Atmos. Meas. Tech., 8, 1197–1205, 2015
www.atmos-meas-tech.net/8/1197/2015/
doi:10.5194/amt-8-1197-2015© Author(s) 2015. CC Attribution 3.0 License.

Assessment of the consistency among global microwave land surface
emissivity products
H. Norouzi1 , M. Temimi2,3 , C. Prigent4 , J. Turk5 , R. Khanbilvardi2 , Y. Tian6,7 , F. A. Furuzawa8 , and H. Masunaga8
1 Department of Construction Management and Civil Engineering Technology, New York City College of Technology,

Brooklyn, New York, USA
2 Civil Engineering Department, NOAA-CREST, the City College of New York, New York, New York, USA
3 Masdar Institute of Science and Technology, Institute Center for Water and Environment (iWater), Abu Dhabi,
United Arab Emirates
4 Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères, Centre National de la Recherché
Scientifique, Paris, France
5 Jet Propulsion Laboratory, Pasadena, California, USA
6 Univ. Maryland College Park, College Park, Maryland, USA
7 NASA’s Goddard Space Flight Center, Greenbelt, Maryland, USA
8 Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan
Correspondence to: H. Norouzi (hnorouzi@citytech.cuny.edu)
Received: 26 August 2014 – Published in Atmos. Meas. Tech. Discuss.: 26 September 2014
Revised: 2 February 2015 – Accepted: 17 February 2015 – Published: 11 March 2015

Abstract. The goal of this work is to intercompare fourglobal land surface emissivity products over various landcover conditions to assess their consistency. The intercompared land emissivity products were generated over a 5year period (2003–2007) using observations from the Advanced Microwave Scanning Radiometer – Earth Observing
System (AMSR-E), the Special Sensor Microwave Imager(SSM/I), the Tropical Rainfall Measuring Mission (TRMM)
Microwave Imager (TMI), and WindSat. First, all products
were reprocessed in the same projection and spatial resolution as they were generated from sensors with various
configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for eachproduct to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe.
The emissivity products were also compared to soil moisture estimates and a satellite-based vegetation index to assess
their sensitivities to changes in land surface conditions.
Results show the existence of systematic differencesamong the products. Also, it was noticed that emissivity values in each product have similar frequency dependency overdifferent land-cover types. Monthly means of emissivity values from AMSR-E in the vertical and horizontal polariza-

tions seem to be systematically lower than the rest of the
products across various land-cover conditions which may be
attributed to the 01:30/13:30 LT overpass time of the sensor
and possibly a residual skin temperature effect in the product. The standard deviation of the analyzed products waslowest (less than 0.01) in rain forest regions for all products
and highest at northern latitudes, above 0.04 for AMSR-E
and SSM/I and around 0.03 for WindSat. Despite differencesin absolute emissivity estimates, all products were similarly
sensitive to changes in soil moisture and vegetation. The
correlation between the emissivity polarization differences
and normalized difference vegetation index (NDVI) values
showed similar spatial distribution across the products, withvalues close to the unit except over densely vegetated anddesert areas.

1

Introduction

In numerical weather prediction (NWP) models, instantaneous microwave land surface emissivity is an importantboundary condition that needs to be determined accurately
in order to retrieve reliable atmospheric profiles. It was sug-

Published by Copernicus Publications on behalf of the European Geosciences Union.

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H. Norouzi et al.: Assessment of the consistency of emissivity products

