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Remote Sens. 2014, 6, 111-134; doi:10.3390/rs6010111
remote sensing
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Article
A Comparative Study of Three Land Surface Broadband
Emissivity Datasets from Satellite Data
Jie Cheng 1,*, Shunlin Liang 1,2, Yunjun Yao 1, Baiyang Ren 1, Linpeng Shi 1 and Hao Liu 1
1 State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System
Science, Beijing Normal University, Beijing 100875, China; E-Mails: boyyunjun@163.com (Y.Y.);
ren.baiyang@163.com (B.R.); shi_linpeng@126.com (L.S.); mewmewsakura@163.com (H.L.)
2 Department of Geographical Science, University of Maryland, College Park, MD 20742, USA;
E-Mail: sliang@umd.edu
* Author to whom correspondence should be addressed; E-Mail: brucechan2003@126.com;
Tel./Fax:+86-10-5880-3001.
Received: 10 October 2013; in revised form: 18 November 2013 / Accepted: 3 December 2013 /
Published: 20 December 2013
Abstract: This study compared three broadband emissivity (BBE) datasets from satellite
observations. The first is a new global land surface BBE dataset known as the Global Land
Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land
Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global
Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from
two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as
the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was
more close to NAALSED BBE with a bias and root mean square error (RMSE) of −0.001
and 0.007 for the summer season, −0.001 and 0.008 for the winter season, respectively.
Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS
BBE for six dominant land cover types were compared. The BBE difference between
vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal
variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the
time series were calculated from GLASS BBE and UWIREMIS BBE using the data from
2003 through 2010. The periodic variations of GLASS BBE were stronger than those of
UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in
land surface models for improving their simulation results.
OPEN ACCESS
Remote Sens. 2014, 6 112
Keywords: broadband emissivity; GLASS; NAALSED; UWIREMIS; remote sensing
1. Introduction
Land surface broadband emissivity (BBE) is a key parameter in the estimation of surface energy
budget and is a common input required for a variety of radiative transfer models [1–8]. Because of
limited temporal and spectral information on land surface emissivity, a constant BBE assumption or
simple parameterization schemes are adopted in land surface models and climate models [9–11].
Satellite remote sensing is the only means for providing global land surface BBE with certain
spatial-temporal resolutions. Furthermore, the satellite-derived realistic BBE has demonstrated its
capability in improving the simulation results of global climate models [10,12].
Several BBE datasets have been produced from remote sensing data by using different methods. For
example, Wilber et al., produced a global BBE (5–100 μm) with 10′ × 10′ spatial resolution for
satellite retrievals of longwave radiation by assigning constant emissivity values to International
Geosphere-Biosphere Program (IGBP) land cover types [13]; Ogawa et al., mapped the global monthly
BBE (8–13.5 μm) by converting the Moderate-resolution Imaging Spectroradiometer (MODIS)
narrowband emissivity product (approximately 5 km) and a North African BBE (8–13.5 μm) using the
Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) narrowband
emissivity product (90 m) [14,15]. Peres et al., produced a global BBE (3–14 µm) map at a 3 km
spatial resolution by converting the narrowband emissivities retrieved from the Spinning Enhanced
Visible and Infrared Imager (SEVIRI) onboard METEOSAT Second Generation (MSG) using
Vegetation Cover Method (VCM) [16].
Recently, some researchers have produced a few narrowband emissivity products from which the
BBE could be obtained by converting it to BBE via a linear regression function [17,18], including the
North American ASTER Land Surface Emissivity Database (NAALSED) composited from the
ASTER narrowband emissivity product [19], the University of Wisconsin Global Infrared Land
Surface Emissivity Database (UWIREMIS) retrieved by adjusting MODIS narrowband emissivity
product (MOD11) with the proposed baseline fit method [20], and those derived from hyperspectral
resolution thermal infrared (TIR) data [2,21–24].
