Evaluation of modeled microwave land surface emissivities with satellite-based estimates
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Evaluation of modeled microwave land surface
emissivities with satellite-based estimates
C. Prigent, P. Liang, Y. Tian, F. Aires, J.-L. Moncet, S. Boukabara
To cite this version:
C. Prigent, P. Liang, Y. Tian, F. Aires, J.-L. Moncet, et al.. Evaluation of modeled microwave land
surface emissivities with satellite-based estimates. Journal of Geophysical Research: Atmospheres,
American Geophysical Union, 2015, 120 (7), pp.2706-2718. �10.1002/2014JD021817�. �hal-02399673�
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Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE Evaluation of modeled microwave land surface emissivities
10.1002/2014JD021817 with satellite-based estimates
Key Points: C. Prigent1, P. Liang2 , Y. Tian3,4 , F. Aires5 , J.-L. Moncet2 , and S. A. Boukabara6
• Modeled land surface microwave
1 CNRS, Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique, Observatoire de Paris, and Estellus, Paris,
emissivity compared to
satellite-based estimate France, 2 Atmospheric and Environmental Research, Inc., Lexington, Massachusetts, USA, 3 NASA Goddard Space Flight
• Over snow-free vegetated areas, the Center, Greenbelt, Maryland, USA, 4 ESSIC, University of Maryland, College Park, Maryland, USA, 5 Estellus, Laboratoire
emissivities agree reasonably well
d’Etude du Rayonnement et de la Matière en Astrophysique, and CNRS, Observatoire de Paris, Paris, France, 6 NESDIS/STAR,
• Further evaluation is provided by
direct comparison with satellite National Oceanic and Atmospheric Administration Joint Center for Satellite Data Assimilation, College Park, Maryland, USA
observations
Abstract An accurate estimate of the microwave surface emissivity is necessary for the retrieval of
Correspondence to:
C. Prigent,
atmospheric quantities from microwave imagers or sounders. The objective of this study is to evaluate the
catherine.prigent@obspm.fr microwave land surface emissivity modeling of the Community Radiative Transfer Model (CRTM), providing
quantitative statistic information for further model improvements. First, the model-simulated emissivity
Citation:
is compared to emissivity estimates derived from satellite observations (TELSEM, Tool to Estimate Land
Prigent, C., P. Liang, Y. Tian, F. Aires, Surface Emissivities at Microwaves). The model simulations agree reasonably well with TELSEM over
J.-L. Moncet, and S. A. Boukabara snow-free vegetated areas, especially at vertical polarization up to 40 GHz. For snow-free surfaces, the
(2015), Evaluation of modeled
microwave land surface emissivities
mean difference between CRTM and TELSEM emissivities at vertical polarization is lower than 0.01 below
with satellite-based estimates, J. Geo- 40 GHz and increases to 0.02 at 89 GHz. At horizontal polarization, it increases with frequency, from 0.01 at
phys. Res. Atmos., 120, 2706–2718, 10.6 GHz to 0.04 at 89 GHz. Over deserts and snow, larger differences are observed, which can be due to
doi:10.1002/2014JD021817.
the lack of quality inputs to the model in these complex environments. A further evaluation is provided by
comparing brightness temperature (Tbs) simulations with AMSR-E observations, where CRTM emissivity and
Received 5 APR 2014 TELSEM emissivity are coupled into a comprehensive radiative transfer model to simulate the brightness
Accepted 2 MAR 2015
Accepted article online 10 MAR 2015
temperatures, respectively. The comparison shows smaller RMS errors with the satellite-derived estimates
Published online 9 APR 2015 than with the model, despite some significant bias at midday with the satellite-derived emissivities at
high frequencies. This study confirms and extends to the global scale previous evaluations of land surface
microwave emissivity model. It emphasizes the needs for better physical modeling in arid regions and over
snow-covered surfaces.
1. Introduction
Surface-sensitive microwave observations from satellite instruments contain key information about precip-
itation, cloud liquid water, and the lower troposphere temperature and water vapor. Accurate estimation of
microwave land surface emissivities is essential to extract such information from the satellite observations.
So far, the assimilation of microwave satellite observations over land in numerical weather prediction
(NWP) scheme is hampered by the lack of reliable emissivity estimates over land. Most of the time, the
surface-sensitive observations are simply disregarded, despite their critical role in characterizing the lower
atmosphere layers.