gested that a 1 % accuracy level in emissivity retrievals is required in applications such as NWP and microwave satellitebased precipitation algorithms (Karbou et al., 2006) to ensure
the development of reliable weather products. Other applications of emissivity values include determination of changes
in land surface condition as well as understanding the variability of land emissivity, which implies relying on estimatesfrom different sensors and therefore the importance of investigating the consistency among the available products.
A number of microwave land emissivity products that areassociated with different sensors have been proposed in theliterature. The sensors used to infer land emissivity havecommon and, in certain cases, unique frequency channels.
Relying on a single sensor’s estimates reduces the potential of the available retrievals from the other sensors. It istherefore important to integrate all retrievals to maximize thespectral range of land emissivity products for an effectiveuse, among others, in land surface classification. However,this requires understanding the consistency among the existing products, which is the first necessary step towards integrating land surface emissivity values from different sensors.
The sources of discrepancies among the existing landemissivity products are various, and they mainly fall into oneof the two following categories. The first category includes
the sensor’s parameters. As microwave land surface emissivity values are impacted by several surface and subsurface
parameters – like soil moisture, vegetation structure and density, freeze and thaw states, soil texture, and topography – achange in one or a number of these land parameters should
impact the determined land emissivity differently dependingon the configuration of the sensor, e.g., frequency, polarization, overpass time, incident angle, and footprint. In addition,even if two sensors concur in terms of frequency and observation geometry, a difference in their calibration process may
introduce a gap between their readings. Polar orbiting satellites, which observe the earth at least twice a day, have different acquisition times that make their corresponding brightness temperature vary.
The second category of factors which may affect theconsistency among the land emissivity products is relative
to the retrieval method and the ancillary data used, which
may introduce an inherent difference in the emissivity estimates. Physical models and retrieval techniques have commonly been utilized to estimate land surface emissivity with
their own benefits and pitfalls. The retrieval of land emissivity involves the use of land surface temperature, which
could be obtained from another sensor, like the Moderateresolution Imaging Spectroradiometer (MODIS) or from reanalysis like the National Centers for Environmental Prediction (NCEP) outputs. Theoretically, in emissivity retrievals
the effect of temperature is removed and one should expect
similar surface conditions from different sensors regardlessof their acquisition time, especially when they are aggregated on a monthly scale. Emissivity is calculated using aradiative transfer model traditionally for cloud-free scenes
Atmos. Meas. Tech., 8, 1197–1205, 2015

since clouds greatly affect the signal. However, even overcloud-free pixels, accounting for the atmospheric contribution was necessary, especially for frequencies higher than
19 GHz. Many studies have attempted to estimate emissivity using forward modeling (Ringerud et al., 2014; Boukabara et al., 2011; Weng et al., 2001). These models often use
emissivity retrieval from satellite observations as a referencein their algorithms. Physical radiative transfer models benefit from including all controlling parameters in algorithms.
However, such a comprehensive approach requires several
inputs, such as soil type, moisture, and temperature, which
are difficult to obtain on large or global scales (e.g., Wenget al., 2001; Ringerud et al., 2014). Global land emissivity
retrieval first was developed by Prigent et al. (1998) whenbrightness temperatures from the Special Sensor Microwave
Imager (SSM/I) were used. Other available products later
were proposed from other sensors, such as the Advanced
Microwave Scanning Radiometer – Earth Observing System (AMSR-E) (Norouzi et al., 2011; Moncet et al., 2011),
the Advanced Microwave Sounding Unit (AMSU) (Karbou
et al., 2005), and the Tropical Rainfall Measuring Mission(TRMM) Microwave Imager (TMI) (Furuzawa et al., 2012).
To retrieve land emissivity values, those studies did not necessarily use the same ancillary data, radiative transfer model,
and assumptions to account for the atmospheric contribution.
Also, microwave brightness temperatures from a variety of
sensors with varying configurations (e.g., observation geometry, frequency, resolution) were used to generate the global
land emissivity maps. Therefore, discrepancies among theavailable land global emissivity maps are expected.
Emissivity estimates from different products were first intercompared as part of a joint effort by members of the Land
Surface Working Group (LSWG) of the Global Precipitation Measurement (GPM) mission, with the goal of improving retrievals from the recently launched GPM satellite (Ferraro et al., 2013). The emissivity estimates were compared atthree points that coincide with previous and ongoing in situ
measurements for Soil Moisture Active Passive (SMAP) andGPM missions. The results showed noticeable differencesamong estimates, with similar seasonal trends and variability.
In another study (Tian et al., 2014), the emissivity estimates from various sensors and providers at four locations
with different land-cover types: two desert and two rain forest locations were evaluated, and large discrepancies were
found across the sensors with different spectral signatures.
The study of Tian et al. (2014) accounted for random and systematic errors using statistical approaches and suggested thatthe differences among retrievals are caused likely by cloudor rain contaminations.
The goal of this study is to expand the point-based intercomparisons to a global scale and to investigate the relative
consistency among different land surface emissivity products. The lack of ground truth measurements on a global
scale made the validation and the benchmarking of eachwww.atmos-meas-tech.net/8/1197/2015/

H. Norouzi et al.: Assessment of the consistency of emissivity products
land emissivity product difficult. In this study, we propose
to overcome this lack of ground truth data by investigating
the consistency among the available global land emissivity
estimates from different sensors. The focus is on emissivity
retrievals from microwave sensors with a constant incidence
angle over a 5-year period. We assume that the consistency
among the existent land emissivity products is an indicatorof the reliability of the retrievals. The analysis of the consistency among the products was conducted over different land
classes. It quantitatively compares available estimates fromdifferent sensors. It is, to our knowledge, the first attempt toassess the consistency among land emissivity products overdifferent land-cover types on a global scale. It also aims to
examine the dynamics of the products in monthly timescale
and find their relationships with surface properties such as
soil moisture and vegetation change both spatially and temporally.