These BBE and narrowband emissivity products share at least two common characteristics:
(1) either the spatial or temporal resolutions of these products are limited. A few are just a BBE map at
global or regional scales. There are two narrowband emissivities (summer season and winter season) in
the NAALSED for the entire nine years (2000–2008). The spatial resolution of MOD11 emissivity
product is 0.05°, and the spatial resolution of single nadir view of current hyperspectral resolution TIR
sensors is larger than 10 km [25]. (2) Most of them are not well validated. For example, the BBE
derived from ASTER, MODIS and SEVIRI narrowband emissivity are not validated [15,26]. Long
time series of high spatial-temporal global land surface BBE will benefit the studies of surface energy
budget. Cheng et al., [27,28] proposed the algorithms for retrieving global land surface BBE from the
Advanced Very High-Resolution Radiometer (AVHRR) and MODIS optical data, and produced the
global eight-day 1 km and 0.05° land surface BBE from 1981 through 2010. This product was known
Remote Sens. 2014, 6 113
as the Global Land Surface Satellite (GLASS) BBE and released to public in November 2012 [29],
and can be ordered from the BNU Center for Global Change Data Processing and Analysis
(http://www.bnu-datacenter.com) and Global Land Cover Facility (http://glcf.umd.edu). GLASS
emissivity has been validated by limited ground measurements obtained from several field
experiments [30], and also by comparison with the BBE derived from the ASTER and MODIS
narrowband emissivity products at regional scales. The objective of this study is to compare GLASS
BBE, the BBE calculated from NAALSED emissivity and UWIREMIS emissivity at both the global and
regional scales, and to provide a guideline for the potential users. The rest of this paper is arranged as
follows. Section 2 introduces the used datasets; Section 3 describes the method of comparing three BBE
datasets; the results and discussion are presented in Section 4; a brief conclusion is provided in Section 5.
2. Data
2.1. GLASS BBE
The Global Land Surface Satellite (GLASS) BBE is a BBE (8–13.5 μm) product that was derived
from AVHRR and MODIS optical data with our newly developed algorithms [27,28,31,32] . GLASS
BBE was composed of two parts: the first is the global eight-day 1 km land surface BBE retrieved
from MODIS seven black-sky albedos ranging from 2000 through 2010; and the second is the global
eight-day 0.05° land surface BBE retrieved from the AVHRR visible and near infrared (VNIR)
reflectance during 1981–1999. In the algorithm used to generate GLASS BBE from MODIS albedos,
the land surface was classified by five types: water, snow/ice, bare soils, vegetated areas and transition
zones. Water and snow/ice were determined by the flag in the input data. The latter three types were
determined by the Normalized Difference Vegetation Index (NDVI) threshold values, i.e., bare soils
(0 < NDVI ≤ 0.156), vegetated areas (NDVI > 0.156), and transition zones (0.1 < NDVI < 0.2). Note
there are overlapped areas between bare soils and transition zones, transition zones and vegetated
areas. The BBE of water and snow/ice was assigned as 0.985 by combining BBE calculated from the
emissivity spectrum in the spectral library (the ASTER spectral library [33] (http://spclib.jpl.nasa.gov)
and the MODIS UCSB spectral library (http://www.icess.ucsb.edu/modis/EMIS/html/em.html)) and
BBE calculated from the emissivity spectra simulated by radiative transfer models [34]. The BBE of
bare soils, vegetated areas and transition zones were formulated as the linear function of seven MODIS
narrowband black-sky albedos individually. When the NDVI was less than 0.1 or larger than 0.2, we
used the formula for bare soils or vegetated areas to calculate their BBE respectively. In the overlapped
areas between bare soils and transition zones (0.1 < NDVI ≤ 0.156), BBE was the average of those
calculated by the formulae for bare soils and transition zones, whereas BBE for overlapped areas for
transition zones and vegetated areas (0.156 < NDVI < 0.2) was the average of those calculated by the
formulae for transition zones and vegetated areas. The BBE derived from the MODIS albedos was
validated by the field measurements conducted over desert areas in the United States and China, and
the absolute difference was found to be 0.02 [27,30]. The method of estimating BBE from AVHRR
VNIR reflectance data was similar to that used for MODIS optical data. The differences lies in (1) the
threshold values for identifying three land surface types. A pixel with 0 < NDVI ≤ 0.2 was identified
as bare soils, a pixel with 0.145 < NDVI ≤ 0.243 was identified as transition zones and a pixel with
Remote Sens. 2014, 6 114
NDVI > 0.2 was identified as vegetated areas. (2) The input of the algorithms. The inputs of the
algorithm developed for AVHRR was the reflectance of Channels 1 and 2, whereas the input for the
algorithm designed for MODIS was seven narrowband black-sky albedos. The BBE derived from the
AVHRR was consistent with that derived from MODIS data. Comparing the BBE derived from
AVHRR and MODIS data in 2000, the mean bias and RMS of the difference were 0.001 and 0.01,
respectively [28].
