant in Planck’s function
constant in Planck’s function
h Planck’s constant (6.626076 x 10 –34Js)
c velocity of light (2.99792458 x
108 ms-1)
k Boltzmann’s constant (1.380658 x
10-23 JK-1)
channel number of a radiometer, reference channel
channel downwelling hemispherical irradiance
spectral radiance measured by a sensor (TOA) in
channel
  channel atmospheric downwelling
irradiance divided by p channel atmospheric upwelling radiance
property of the surface
property at the satellite
top, ground and side of pixel, and vegetation
respectively
form factors (4.1.5)
proportion
pressure, pressure at Earth’s surface
zenith angle
azimuth
scattering angle
normalized response function of the sensor in
channel i
spectral emissivity, channel emissivity
atmospheric spectral transmission, channel
transmission
spectral absorptivity, coefficient in alpha-residual
method
channel-specific constants (TISI method)
SWT coefficients
coefficients in NDVI method
parameters
in local SWT (3.2.3)
Local SWT coefficients
Parameter depending on channel and atmosphere
(3.2.5)
Amount of water vapour in atmosphere
absorption coefficient of the atmosphere in channel
coefficients in multi-angle method
parameter in multi-angle technique (from Planck’s function)
emissivity ratios in TES method (4.1.7)
frequency
|
Acknowledgements
The MODTRAN-3 code was supplied free of charge by the
Air Force Research Laboratory (AFRL), Hanscom AFB, MA. We wish to thank Dr. T.
J. Schmugge at the USDA Hydrology Laboratory, Beltsville, for useful
information and for providing some relevant references. We are thankful to Dr.
F. Nerry at LSIIT, Ilkrich, France, and to Dr. F. Petitcolin, UMD at College
Park, NASA GSFC Maryland, for useful discussions. We also wish to thank the
reviewers for their comments, which enabled us to substantially improve this
manuscript, and Prof. A. P. Cracknell at the University of Dundee, UK, for his
help concerning structural aspects of the manuscript.
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List of table
Table 1: Sensor specific coefficients for
the SWT equation 
(Source:
Czajkowski et al. 1998)
Table 2: Angular
variation of emissivity of some natural substance for the nadir and
forward-mode (0° and 55°) of ERS-ATSR.
(Source:
Sobrino and Cuenca 1999)
|
SENSOR
|
A
|
b
|
|
AVHRR-7
AVHRR-9
AVHRR-11
AVHRR-12
AVHRR-14
|
2.24
2.56
2.40
2.34
2.08
|
2.67
1.95
2.86
7.86
5.54
|
Table 1
Substance
|
Decrease in absolute emissivity (%)
|
|
Water
Sand
Clay
Slime
Gravel
Grass
|
3.3
1.9
0.5
0.9
1.2
0
|
Table 2
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