2

Data sets

Five years (from January 2003 to December 2007) of landemissivity data from different providers were collected. The
sensors included in this study are AMSR-E, SSM/I, TMI, andWindSat. The SSM/I-based emissivity product is generated
by Centre National de la Recherche Scientifique (CNRS) in
France (Prigent et al., 1998, 2006). This data set has thelongest available record of emissivity estimates for frequencies of 19 to 85 GHz. The data set uses International Satellite
Cloud Climatology Project (ISCCP) skin temperature and the
NCEP reanalysis for air temperature and water vapor column.
The AMSR-E instantaneous emissivity has been produced by the National Oceanic Atmospheric Administration (NOAA) Cooperative Remote Sensing and Technology(CREST) center for more than 6 years and is available ona monthly scale on the National Snow and Ice Data Center(NSIDC) website (Norouzi, 2013). This retrieval uses ancillary data from ISCCP and the TIROS Operational Vertical
Sounder (TOVS) for skin temperature, cloud mask, and atmospheric information (Rossow and Schiffer, 1999).
The emissivity product based on TMI observations is provided by Nagoya University in monthly format (Furuzawa
et al., 2012). This product uses Japanese 25-year ReAnalysis (JRA-25) as ancillary data (Onogi et al., 2007). It finds
required parameters from JRA-25 by an interpolation technique based on TMI acquisition time for each pixel.
WindSat emissivity estimates are derived using ancillarydata from Atmospheric Infrared Sounder (AIRS) and NCEP
data. Further details about the retrieval algorithms and the
product can be obtained from Turk et al. (2014).
All sensors, except TMI, are sun-synchronous and have ascending and descending overpasses. They are all microwave
imagers (not sounders) and have a few years of overlap intheir life span. There are some differences in frequencies, inwww.atmos-meas-tech.net/8/1197/2015/

1199

cidence angle, acquisition time, footprint, and calibration ofthese microwave sensors. Unlike other polar orbiting sensors
that are considered in this study, the geographic coverageof TMI does not include areas above and below 38◦ N and
38◦ S latitude, respectively. The details of these differences
are listed in Table 1.
A vegetation and land use global data set compiled from alarge number of published sources at 1◦ equal area grid resolution by Matthews (1983), adopted by Prigent et al. (2001)
in 0.25◦ , is used in this study to distinguish among various surface types. The land classes include rain forest, evergreen forest, deciduous forest, evergreen woodland, deciduous woodland, cultivation, grassland, tundra, shrubland, anddesert.

3

MethodResampling of data products: first, it was necessary to reprocess the selected land emissivity products by reprojecting them in a common equal-area grid (0.25◦ at the Equator)
projection and resampling them to the same spatial resolution. This step is required to make the intercomparison possible despite the systematic differences that it may introduce.
There were no further adjustments done, in terms of interfrequency or interangle interpolations, to account for the differences in the sensors’ configurations and observation geometries. The intercomparison was performed on a global scale
except in the case of TMI, where the spatial coverage of thesensor was limited to the ±38◦ latitude region.
The mean values of monthly emissivity products fromeach sensor were calculated for the period of 2003 to 2007 todetermine the relative differences among the monthly variation of emissivity products.
Moreover, the standard deviation of monthly estimatesfrom each product for each pixel is calculated as indicatorof dynamics of emissivity using 5 years of monthly data.
Emissivity microwave polarization difference index(MPDI): the intercomparison of different land emissivity
products included analysis of their sensitivity to the different
land surface parameters. To this end, a polarization index,
the emissivity microwave polarization difference index(MPDI), was calculated. The MPDI should exhibit greater
sensitivity to surface parameters and should mitigate theeffect of the atmosphere and land surface temperatureand therefore reduce their impact on the reliability of the
products’ intercomparison. In addition, it has been shownthat differences between horizontal and vertical polarization
signals contain a wealth of information regarding soil
moisture and vegetation density (Felde, 1998). Amongthe indices that accounted for the polarization difference,
the microwave polarization difference index for brightness
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H. Norouzi et al.: Assessment of the consistency of emissivity products