2.2. NAALSED Emissivity
The North American ASTER Land Surface Emissivity Database (NAALSED) is a mean seasonal
gridded 100 m emissivity database that composed from ASTER 90 m standard land surface temperature
and emissivity (LST&E) products over North America [19]. NAALSED includes two seasons, the
summer season (July–September) and winter season (Jane–March). In the generation of NAALSED, the
cloud contaminated ASTER pixels are screened out [35]. For each location, NAALSED emissivity is the
average emissivity of all-clear sky pixels from all the ASTER scenes acquired in the summer and winter
seasons of 2000–2008. NAALSED also produced the gridded emissivity products on spatial resolutions
of 1 km, 5 km and 50 km by aggregating 100 m emissivity product. NAALSED V2.0 product consists of
18 bands; the mean and standard deviation for the five bands surface narrowband emissivity, surface
temperature, NDVI, a land-water map, the total yield (number of ASTER observations collected at each
pixel), and geodetic latitude and longitude. NAALSED was validated by the laboratory-measured sand
emissivity collected at nine pseudo-invariant sand sites in the western United States [36]. The mean
difference for all nine sites and all five ASTER thermal-infrared (TIR) channels was found to be 0.016,
which represents approximately a 1 K error in LST retrieval.
2.3. UWIREMIS Emissivity
The University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) is a
monthly data set derived from the MODIS composited monthly 0.05° narrowband emissivity product
(MODIS level 3 operational land surface emissivity product MYD11) by the baseline fit (BF) method [19].
The BF method derived global land surface emissivity at ten hinge points (3.6, 4.3, 5.0, 5.8, 7.6, 8.3,
9.3 10.8, 12.1 and 14.3 μm) by adjusting a baseline emissivity spectrum based on MOD11 land surface
narrowband emissivity product according to a conceptual model of land surface emissivity. Testing by
the 123 emissivity spectra in the MODIS UCSB emissivity library indicated that the BF-derived
emissivity generally agrees well with the laboratory-measured emissivity in shape and magnitude. The
UWIREMIS emissivity could be interpolated between the hinge points and also could be used to
derive a high spectral resolution emissivity spectrum by virtue of the principal component regression
and eigenvector from laboratory-measured emissivity spectra.
3. Methodology
3.1. Broadband Emissivity Calculation
The hemispherical emissivity is defined as follows [36],
Remote Sens. 2014, 6 115
(1)
where μ is the cosine of view angle, λ is the wavelength, and ε(μ, λ) is the directional emissivity. The
broadband emissivity can be represented as
(2)
where Ts is the surface temperature. Both the satellite emissivity products and field measured
emissivity are derived from the observations at a certain view angle. Thus, the derived emissivity has
the directionality. Under the framework of current satellite emissivity retrieval, it is impractical to
obtain emissivity at enough view angles to calculate hemispherical emissivity. The satellite retrieved
directional emissivity is usually used in Equation (2), and the emissivity directionality is ignored. The
directionality was also ignored in the BBE that derived from the existing satellite narrowband
emissivity products [15,27]. This ignorance will certainly incur some errors in the broadband
emissivity and surface longwave net radiation estimation [37–39].
The NAALSED emissivity and UWIREMIS emissivity are narrowband emissivity products. We
converted them to BBE at 8–13.5 μm by using the linear functions before the comparison. The
NAALSED emissivity was converted to BBE by the formula given below [28]
(3)
where εNAALSED is the NAALSED BBE, ε10 – ε14 are the five ASTER narrowband emissivities. The
R-square and root mean square error (RMSE) for Equation (2) are 0.983 and 0.005, respectively.