The physical modeling of the microwave land surface emissivity is particularly challenging. First, it is very
difficult to model the interaction of the electromagnetic waves with the surface components, and different
schemes have to be developed depending on the surface types. Second, the land surface emissivity is
sensitive to a large number of parameters (e.g., soil moisture, topography, presence and physical properties
of vegetation or snow) and shows a large spatial and temporal variability. Lastly, the modeling requires
a large number of ancillary parameters that are not easily available at global scales and whose accuracy
are generally questionable (e.g., soil texture, soil dielectric properties, and snow grain sizes). Nevertheless,
physical surface emissivity models have been developed for assimilation of surface sensible microwave
observations in regional and global NWP systems. Some of them have been integrated to community
radiative transfer models. The Community Radiative Transfer Model (CRTM) is such an example, developed
at NOAA Environmental Satellite and Information Service [Weng et al., 2001]. It runs at NOAA/NASA/DoD
Joint Center for Satellite Data Assimilation.
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2706
Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
In parallel to the modeling activity, land surface emissivities have been derived directly from satellite
observations, with the atmospheric contribution and the modulation by the land surface temperature
removed [e.g., Prigent et al., 1997, 2006; Moncet et al., 2011]. This methodology has been proven to provide
reliable first-order emissivity estimates for practical use in operational algorithms [e.g., Karbou et al., 2014].
The Tool to Estimate Land Surface Emissivities in the Microwaves (TELSEM) [Aires et al., 2011] has been
developed using satellite-derived emissivity, providing estimates of land surface emissivity in the 5 to
200 GHz range, with regard to the scanning geometry and polarizations.
The Global Precipitation Mission (GPM) core satellite, successfully launched on 27 February 2014, carries a
microwave conical scanner between 10 and 190 GHz for the measurements of global precipitation. GPM
will provide enhanced detection and better quantification of light rainfall and snowfall over the previous
Tropical Rainfall Measurement Mission (TRMM), thanks to observations at higher frequencies, up to 190 GHz.
Achieving this objective requires accurate characterization of the surface contribution. For this reason,
significant effort is being devoted to provide state-of-the-art treatment of land surface emissivity in the GPM
algorithm [see Kummerow et al., 2001, http://pps.gsfc.nasa.gov/Documents/GPM2011CombinedL2ATBD.
pdf]. In a first version of the algorithm, the emissivity information will be derived from the TELSEM
satellite-derived estimates. However, in a future step, the objective is to adopt a more physical algorithm
that will include the CRTM radiative transfer emissivity model.
In this framework, a careful evaluation of the emissivity model is necessary. Studies have been done at
local scale [Ferraro et al., 2013; Tian et al., 2013; Ringerud et al., 2013] or at continental scales but for the
lower frequencies only. The objective of this work is to provide a global evaluation of microwave emissivity
modeling over a seasonal cycle (1) to confirm local evaluations that have already been performed,
(2) to identify the surface types that require additional modeling efforts, and (3) to provide guidance to the
emissivity model developments. In the present study, we first evaluate emissivity estimates produced by
the model by directly comparing them with TELSEM global satellite-derived estimates. TELSEM provides
monthly mean climatology estimates, and the emissivities are compared on a monthly mean basis. Then
the emissivities from both sources are fed into a radiative transfer model, and the results are compared
to satellite-measured brightness temperatures (Tbs). For this work, we use measurements from the Earth
Observations Satellite (EOS)-Aqua Advanced Microwave Scanning Radiometer-E (AMSR-E) sensor. Although
AMSR-E only covers the 6–90 GHz frequency range, the two sensors, AMSR-E and the GPM microwave
imagers, are conical scanners with similar scan angles and provide measurements at similar frequencies up
to 90 GHz. At higher GPM frequencies, the atmospheric absorption increases; and as a consequence, the
surface emissivity contribution decreases. Comparisons will be conducted for the frequency range the two
sensors have in common.
2. The Emissivity Databases
2.1. The Modeled Emissivities
The Community Radiative Transfer Model (CRTM V2.0) benefits from up-to-date emissivity models
especially in the microwave, with specific model developments depending on the surface types. The
original Microwave Land Emissivity Model was developed by Weng et al. [2001]. It is a three-layer model
characterizing the emission and scattering processes of various land surfaces such as snow cover, deserts,
and vegetation. The most important parameters affecting the emissivity are the optical parameters (e.g.,
scattering and absorption coefficients) and the interface reflection coefficients. For a medium having a
higher fractional volume of particles such as snow and desert, the scattering and absorption coefficients
are approximated using the dense-medium theory [Tang et al., 1984]. For a snow layer thicker than 1 cm, an
empirical relationship is adopted in the model. The reflection and transmission at the surface-air interface
and lower boundary are derived by modifying the Fresnel equations to account for cross-polarization and
surface roughness effects. The model computes surface emissivities for most surface types at frequencies
for the 5–150 GHz range in both V and H polarizations. This model has demonstrated significant impacts
on assimilations of various satellite microwave data in NWP through CRTM. In this work, the version V2.0 is
used, with the default parameterization. In the following, we will refer to this emissivity model as CRTM.