temperature was largely used. The emissivity-based MPDI
is defined as

MPDI =

εv − εh,εv + εh(1)

where εv and εh are emissivities at vertical and horizontal polarization, respectively, for a specific frequency. This index is
calculated for each pixel and then is evaluated with satellitebased soil moisture and the normalized difference vegetationindex (NDVI).
4

Results and discussion

Monthly mean values were calculated for all products fromJanuary 2003 to December 2007. The obtained results wereaveraged over the different land-cover types, namely, rain
forest, evergreen woodland, grassland, and deserts. Figure 1 reveals clear differences among the emissivity products
which are sensitive to frequencies and land-cover conditions.
Over densely vegetated areas such as rain forests the discrepancies among the products are larger at high frequencies toreach, for instance, 0.06 between AMSR-E 89 GHz and TMI
85 GHz horizontal emissivity values. The products show better agreement at lower frequencies which involve less scattering and deeper penetration into the canopy unlike highfrequency brightness temperature which penetrates less andreflects the top of the canopy microwave radiating temperature. The use of the canopy skin temperature, in the rain forest region, to approximate the canopy effective temperature
for low and high frequencies in the emissivity retrieval canlead to discrepancies among the products that are frequency
dependent (Norouzi et al., 2011; Prigent et al., 1999). The
effective optical depth of the canopy depends on the vegetation water content, intercepted water in rain forests, and thevegetation structure and type. Moreover, the differences canbe attributed to the divergences among the products when
accounting for the atmospheric perturbations which are considerable in the high range of frequencies due to the higher
water vapor effect in tropical and rain forest regions.
In desert, unlike rain forest regions, maximum differences
are at lower frequencies and agreements relatively improveat higher frequencies, particularly in the horizontal polarization values. The deeper penetration of the microwave signal
especially at low frequencies in desert which leads to differences in the diurnal amplitude and phase of skin temperatureand microwave brightness temperature can introduce considerable error in emissivity retrievals (Norouzi et al., 2012).
This issue is more highlighted in desert areas due to moisturescarcity and minimal vegetation interferences. These results
are consistent with a previous study by Tian et al. (2014) interms of systematic differences at various frequencies. Also,a wider gap can be noticed between the average monthly
emissivity values in the horizontal and vertical polarizations
Atmos. Meas. Tech., 8, 1197–1205, 2015

over desert. The horizontal polarization increase with increasing frequencies while the vertical polarization declines
with the frequency increase (Yubao et al., 2014). This behavior was consistent among all investigated products.
According to Fig. 1, AMSR-E has the highest variationof emissivity spectrally. AMSR-E has lower emissivities for
10, 19, and 37 GHz, and higher at 89 GHz. At 89 GHz, themicrowave signal is more affected by the atmosphere andthe impact of the differences in ancillary data and radiativetransfer modeling can be critical. It was noticed that bothhorizontal and vertical polarizations reflect the same variability in terms of differences with other sensors (solid anddashed lines). However, the differences between horizontal
and vertical polarization emissivity values increase as vegetation density decreases from rain forests to desert land cover.
Previous studies have shown that differences in channel frequencies and incidence angles between AMSR-E and
SSMI/I channels may lead to around 0.01 error in the emissivity retrieval (Norouzi et al., 2011). TMI and SSM/I have
similar emissivity values especially at 10, 19, and 37 GHz.
The discrepancies are noticeable in 89 GHz. SSM/I and TMIemissivities are more stable with varying frequency. The
emissivities from AMSR-E and WindSat are less consistentthan other products almost at all frequencies and land-covertypes. The results for other land-cover types are the same as
the presented ones.
The seasonality of the different land emissivity products
was analyzed. Standard deviations of monthly land emissivity estimates for 5 years of data at different frequencies were
calculated (Fig. 2). Higher variation is observed across all
products (higher standard deviation) in monthly estimatesover areas where surface properties such as moisture andvegetation change more significantly over the seasons due tothe presence/melting of snow during winter/summer, vegetation growth, and seasonal precipitation. Figure 2 depicts thecalculated standard deviations of monthly emissivity means
at 37 GHz (horizontal polarization) for all sensors. SSM/I
and AMSR-E emissivity values show high standard deviations greater than 4 % (dark red) in high-latitude and boreal
regions, which do not seem to be present in the WindSat values in the horizontal polarization. The highest standard variation for WindSat at northern latitudes was less than 0.03.
Das et al. (2014) reported a higher disagreement between
AMSR-E and WindSat brightness temperatures in Dom-C
area in Antarctica in horizontal polarization. The relatively
high standard deviation values across all sensors could be
explained by the transition between freeze and thaw conditions throughout the seasons. However, one should expect
consistency between AMSR-E and WindSat because of theirsimilar configurations. In line with what was stated in Das etal. (2014), the difference in incident angles (49.9◦ for WindSat and 55◦ for AMSR-E) seems to have a considerable impact on the northern latitude retrieval, affecting more the horizontal polarization observation than the vertical polarization
ones. Moreover, the snow-covered regions are also flagged
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H. Norouzi et al.: Assessment of the consistency of emissivity products