Regarding the UWIREMIS emissivity, we developed the conversion formula using the 89 spectra from
the ASTER spectral library and 109 emissivity spectra from MODIS UCSB emissivity by linear
fitting. The surface feature types include soil, vegetation, rock, water body and ice/snow. As data
selected from the ASTER spectral library are directional-hemispheric reflectance, they should be
converted into emissivity according to Kirchhoff’s law. That is, under thermal equilibrium, the
relationship between emissivity and reflectance can be expressed as ελ = 1 − ρλ. Based on the above
spectral data, we calculated the emissivity at ε6 – ε9 by interpolation and calculated the broadband
emissivity at 8–13.5 μm. The regression analysis is then conducted to obtain the linear relationship
between the BBE and emissivity at ε6 – ε9. The formula is expressed as follows
(4)
where εUMIREMIS is the UWIREMIS BBE, ε6 – ε9 are the UWIREMIS emissivity at 8.3, 9.3 10.8 and
12.1 μm. The R-square and RMSE for the fitting formula are 0.983 and 0.005, respectively.
3.2. Compare to NAALSED BBE
The primary objective of the ASTER temperature and emissivity separation (TES) algorithm is to
provide high accuracy narrowband emissivity for large spectral contrast surface types such as soils and
rocks [40,41]. Some validation work indicated the ASTER narrowband emissivity can achieve high
accuracy over arid and semi-arid areas [36,42–44]. Therefore, the accuracy of emissivity retrieval for
soils and rocks is guaranteed. Regarding surface types with small spectral contrast such as water
1
0
(, ) 2 ( , )hd
ελ πε
μ
λ
μμ
=
2
1
2
1
(, ) ( , )
(, )
s
bb
s
hBTd
BTd
λ
λ
λ
λ
ελλ λ
ε
λλ
=
10 11 12 13 14
0.197 0.025 0.057 0.237 0.333 0.146
NAALSED
ε
ε
ε
ε
ε
ε
=+++++
UMIREMIS 6 7 8 9
0.068 0.045 0.297 0.215 0.372
ε
ε
ε
ε
ε
=+ + + +
Remote Sens. 2014, 6 116
bodies and vegetated areas, the accuracy of emissivity inversion cannot meet the design goal as
reported by several authors [41,45]. The TES algorithm has been modified several times to
accommodate low emissivity spectral contrast and error in the measured data and the accuracy has
been improved over the first version [46]. The ASTER emissivity product is well recognized by the
remote sensing community and the most accurate emissivity product currently available. The RMSE of
the converting formula for ASTER is 0.005, which is equal to the RMSE of the converting formula for
UWIREMIS emissivity and less than that for MODIS. Theoretically, the accuracy of BBE derived by
converting ASTER emissivity is better than that derived by converting UWIREMIS emissivity and
MODIS emissivity. Thus, the NAALSED BBE was used as reference to evaluate the GLASS BBE and
UWIREMIS BBE. The eight-day 1 km sinusoidal projection GLASS BBE was projected to the 0.05°
Climate Model Grids (CMG). The summer and winter seasons GLASS BBE were composited by
averaging the data of January–March and July–September from years 2000 through 2008. As large
year-to-year variability in UWIREMIS emissivity was observed in its early evaluation [3], the summer
and winter UWIREMIS BBE were composited from the data ranging from the year 2003 through to 2006.
The spatial coregistration was performed by finding the nearest pixel in GLASS and UWIREMIS
according to the geolocation of each NAALSED pixel. The spatial-temporal matched GLASS BBE
and UWIREMIS BBE were compared to NAALSED BBE.