Since CRTM needs land surface variables, such as soil moisture, soil temperature, and vegetation states,
to output simulated emissivity, it is coupled to NASA Land Information System (LIS) [Kumar et al., 2006]
for these inputs. LIS is a land surface modeling and data assimilation framework, containing several land
surface models (LSMs) and data assimilation schemes. It is driven by the 3-hourly precipitation data from
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2707
Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
NASA Tropical Precipitation Measuring Mission (TRMM) Multisatellite Precipitation Analysis Version 6
(3B42V6) [Huffman et al., 2007]. Other forcing data variables (e.g., wind speed, pressure, and air temperature)
are from NOAA Global Data Assimilation System [Derber et al., 1991]. The version Noah V 3.2 is used. Leaf
area index (LAI) data are from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5
(MCD15A2.005) [Yang et al., 2006], soil texture data are from Reynolds et al. [2000], and land cover types are
based on the 13-type classification scheme of Hansen et al. [2000]. CRTM coupled to LIS is run at 0.25◦ ×0.25◦
resolution at 30 min intervals, with output every 3 h. The simulation period is 5 years (from July 2004
through June 2009), after a 2 year spin-up period for the LSM.
Note that both CRTM V2.0 and V2.1 have actually been tested. The difference in V2.1 is that it implements a
lookup table approach for soil composition based on soil type and LAI based on vegetation type. The rest
of the physics is still largely the same. Since LIS can provide soil composition data and MODIS-based LAI
data directly, there is no need to use the lookup table for the soil and vegetation parameters. Therefore, the
revision in V2.1 does not affect the results, and our tests gave nearly identical simulations.
2.2. The Satellite-Derived Emissivity
Land surface emissivities have been calculated directly from satellite observations, under clear-sky
conditions, by removing the atmospheric contribution and the surface temperature modulation from the
signal, using ancillary information [Prigent et al., 1997, 2006]. This technique has been applied not only to
conical imagers such as the Special Sensor Microwave/Imager (SSM/I) or the TRMM Microwave Instrument
(TMI) but also to cross-track sounders such as the Advanced Microwave Sounding Units (AMSU). From the
analysis of emissivities derived from SSM/I, TMI, and AMSU, a parametrization of the land surface emissivities
between 19 and 100 GHz has been derived, for all incidence angles and both orthogonal polarizations,
anchored to climatological monthly mean maps of the emissivities at 19, 37, and 85 GHz, and calculated
from SSM/I. TELSEM is derived from this parameterization and has been developed for the EUMETSAT
Satellite Application Facility [Aires et al., 2011]. It provides surface microwave emissivity climatologies to be
used as realistic first guess in land surface retrieval schemes and/or atmospheric retrieval algorithms over
land, including variational assimilation.
TELSEM is originally designed for frequencies between 19 and 85 GHz, but tests proved that it is beneficial
down to 5 GHz and up to 190 GHz. The nominal spatial resolution of the emissivity estimates is 0.25◦ × 0.25◦ .
It has been evaluated from 6 to 190 GHz, with observations from AMSU-A and B and AMSR-E [Aires et al.,
2010, 2011; Bernardo et al., 2013]. The use of TELSEM improves the agreement between the simulations and
the observations, with errors not larger than over ocean. The results have been thoroughly evaluated and
the root-mean-square (RMS) errors are usually within 0.02, with the noticeable exception of snow-covered
regions where the high spatial and temporal variability of the emissivity signatures are difficult to capture.