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Table 1. List of global land surface emissivity products used in this study.
Sensor

ProviderAMSR-ESSM/I
TMIWindSat

NOAA-CREST
CNRS-France
Nagoya Uni.
JPL/NRL

Frequencies

Incidence angle

Ancillary data

6.9, 10.65, 18.7, 23.8, 36.5, and 89.0
19.35, 22.235(v), 37.0, and 85.5
10.65, 19.35, 21.3(v), 37.0, and 85.5
6.8, 10.7, 18.7, 23.8, and 37.0

550
530
53.40
49.90 to 55.30

ISCCP-DX, TOVSISCCP-DX, NCEP Reanalysis
JRA-25
NCEP Reanalysis, AIRS

Figure 1. Mean of monthly emissivity values (from 2003 to 2007) for rain forest, evergreen woodland, grassland, and desert regions on
a global scale from AMSR-E, TMI, SSM/I, and WindSat. The solid lines present vertical polarization, and dashed lines are for horizontal
polarization.

in the retrieval of WindSat emissivities which caused lower
variability in the Northern Hemisphere (Turk et al., 2014).
Other land-cover signatures are also seen in monthly standard deviations. For instance, WindSat clearly shows a low
emissivity standard deviation (less than 0.005 %) over the
Amazon and Congo with persistent and steady dense vegetation. The Sahara is clearly distinguishable from SSM/I
emissivity values when low emissivity values contrast withthe transition region (south of the Sahara) with higher emissivity variation because of seasonal variation of moisture andvegetation cover. Surface properties in terms of soil moisture
do not change in the Sahara with almost no vegetation cover.
This can explain the low emissivity change and standard deviation. AMSR-E and TMI also show the same pattern, but
it is less recognizable in the WindSat map. There are small
regions that show a very high standard deviation in SouthAmerica, corresponding to floodplains that are seasonally inundated, which explains the high standard deviation values
in all products except WindSat. Moreover, the standard deviation of the TMI emissivity values in the Amazon seems
to be higher (around 0.01–0.015) than the values obtained

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with the other sensors (around 0.005–0.01). In WindSat, owing to the simplified parameterizations of the vegetation in
the retrieval, the variability of emissivity is not very high intransition areas (Turk et al., 2014). Similar results of standard
variation analysis also were found in other channels that are
not presented here. Overall, despite the relative differences instandard deviation values, the dynamics of emissivity products tend to be related to known changes in surface condition
across the globe.
The relationship between the investigated products andtwo key surface parameters, namely, soil moisture and vegetation cover, was assessed. The emissivity MPDI values areplotted against soil moisture content and NDVI values over
the TMI coverage region for all products (Fig. 3). Soil moisture estimates are microwave-based from WindSat C-band
observations because of its availability over the time periodof this study (Turk et al., 2014). NDVI estimates are from
MODIS and are available every 16 days globally. Monthly
averages of NDVI and soil moisture estimates are calculated
in this study. Emissivity-based MPDI values for the range ofsoil moisture and NDVI values at 19 GHz are shown in Fig. 3

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H. Norouzi et al.: Assessment of the consistency of emissivity products

Figure 3. Emissivity MPDI values from various sensors/providers
at different NDVI and soil moisture ranges at 19 GHz for July 2003.