3.3. Comparison between UWIREMIS BBE and GLASS BBE
To match the spatial resolution of the UWIREMIS BBE, we designed the code that can mosaic the
eight-day 1 km sinusoidal projection GLASS BBE into the eight-day 0.05° CMG BBE. Then the
monthly mean GLASS BBE was calculated by averaging the mosaic BBE within the entire month to
match the temporal resolution of the UWIREMIS BBE. The spatial distribution patterns of GLASS
BBE and UWIREMIS BBE in January, April, July and October 2003 for the dominant land cover
types were analyzed. The seasonal pattern of GLASS BBE and UWIREMIS BBE was analyzed with
the data from 2003 through 2006. Time series for GLASS BBE and UWIREMIS BBE was compared
to each other with the data from 2003 through 2010.
4. Results and Discussion
4.1. Compare to NAALSED BBE
Figures 1 and 2 show the comparison results between NAALSED BBE and GLASS BBE for the
summer season and winter season, respectively. Note that the display difference in the Great Lakes
was attributed to the use of different water/land masks. The BBE difference was calculated only at the
pixel with both NAALSED BBE and GLASS BBE. Visually, GLASS BBE was more complete than
NAALSED BBE, especially for the winter season. There was almost no missing data in the GLASS
BBE while there were many gaps in the NAALSED BBE. The spatial pattern of NAALSED BBE and
GLASS BBE were very similar. The BBE was relatively low in western semi-arid areas of the US, for
example over the quartz-rich deserts of southeastern California, the Colorado Plateau, and the Grand
Desierto in Mexico. The BBE was relatively high in the eastern agriculture areas of the US. In the
western US, the GLASS BBE was larger than NAALSED BBE in summer season. In the northern US,
Remote Sens. 2014, 6 117
the GLASS BBE was larger than NAALSED BBE in winter season. In general, GLASS BBE and
NAALSED BBE were in good agreement. The bias and RMSE were −0.001 and 0.007 for the summer
season, –0.001 and 0.008 for the winter season, respectively.
Figure 1. Comparison between NAALSED BBE and GLASS BBE for summer season.
(a) GLASS BBE; (b) NAALSED BBE; (c) the difference between GLASS BBE and
NAALSED BBE; (d) the histogram of the difference.
(a) (b)
(c) (d)
Figure 2. Comparison between NAALSED BBE and GLASS BBE for winter season.
(a) GLASS BBE; (b) NAALSED BBE; (c) the difference between GLASS BBE and
NAALSED BBE; (d) the histogram of the difference.
(a) (b)
Remote Sens. 2014, 6 118
Figure 2. Cont.
(c) (d)
The comparison results between NAALSED BBE and UWIREMIS BBE are presented in Figures 3
and 4. Visually, UWIREMIS BBE was more complete than NAALSED BBE, especially for the winter
season. The spatial pattern of UWIREMIS BBE and NAALSED BBE were very similar in the western
US. The BBE was relatively low in the western semi-arid area. The UWIREMIS BBE was larger than
NAALSED BBE in the western US. The UWIREMIS BBE and NAALSED BBE were quite different
in the northeastern US. The UWIREMIS BBE was much lower than NAALSED BBE. The difference
between UWIREMIS BBE and NAALSED BBE was smaller than that between GLASS BBE and
NAALSED BBE in the western US, where the difference between UWIREMIS BBE and NAALSED
BBE was larger than that between GLASS BBE and NAALSED BBE in the northeastern US. The bias
and RMSE were −0.006 and 0.009 for the summer season, −0.008 and 0.011 for the winter season,
respectively. It is evident that the GLASS BBE was closer to NAALSED BBE than UWIREMIS BBE.
As described in Section 3.2, the BBE derived by converting ASTER emissivity has the highest
accuracy in theory. So, the composited NAALSED BBE has the highest accuracy accordingly. Thus,
the GLASS BBE was accurate than UWIREMIS BBE. Moreover, the validation studies indicated that
the accuracy of 1 km GLASS BBE is 0.02 [30] whereas the UWIREMIS emissivity and UWIREMIS
BBE are not validated.
Figure 3. Comparison between NAALSED BBE and UWIREMIS BBE for the summer
season. (a) UWIREMIS BBE; (b) NAALSED BBE; (c) the difference between
UWIREMIS BBE and NAALSED BBE; (d) the histogram of the difference.