3. Emissivity Comparisons
First, maps of CRTM and TELSEM emissivities are prepared and compared, at AMSR-E frequencies (10.6, 18.7,
23.8, 36.5, and 89 GHz), for both orthogonal polarizations. In this study, averaged monthly emissivities are
compared, not instantaneous emissivities that can vary from the mean. Figure 1 shows the emissivities at
18.7 and 89 GHz, at vertical (V) and horizontal (H) polarizations, for CRTM on the left averaged over January
2008 and for TELSEM on the right, for a climatological January. At 18.7 GHz, a reasonable agreement is
observed at V polarization, except over Greenland. At H polarization, the differences are significant under
snow condition (see the Northern Hemisphere, above 30◦ N) and in desert regions (e.g., the North African
desert). Over vegetated environments (most of South America), the agreement is acceptable. Note that the
10.6 and 18.7 GHz results are very similar. Large hydrological structures associated to low emissivities at all
frequencies with TELSEM are not observed on the CRTM estimates (e.g., the Mississippi, the Amazon, and
the Yangzi Rivers), as LIS does not compute water surfaces. At 89 GHz, the modeled and satellite-derived
emissivities are very different regardless of the environment. Deserts show particularly large differences with
the model emissivities at H polarization. The modeled emissivities are similar over desert and vegetation
in Africa, whereas the satellite-derived emissivities for deserts are much lower than the vegetation ones.
We can even see opposite dependence in vegetation density in Africa, at 89 GHz at H polarization. The low
emissivities simulated over equatorial forests are very surprising, as compared to the high emissivities
observed from satellite. Note also that the modeled emissivities at 89 GHz for snow show very limited
spatial structures, whereas a large variability is observed in the satellite-derived estimates. This is likely
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2708
Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817 Figure 1. (top to bottom) Emissivity at 18.7 GHz V and H polarizations, and 89 GHz V and H polarization for January. (left column) The CRTM model for January 2008. (right column) The TELSEM estimates for a climatological January. PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2709
Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
Figure 2. Emissivity differences between CRTM model and TELSEM estimates at 36.50 GHz. (top row) Vertical polarization and (bottom row) horizontal
polarization. (left column) January and (right column) July.
mostly related to the complexity of modeling the interaction between the snow and the radiation and to
the lack of reliable inputs to the emissivity model. Simulating snow presence and snow cover properties are
still a challenging task for the land surface modeling community [e.g., Wang et al., 2010]. Large differences in
the emissivity are observed at both polarizations, north of 30◦ N, even in regions that are likely not affected
by snow (see, for instance, south of the U.S.). Questions arise related to the soil moisture estimates from LIS
used as input for the model. For both the model and the satellite-derived estimates, the 36.5 GHz emissivity
behavior falls between the 18.7 and the 89 GHz. For summer months (not shown), the general agreement is
better, the problem of snow being avoided. Nevertheless, differences still arise, especially at H polarization
and at high frequencies, and can be quite large over deserts.
TELSEM is representative of a climatology (no year specified), whereas CRTM is run for specific years. We
checked the interannual variability in the CRTM emissivity calculation. There are some differences from year
to year, but these changes are very small compared to the large differences observed between the modeled
and the satellite-derived emissivities, and the interannual variations cannot explain the discrepancies.
Figure 2 shows the differences between the emissivity estimates, at 37 GHz, V and H polarizations, for
January (left column) and July (right column). It confirms that the large differences are concentrated over
snow-covered regions and over deserts.
Figure 3 presents the histograms of the differences between the emissivity estimates, for all frequencies and
at the two polarizations for January (left) and July (right). These histograms exclude snow and ice regions.
Snow-covered pixels are isolated using the AMSR-E/Aqua Monthly L3 Global Snow Water Equivalent. The
large tails on the right of the histograms at H polarization correspond to the arid regions.
The emissivity differences have been examined further, separating the results by surface types (Figure 4).
Table 1 summarizes the results. The MODIS yearly land cover type is adopted (MODIS product MCD12C1).
For snow- and ice-free surfaces, the agreement between CRTM and TELSEM is reasonable, except for deserts
at 89 GHz. For the deserts, the mean differences are of the order of 0.1 at H polarization, for all frequencies,
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2710Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
Figure 3. Histograms of the differences between emissivity simulations with CRTM and TELSEM estimates, for all
frequencies and polarizations (solid lines at vertical polarization and dashed lines at horizontal polarization), for (left)
January and (right) July. These histograms are limited to snow- and ice-free regions.
although with limited dispersion. Over snow and ice regions, differences are also very large, with mean
differences below 0.03 for the two lower frequencies but up to 0.09 at 89 GHz, always with large
standard deviation.
These results confirm the previous studies, with the largest differences being observed over snow cover
regions and deserts [Prigent et al., 2008; Ferraro et al., 2013; Ringerud et al., 2013; Tian et al., 2013]. Note that
these surface types correspond not only to large discrepancies between modeled and satellite-derived
emissivities but also to significant variability among models and among sources of satellite-derived
emissivities [Ferraro et al., 2013].