Figure 2. Monthly standard deviation of emissivity estimates from
AMSR-E, SSM/I, WindSat, and TMI from 2003 to 2007 at 37 GHz(horizontal polarization).

for all products for July 2003. High emissivity-based MPDI
values greater than 0.06 are found for low soil moisture andlow NDVI values in all products. This is in line with previous studies that suggest that the contrast between horizontal and vertical microwave signals is higher in desert regionswith almost no vegetation (e.g., Norouzi et al., 2011; Prigent
et al., 2006). Soil moisture has been found to decrease theemissivity across the frequencies (Basist et al., 1998). Lower
emissivity-based MPDI values are seen as vegetation density
and soil moisture increase in all products. Higher vegetation
Atmos. Meas. Tech., 8, 1197–1205, 2015

causes more scattering of microwave signal, and therefore
the difference between horizontal and vertical polarizations
decreases. The pattern is very similar for all products except WindSat, which shows lower values (about 0.05) in the
low soil moisture range. This could be because the soil moisture data are based on WindSat observations. Consequently,
one can conclude that emissivity retrievals are consistent interms of their relationship between emissivity-based MPDI
and surface condition.
The comparison of emissivity and NDVI as indicators ofvegetation density and surface condition revealed that the differences between horizontal and vertical emissivity values(eH − eV ) have positive correlation with vegetation and soil
moisture values (Norouzi et al., 2011; Prigent et al., 2006)
using AMSR-E and SSM/I values. Monthly temporal correlation between eH − eV and NDVI values were calculated
for 5 years globally for each pixel. The calculated correlation values for all products at 19 GHz are shown in Fig. 4.
At first glance, all products present a high correlation (morethan 0.9) with monthly NDVI variations in most regions. Thisshows that emissivity estimates in these regions are in phasewith what is expected from the surface in terms of vegetation. However, desert regions in the Sahara, Australia, andthe Middle East as well as regions with very high vegetation
density show a much lower correlation (around −0.2 to 0).
This is because in desert regions there is almost no vegetationand the surface vegetation and soil moisture do not change.
Besides, in highly vegetated areas such as the Amazon andCongo, the vegetation density remains high throughout theyear. Therefore, the NDVI and eH −eV variation comparisons
are not representative of the surface condition variation inhighly vegetated and desert areas. The observed variability
could be because of noises or atmospheric residuals in theemissivity retrievals. WindSat has a different spatial pattern,
especially in the semi-arid region that marks the transitionzone between desert and rain forest regions in Africa.
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H. Norouzi et al.: Assessment of the consistency of emissivity products
One key factor in emissivity retrievals is the cloud mask information that is utilized to mask out the cloudy scenes and toensure that the retrieval of emissivity is only performed overcloud-free pixels. The investigated data products do not necessarily use the same cloud mask. The inconsistency among
the detected cloudy pixels in the analyzed products could be
an additional source of discrepancy, which can also explain
the differences in the mean monthly maps. The differences
between emissivities could be up to 10 % at higher frequencies with false detection of cloudy scenes.
NWP models rely on radiative transfer (RT) models (e.g.,
Community Radiative Transfer Model (CRTM)) that areused to determine the state of the atmosphere and accountfor the radiative transfer in different spectral ranges, suchas, among others, the microwave frequencies (window andsounding channels). Estimates of land surface emissivity areparticularly important as NWP models attempt to assimilate passive microwave observations over land (Prigent etal., 2006). Specifically, when it comes to window channels,
the surface radiance that is controlled by the land emissivity
should be determined. The simulations using the RT models
should be carried across a wide range of angles and frequencies. Sensors can only provide estimates at specific angles
and frequencies.
The results of this study can serve the development of a
global blended land emissivity product that accounts for theidentified spatial inconsistencies among the different existing land emissivity products. A blended product may balance the errors among the distinct products as such product could be obtained using a weighted regression among all
emissivity retrievals where weights should vary spatially to
account for the spatial variability of the consistency among
the products (Sahoo et al., 2011). Given the instantaneous
scale of NWP models, the assimilation of such blended product may not be possible. However, such a product could be
useful if it considered with an error estimate that accounts
for the instantaneous bias. Using forward operators in assimilation models, some instantaneous biases may be removed
by additional observations such as infrared sounders and radiosondes measurements. This should lead to weather forecasts that are more accurate than obtained when any individual land emissivity product is used. The emissivity blended
composite could also satisfy the needs of many precipitation
retrieval algorithms (Skofronick-Jackson et al., 2013). Moreover, land emissivity is sensitive to soil wetness, which canaffect brightness temperatures when they are used in NWP
models. The perturbations in emissivity due to the soil moisture effect may propagate significant error in atmospheric information estimates from when they are not accounted for.
This could be particularly important since the sensors havedifferent acquisition times which may reflect some variability
in their estimates of soil moisture (especially AMSR-E with
01:30/13:30 crossing times) (Jackson et al., 1997). This variability may have imposed some discrepancies among sensors, although they are aggregated to a monthly scale. A crosswww.atmos-meas-tech.net/8/1197/2015/