(a) (b)
Remote Sens. 2014, 6 119
Figure 3. Cont.
(c) (d)
Figure 4. Comparison between NAALSED BBE and UWIREMIS BBE for the winter
season. (a) UWIREMIS BBE; (b) NAALSED BBE; (c) the difference between
UWIREMIS BBE and NAALSED BBE; (d) the histogram of the difference.
(a) (b)
(c) (d)
Remote Sens. 2014, 6 120
4.2. Comparison between UWIREMIS BBE and GLASS BBE
4.2.1. Spatial Distribution Pattern
Figure 5 presents the distribution of six dominant land cover types, combined from the 2003
MODIS Land cover product (MCD12C1). The global distribution of UWIREMIS BBE and GLASS
BBE for four seasons (January, April, July and October) in 2003 is presented in Figure 6. In general,
the BBE was very low over arid and semi-arid areas, for example, the Sahara Desert, northwest China,
and the western United States. Shrub also had a lower BBE. The vegetated areas had relatively high
BBE. The BBE for vegetated areas usually increases with the increasing of fractional vegetation cover,
but the seasonal variation of BBE over vegetated areas cannot be seen from Figure 6 visually. In the
algorithm for producing GLASS BBE, the BBE for snow/ice was assigned as 0.985. The UWIREMIS
snow/ice BBE was calculated from emissivity at the four hinge points derived from the MODIS snow
emissivity, which was retrieved by use of a physical-based day/night algorithm. The UWIREMIS
snow/ice BBE was variable and does not exactly equal 0.985 at most conditions. The colors that
represent snow/ice BBE were different in Figure 6. From the color of UWIREMIS BBE at Greenland,
we can see the seasonal variation of snow cover. Most areas of Greenland were covered by snow in
January and April, and most snow had melted in July. The UWIREMIS BBE was low in April and
October in the northern part of Europe. This was unreasonable as both the snow and forest had
relatively high BBE. The snow/ice flag was extracted from the MODIS reflectance product
(MOD09A1) in the GLASS BBE algorithm. The snow cover variation for four seasons could be
reflected from the GLASS BBE variations. The high latitude area was covered by snow in January,
and the snow began to melt with the passage of time. There was almost no snow cover in July except
in Greenland. By October, snow fall was present at high latitudes.
Figure 5. Global distribution of six land cover types composited from 2003 MODIS land
cover product.
Remote Sens. 2014, 6 121
Figure 6. Geographical distribution of global UWIREMIS BBE and GLASS BBE.
(a) UWIREMIS January BBE; (b) GLASS January BBE; (c) UWIREMIS April BBE;
(d) GLASS April BBE; (e) UWIREMIS July BBE; (f) GLASS July BBE; (g) UWIREMIS
October BBE; (h) GLASS October BBE.
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Remote Sens. 2014, 6 122
4.2.2. Seasonal Pattern
The monthly average BBE for barren is presented in Figure 7. The Northern Hemisphere (NH)
GLASS BBE in summer was larger than that in winter and almost constant in the Southern
Hemisphere (SH). The UWIREMIS BBE exhibited a decline trend from January to December in the
NH and SH. The variation of both global GLASS BBE and UWIREMIS BBE were similar to variation
in NH. The soil moisture is the main factor that influenced its emissivity. The soil emissivity increase
with the increasing of water content before it is Saturation. The soil moisture in summer is higher than
that in winter in the NH. The soil moisture is not a predictor in the GLASS BBE algorithm. The
seasonal variation of GLASS BBE over barren is consistent with that of the soil moisture in the NH.
Thus, the variation of GLASS BBE over barren in the NH seems more reasonable than that of
UWIREMIS BBE.
Figure 7. Monthly mean BBE of barren calculated from UWIREMIS BBE (a) and GLASS
BBE (b) for global, North Hemisphere (NH) and South Hemisphere (SH), respectively.