Figure 4. Histograms of the differences between emissivity simulations with CRTM and TELSEM estimates, at all
frequencies and polarizations (solid lines at vertical polarization and dashed lines at horizontal polarization), for (top
left) evergreen broadleaf forest, (top right) grassland, (bottom left) desert, and (bottom right) snow and ice regions. The
results are presented for the Northern Hemisphere for July (except for snow presented for January).
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2711Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
Table 1. Statistics of the Differences Between the CRTM Emissivities and the Simula-
tions and TELSEM Estimates, for All Frequencies, Averaged Over the 4 Weeks of the
Comparison During 2008, Separately for Four Surface Typesa
Tropical Forest Grassland Desert Snow and Ice
10.6 V 0.001 (0.035) −0.017 (0.031) −0.007 (0.027) 0.014 (0.046)
10.6 H 0.010 (0.061) −0.006 (0.060) 0.087 (0.040) 0.023 (0.082)
18.7 V 0.002 (0.035) −0.013 (0.032) −0.007 (0.027) 0.027 (0.048)
18.7 H 0.016 (0.062) 0.008 (0.059) 0.098 (0.039) 0.038 (0.083)
23.8 V 0.007 (0.033) −0.007 (0.030) 0.000 (0.026) 0.034 (0.045)
23.8 H 0.019 (0.058) 0.014 (0.057) 0.102 (0.037) 0.048 (0.079)
36.5 V 0.016 (0.027) 0.010 (0.026) 0.020 (0.025) 0.058 (0.050)
36.5 H 0.027 (0.048) 0.026 (0.051) 0.109 (0.034) 0.065 (0.075)
89.0 V −0.009 (0.026) 0.019 (0.029) 0.052 (0.025) 0.048 (0.063)
89.0 H −0.001 (0.039) 0.040 (0.047) 0.116 (0.031) 0.091 (0.079)
a Themean difference is indicated, followed by the standard deviation of the differ-
ence, between parenthesis.
4. Evaluation With AMSR-E Observations
At this stage, we evidenced large differences between the modeled and the satellite-derived emissivities
but cannot judge their intrinsic quality. For further evaluation, we suggest to compare forward radiative
transfer calculations using the two emissivity data sets, directly with satellite observations. For this
experiment, observations from AMSR-E on board the EOS-Aqua satellite have been collected for 4 weeks in
2008, 1 week during each season (January, April, July, and October) to cover the full annual cycle. AMSR-E
equator crossing times are 13:30 and 1:30 local time, for the ascending and descending orbits, respectively.
The radiative transfer calculations are performed using a fast model based on the Optimal Spectral Sampling
technique trained on the MonoRTM Line-By-Line Radiative Transfer Model with an accuracy of 0.05 K
[Moncet et al., 2008; Lipton et al., 2009]. MonoRTM is a monochromatic version of LBLRTM [Clough et al.,
2005] better suited for microwave applications that allow exact calculations at one or a number of discrete
frequencies. MonoRTM is used with both TELSEM and CRTM emissivities, for a fair comparison. The
atmospheric profiles are extracted from the 1◦ six-hourly National Centers for Environmental Prediction
(NCEP) GFS analysis and interpolated to the time and location of the Aqua satellite overpass. The surface
temperature (Ts) is obtained from the NCEP model. For cloud screening, we use the cloud liquid water field
from NCEP, although we are aware that this information has limitations [Yoo and Li, 2012]. All pixels with
more than 0.01 kg/m2 of liquid water are filtered out. The radiative transfer calculations are run twice for
each frequency, once using the CRTM emissivities, and a second time with the TELSEM estimates. TELSEM
has been calculated from satellite observations, but it is a general emissivity database for the microwaves,
applicable to all passive microwave sensors, regardless of the year. Here it is applied to AMSR-E in 2008,
whereas it has been calculated essentially from SSM/I, and averaged over 12 years (2008 not included).
The direct contribution from the surface is the product of the emissivity by the surface temperature. The
surface temperature is a very critical parameter in these comparisons between Tb simulations and satellite
observations. First, in this comparison, that is expected to mimic the use of the emissivity in NWP appli-
cation, the surface temperature from the analysis is selected, with the known limitation of this parameter.