1203

Figure 4. Correlation maps between temporal variations of eH −eVfrom all sensors at 19 GHz with monthly NDVI values from 2003
to 2007.

calibration that involves data from all the sensors used inthis study is necessary to detect the magnitude of the discrepancies in the raw data and the determined brightness
temperatures and to apply appropriate corrections to mitigate its impact on land emissivity retrievals. The efforts partof the Global Precipitation Measurement mission showedsmall differences between similar channels (but not identiAtmos. Meas. Tech., 8, 1197–1205, 2015

1204

H. Norouzi et al.: Assessment of the consistency of emissivity productscal) of conical orbiting radiometers on two pairs of sensors,
TMI/WindSat and TMI/AMSR-E. However, in some otherchannels, such as 37V, the differences between TMI andWindSat could reach 3 K (Wilheit, 2013), which can havea non-negligible influence on emissivity estimates. This difference causes about 0.01 difference in emissivity estimates,
which is significant especially in regions where emissivity
values are higher than 0.95 (Norouzi et al., 2012).

5

Conclusions

The global emissivity retrieval products from various passive microwave sensors over land were intercompared ona monthly scale for a 5-year period of time. The sensors
have a general configuration difference that can induce somesystematic differences among them. Previous studies haveshown that differences due to channel frequency and incidence angle are not significant especially at lower frequencies. Systematic differences among retrievals could be due
to ancillary data and the radiative transfer models used. All
products use a relatively similar retrieval algorithm to estimate emissivity on top of the canopy or the land surface.
However, the differences in ancillary data and the ways they
may be interpolated or resampled prior to their use in the
retrieval may affect the obtained emissivity values. In addition, the differences among the methods to account for atmospheric contributions are among the sources of differences.
Emissivity values are the signals from microwave observation after removing the effect of temperature and atmosphere from the brightness temperature. The differences atlower frequencies were found to be higher in desert regions
because of penetration depth and discrepancies between skintemperature and microwave brightness temperature originating depths. At higher frequencies (more than 37 GHz) due
to atmospheric residuals in emissivity values, the inconsistencies increase in regions with high vegetation density andwater vapor amount. Systematic uncertainties are similar between horizontal and vertical polarizations. The emissivity
values from SSM/I and TMI were found to be more consistent over different land-cover types. The maximum systematic difference among emissivities was found to be about 4 %at all frequencies and polarizations. This could be an indicator of uncertainty level from emissivity retrievals despite
10 % error in physical model-based emissivities (Ringerud etal., 2014).
The seasonal variation of emissivities was evaluated bylooking at monthly standard deviation values, and they were
found to be consistent with what is expected qualitatively
from the surface in most regions except the Amazon andSouth America. Moreover, the dynamics of the emissivity estimates compared to surface properties such as soil moistureand vegetation were found to be more promising than the absolute value estimates.
Atmos. Meas. Tech., 8, 1197–1205, 2015

Results of this study highlight the need for a more thorough review of emissivity values before using them in physical models or precipitation measurement algorithms. Daily
or instantaneous emissivity estimates from different sources
may also yield more information about systematic and random uncertainties from retrievals. For more in-depth error
analysis and to find the sources of discrepancies, an analysis
can be done by applying the same inputs for different algorithms that were used for all four sensors. Moreover, modifications could be applied to each retrieval method to accountfor instantaneous biases that could be estimated with respect
to a blended composite emissivity product.
Acknowledgements. This publication was made possible by
NOAA, Office of Education Educational Partnership Program
award NA11SEC4810004. Its contents are solely the responsibilityof the award recipient and do not necessarily represent the official
views of the US Department of Commerce, NOAA.
Edited by: J. Munchak

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