(a) (b)
The monthly average BBE for five vegetated land cover types calculated from UWIREMIS BBE
and GLASS BBE respectively is presented in Figure 8. In order to better analyze the seasonal variation
of vegetated land cover types, we also calculated the monthly average NDVI using the MODIS
vegetation index product (MOD13C2) from 2003 through 2006. As shown in Figure 8, the variation of
NDVI was coincided with the growth season of vegetation in NH (from May to September). The
NDVI started from a lower value in winter, increased gradually from spring season until July when the
NDVI achieved the maximum. Then, NDVI decreased gradually and reached a lower value again in
winter season. In SH, the variation of NDVI is quite small. Generally, the emissivity of vegetation
canopy is higher than that of bare soil. The emissivity of mixed pixel composed of vegetation canopy
and bare soil increases with an increase of fractional vegetation cover [47–49]. The fractional
vegetation cover can be represented by the NDVI. Thus, the seasonal variation of BBE should coincide
with the seasonal variation of NDVI. However, this is not the case for both GLASS BBE and
UWIREMIS BBE.
The seasonal variation of SH GLASS BBE was very small, while the seasonal variation of NH
GLASS BBE was opposite to that of NDVI, except for savanna. According to the NDVI of savanna,
Remote Sens. 2014, 6 123
we can judge that savanna belongs to a sparsely vegetated pixel whose emissivity was controlled
mainly by the emissivity of bare soil. The seasonal variation of NH GLASS BBE resembled a cosine
curve and the BBE of the growth season is lower than that of the non-growth season in NH for forest,
grass, and crop. The seasonal variation of GLASS BBE for shrub agreed well with the variation of
NDVI. The seasonal variation of global GLASS BBE was similar to that of NH GLASS BBE except
for that for savanna. By contrast, the seasonal variation of UWIREMIS BBE in global and NH was
similar to that of GLASS BBE for grass and crop. The seasonal variation of global and NH
UWIREMIS BBE was very small for forest. The seasonal variation of UWIREMIS BBE in SH is larger
than that of GLASS BBE in SH, and exhibits a peak value in summer for forest, grass, crop and savanna.
The seasonal variation of UWIREMIS BBE was very small for shrub. In conclusion, the seasonal
variation of GLASS BBE is more reasonable that that of UWIREMIS BBE and even GLASS BBE
cannot reflect the seasonal variation of fractional vegetation cover for most vegetated land cover types.
According to the studies of Wang and Liang [50], the seasonal variation of monthly BBE derived
by converting MODIS narrowband emissivity product during 2002 and 2006 was very small, and the
monthly BBE derived by converting ASTER narrowband emissivity during 2000 and 2007 in summer
was lower than that in winter over six surface radiation budget observing network (SURFRAD) sites
(Bondville, Boulder, Fort Peck, Goodwin Creek, Penn State and Sioux Falls). We can deduce that
MODIS narrowband emissivity lack seasonal variation because the used coefficients for converting
narrowband emissivity to broadband are all positive. Furthermore, the temporal information was not
considered in the baseline fit method [20]. So, it is not difficult to understand why UWIREMIS BBE
lacks seasonal variation. As the ASTER BBE in summer is lower than that in winter, GLASS BBE in
summer is likely to be lower than that in winter for the reason that the algorithm for retrieving GLASS
BBE established the linear relationship between ASTER BBE and MODIS black-sky albedos.
Figure 8. Monthly mean BBE of five land cover types calculated from UWIREMIS BBE
and GLASS BBE for global, North Hemisphere (NH) and South Hemisphere (SH),
respectively. (a) GLASS Forest BBE; (b) UWIREMIS Forest BBE; (c) MODIS Forest
NDVI; (d) GLASS Grass BBE; (e) UWIREMIS Grass BBE; (f) MODIS Grass NDVI;
(g) GLASS Crop BBE; (h) UWIREMIS Crop BBE; (i) MODIS Crop NDVI; (j) GLASS
Shrub BBE; (k) UWIREMIS Shrub BBE; (l) MODIS Shrub NDVI; (m) GLASS Savanna
BBE; (n) UWIREMIS Savanna BBE; (o) MODIS Savanna NDVI.
(a) (b)(c)
Remote Sens. 2014, 6 124
Figure 8. Cont.