Compared to Ts from satellites, Ts from model generally underestimates the amplitude of the diurnal cycle
and, as a consequence, underestimate the Ts at midday, especially for dry environments [e.g., Paul et al.,
2012]. Note that for 2008, a daytime cold bias in the land surface skin temperature over arid or semiarid
regions in warm seasons was evidenced in the NCEP GFS data, related to problems in the thermal roughness
length in the surface layer scheme. Second, the emissivity model as well as the satellite-derived emissivity
assumes that the surface temperature at which the surface emits corresponds to the surface skin temper-
ature. However, we are fully aware that for rather dry surfaces, passive microwaves can be emitted by the
subsurface at physical temperatures that do not correspond to the surface skin temperature [Prigent et al.,
1999; Galantowicz et al., 2011]. The drier the surface and the lower the frequency, the deeper below the
subsurface the microwave emission contribution. Under dry conditions at low frequencies, the emission
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2712Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
Figure 5. Maps of the Tbs for July for the midnight overpassing (descending orbit) (top row) observed by AMSR-E, (middle row) simulated using CRTM emissivity,
and (bottom row) simulated using TELSEM emissivity. (left column) The 18.7 GHz V polarization and (right column) 18.7 GHz H polarization.
Figure 6. Correlations between the observed and simulated Tbs, for all channels for July, with the CRTM and TELSEM
emissivities. Calculations have been performed separately for the midnight and midday overpasses.
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2713Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
LIS-CRTM 2008 - AMSR-E Des July (exclude ice/snow) Telsem - AMSR-E Des July (exclude ice/snow)
30 10.6 30 10.6
18.70 18.70
23.80 23.80
25 36.50 25 36.50
89 89
N of grids x1000
N of grids x1000
20 20
15 15
10 10
5 5
0 0
-20 -10 0 10 20 -20 -10 0 10 20
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
LIS-CRTM 2008 - AMSR-E Asc July (exclude ice/snow) Telsem - AMSR-E Asc July (exclude ice/snow)
30 10.6 30 10.6
18.70 18.70
23.80 23.80
25 36.50 25 36.50
89 89
N of grids x1000
N of grids x1000
20 20
15 15
10 10
5 5
0 0
-20 -10 0 10 20 -20 -10 0 10 20
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
Figure 7. Histograms of the differences between simulations and observations, for July, at all frequencies and polariza-
tions (solid lines for vertical polarization and dashed lines for horizontal polarization), for the (left column) CRTM model
and (right column) TELSEM, for (top row) midnight, and (bottom row) midday. These histograms are limited to snow- and
ice-free regions.
comes from the subsurface and is emitted at a temperature that has a lower diurnal amplitude than the
surface skin temperature and that is time lagged, with a response related to the soil thermal conductivity.
Figure 5 shows the observed averaged clear-sky Tbs (top row), simulated Tbs with emissivity from CRTM
(middle row), and simulated with emissivity from TELSEM (bottom row) at 18.7 GHz, for July at midnight,
at both polarizations. The observed Tbs show rather high values over arid regions at V polarization, as
Table 2. Statistics of the Differences Between the Simulations and the AMSR-E
Observations, Separately for the Midnight and Midday Orbits, for All Frequencies,
Averaged Over the 4 Weeks of the Comparison During 2008a
Midnight Orbit Midday Orbit
CRTM-AMSR TELSEM-AMSR CTRTM-AMSR TELSEM-AMSR
10.6V −1.66 (11.44) 0.419 (5.87) −1.95 (11.43) 0.00 (6.46)
10.6H 7.76 (19.34) 2.99 (7.25) 7.93 (20.77) 3.04 (8.06)
18.7V −0.07 (9.60) 0.99 (4.30) −1.48 (9.94) −0.34 (5.42)
18.7H 8.04 (15.60) 0.56 (6.39) 7.51 (17.41) −0.2 (7.27)
23.8V −0.45 (7.23) −0.70 (3.50) −2.10 (7.48) −2.3 (4.02)
23.8H 5.93 (12.28) −1.15 (5.06) 4.64 (14.15) −2.66 (5.43)
36.5V 1.61 (7.07) −2.17 (3.25) −0.99 (7.98) −4.92 (2.05)
36.5H 8.47 (11.76) −1.73 (5.81) 6.14 (14.46) −4.47 (5.39)
89.0V 1.94 (5.18) −1.65 (2.92) −1.14 (5.95) −4.9 (2.36)
89.0H 5.97 (7.21) −1.95 (4.54) 3.05 (9.41) −5.23 (4.03)
a The mean difference is indicated, followed by the standard deviation of the
difference, between parenthesis.
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2714Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
LIS-CRTM 2008 - AMSR-E Des January Evergrn_brd Telsem - AMSR-E Des January Evergrn_brd
8 18.70 8 18.70
89 89
N of grids x100
N of grids x100
6 6
4 4
2 2
0 0
-20 -10 0 10 20 30 -20 -10 0 10 20 30
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
LIS-CRTM 2008 - AMSR-E Des January Grass Telsem - AMSR-E Des January Grass
8 8
18.70 18.70
89 89
6 6
N of grids x100
N of grids x100
4 4
2 2
0 0
-20 -10 0 10 20 30 -20 -10 0 10 20 30
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
LIS-CRTM 2008 - AMSR-E Des January Barren Telsem - AMSR-E Des January Barren
35 35
30
18.70 30
18.70
89 89
25 25
N of grids x100
N of grids x100
20 20
15 15
10 10
5 5
0 0
-20 -10 0 10 20 30 -20 -10 0 10 20 30
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
LIS-CRTM 2008 - AMSR-E Des January Snow_ice Telsem - AMSR-E Des January Snow_ice
50 50
18.70 18.70
89 89
40 40
N of grids x100
N of grids x100
30 30
20 20
10 10
0 0
-20 -10 0 10 20 30 -20 -10 0 10 20 30
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
Figure 8. Histograms of the differences between simulations and observations, for January, at 18.7 and 89 GHz at both
polarizations (solid lines at vertical polarization and dashed lines at horizontal polarization), using the (left column) CRTM
model and using (right column) TELSEM, for midnight. (top to bottom) Evergreen broadleaf forest, grassland, barren, or
sparsely vegetated regions, snow, and ice.
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2715Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
LIS-CRTM 2008 - AMSR-E Asc January Grass Telsem - AMSR-E Asc January Grass
8 8
18.70 18.70
89 89
6 6
N of grids x100
N of grids x100
4 4
2 2
0 0
-20 -10 0 10 20 30 -20 -10 0 10 20 30
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
LIS-CRTM 2008 - AMSR-E Asc January Barren Telsem - AMSR-E Asc January Barren
35 35
30
18.70 30
18.70
89 89
25 25
N of grids x100
N of grids x100
20 20
15 15
10 10
5 5
0 0
-20 -10 0 10 20 30 -20 -10 0 10 20 30
AMSR-E Tb Difference [K] AMSR-E Tb Difference [K]
Figure 9. Histograms of the differences between simulations and observations, for January, at 18.7 and 89 GHz at both
polarizations (solid lines at vertical polarization and dashed lines at horizontal polarization), using the (left column) CRTM
model and using (right column) TELSEM, for midday, for (top row) grassland, and for (bottom row) barren or sparsely
vegetated regions (bottom).
compared to the H polarization. A positive gradient is observed in Africa from the equator to the north, i.e.,
from tropical forest to desert, at V polarization. The opposite gradient is observed at H polarization. The
snow-covered areas north of Canada and in Greenland are associated with low Tbs at both polarizations.
Significant differences between the CRTM-simulated and measured Tbs can be noted. For instance, the
CRTM simulations show smaller gradients at H polarized Tbs in the transition from forest to desert over
Africa and lower Tbs over Greenland. The simulations using TELSEM appear very consistent with the
observations at this frequency. Even subtle structures in the measurements such as the low Tbs associated
to the presence of carbonates in Algeria or in Oman [Prigent et al., 2005] are well reproduced by TELSEM.
One can also see that the Amazon River is present in the observations and is well reproduced by TELSEM
but not by the model. At midday for the same month at this frequency, similar agreements/disagreements
are obtained (not shown). In January for snow-covered regions, simulations with CRTM and with TELSEM
show large differences as compared to the observations, the simulations with TELSEM being closer to
the observations.
Correlations between the observed and simulated Tbs have been calculated, for all channels, with the
CRTM and TELSEM emissivities. Figure 6 presents the results for July. The correlations are always very high
with TELSEM, at both polarizations, although slightly lower at 89 GHz. They are significantly lower with the
modeled emissivities, especially at H polarization. Figure 7 shows the histograms of the Tb differences
between the simulations and observations, at all frequencies and polarizations over snow-free areas in
July for the midday and midnight orbits, separately. Table 2 summarizes the statistics. At midnight, less
uncertainties are expected from the Ts estimates in the radiative transfer calculation. The surface skin
temperature undergoes less variations than at midday, and the differences between the temperature of
the surface and the subsurface is more limited. For most channels, lower biases are observed during the
night between simulations with TELSEM and AMSR-E observations. The standard deviation is significantly
lower for the calculations with TELSEM than with CRTM, although less bias is observed with CRTM at midday,
especially for the higher frequencies. In the framework of the assimilation, biases can be subtracted and
PRIGENT ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2716Journal of Geophysical Research: Atmospheres 10.1002/2014JD021817
are less problematic. However, it is important to have low standard deviations, i.e., to be able to reproduce
the correct temporal variability. Figure 8 shows the histograms of the differences per surface type (tropical
forest, grassland, desert, and snow) at 18.7 and 89 GHz, V and H, in January for the Northern Hemisphere at
midnight, separately for the calculations with CRTM and TELSEM. With the satellite-derived emissivities, at
a given frequency, the results are rather similar at the two polarizations. For the modeled emissivities, very
different behaviors are observed, at the two polarizations, pointing at difficulties to handle the polarization,
especially for the arid regions. The histograms have also been examined for midday overpasses (Figure 9
shows the results for grassland and deserts, the other histograms being similar to the midnight overpasses).
The biases are significantly larger than at midnight with TELSEM. The emissivities, from the model and
derived from satellites, are effective emissivities that are assumed to emit at the surface skin temperature.
Over desert, penetration of the microwave radiation is expected, the lower the frequency, the larger the
penetration. At midday in these regions, the surface skin temperature is larger than the temperature of the
surface below. As a consequence, differences with the AMSR-E observations were expected to be larger at
low frequencies (the measured Tbs being lower than the simulated ones) than at high frequencies, where
the emission temperature of the surface should be closer to the skin temperature. Our interpretation is that
the NCEP Ts over arid regions is likely underestimated, making the simulated Tbs too low at that time of the
day with TELSEM, whereas providing the right simulated Tbs at low frequencies, where more discrepancies
were expected.
5. Conclusion
The microwave land surface emissivity model from CRTM (V2.0) has been compared to the emissivity
estimates derived from satellite observations (TELSEM), on a monthly mean basis. For snow-free surfaces,
the mean difference between CRTM and TELSEM emissivities at the vertical polarization is lower than 0.01
below 35 GHz and increases to 0.02 at 89 GHz. At horizontal polarization, it increases with frequency, from
0.01 at 10.6 GHz to 0.04 at 89 GHz. Over deserts and snow, larger differences are observed. They can be
partly due to the lack of quality inputs to the model, in these very complex environments.
A further evaluation of the emissivities is provided by direct comparison with AMSR-E observations, with
radiative transfer simulations using the CRTM and TELSEM emissivities. The comparisons show smaller RMS
errors with the satellite-derived estimates than with the model, despite some significant bias at midday
with the satellite-derived emissivities at high frequencies. This points to the consistency of the surface
temperature and emissivity estimates, for a correct account of the surface contribution in the radiative
transfer simulations.
This study confirms and extends to the global scale previous evaluations of land surface microwave
emissivity models at local scales. The comparisons are performed at monthly time scales. The significant
differences found here stress the interest of the analysis even at these broad scales, to provide first-order
guidance for the emissivity modeling. The potential ability of the model to account for small-scale temporal
changes in the emissivity is not analyzed here. This study emphasizes the need for a better account of
the emissivity properties in models under arid environments. A strong collaboration between the model
developers and the satellite remote sensing community should help obtain a better agreement between
Acknowledgments simulations and observations. Land surface emissivity is still a very challenging area of model develop-
This study has been supported by a ments. The inputs to the emissivity model could also be questioned, especially over snow. Limitations in the
NASA/NOAA contract NNH12CD07C
availability and accuracy of the input parameters are key issues where improvements in the near future are
“Development of a common,
consistent infrared and microwave still needed.
emissivity database for use as a priori
in the JCSDA.” We are very grateful Assimilation of microwave imagers (such as GPM) will ideally require an accurate emissivity model over land
to Fuzong Weng for interesting surfaces as well as over oceans. Significant advances have been made lately in this direction, and the models
discussions and suggestions. We are performing well under a wide range of environments. The satellite-derived emissivity can help, both as a
thank three anonymous reviewers
for their careful reading of the paper transitory solution and as a reference for model developments.
and their constructive comments.
The modeled emissivity data set is
available at http://lis.gsfc.nasa.gov/
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