(d) (e) (f)
(g) (h) (i)
(j) (k)(l)
(m) (n)(o)
We first calculated the average NAALSED BBE, GLASS BBE and UWIREMIS BBE for summer
and winter seasons with data used in Section 3.2. The results are presented in Table 1. NAALSED
BBE in summer was lower than that in winter for all the six land cover types. GLASS BBE in summer
was lower than that in winter for forest, grass and crop, and GLASS BBE in summer was higher than
that in winter for shrub, savanna and barren. UWIREMIS BBE in summer was lower than that in
winter for grass, shrub, savanna and barren, and UMIREMIS BBE in summer was higher than that in
winter for forest and crop. These results indicated that seasonal variation is incorrectly characterized in
Remote Sens. 2014, 6 125
North America by all three BBE datasets. For NAALSED BBE and UWIREMIS BBE, this poor
seasonal characterization can be ascribed to poor seasonal variation of the ASTER and MODIS
narrowband emissivity. Regarding the GLASS BBE, its seasonal variation was influenced by both
ASTER narrowband emissivity and MODIS spectral albedos. Taking deciduous needle leaf forest land
cover as an example, we selected a homogeneous site (the land cover remained unchanged from 2001
through 2010; the central location: 59.8°N, 128.7°E) from MODIS land cover product, and
downloaded the ASTER narrowband emissivity product, MODIS vegetation index product and
MODIS albedo product. The MODIS and ASTER data were spatial matched. We averaged the NDVI
and spectral albedos for 3 × 3 MODIS pixels, and averaged ASTER narrowband emissivity from 33 × 33
ASTER pixels. The ASTER BBE was calculated from averaged ASTER narrowband emissivities. We
also calculated the corresponding BBE using GLASS BBE algorithm for vegetation with MODIS
spectral albedos. The result is shown in Figure 9. As seen from Figure 9a, the NDVI increased
gradually from spring and achieved the maximum in summer, then begun to decrease and achieved the
minimum in winter; Figure 9b shows the corresponding MODIS spectral albedos. In growth season,
the variation of first four albedos was contrary to the variation of NDVI, as the albedos tend to
decrease with the increasing amount of vegetation. The seasonal variation of last three albedos was
very small, as they did not reflect the growth of vegetation. Figure 9c presents the calculated ASTER
BBE. The maximum occurred in March and the minimum appeared in September. Overall, the ASTER
BBE did not exhibit seasonal variation. The derived GLASS BBE is provided in Figure 9d. Its
seasonal variation resembled that of MODIS first four albedos. By comparing Figures 9b and 9d, we
can see clearly that the variation of GLASS BBE is mainly determined by the seasonal variation of
MODIS spectral albedos. In order to better characterize land surface [51,52], both the ASTER
narrowband emissivity and MODIS narrowband emissivity products should be improved to consider
the seasonal variation for vegetated areas. We are improving the GLASS BBE algorithm for vegetation
to incorporate the seasonal variation of vegetation.
Table 1. Seasonal average BBE for six land cover types calculated from the data used in
Section 3.2.
Data Set Forest Grass Crop Shrub Savanna Barren
Summer Season
NAALSED 0.975±0.003 0.966±0.007 0.972±0.005 0.952±0.010 0.972±0.005 0.938±0.017
GLASS 0. 971±0.003 0. 965±0.005 0. 967±0.004 0. 959±0.007 0. 968±0.002 0. 951±0.017
UWIREMIS 0.965±0.008 0.962±0.007 0.967±0.006 0.951±0.010 0.959±0.006 0.940±0.015
Winter Season
NAALSED 0.977±0.003 0.974±0.005 0.974±0.005 0.960±0.010 0.974±0.002 0.939±0.021
GLASS 0.977±0.006 0.969±0.009 0.969±0.005 0.957±0.003 0.966±0.002 0.946±0.018
UWIREMIS 0.963±0.007 0.964±0.006 0.966±0.006 0.956±0.008 0.968±0.005 0.941±0.015
Remote Sens. 2014, 6 126
Figure 9. The calculated parameters for a homogeneous deciduous needle leaf forest site
using coregistered MODIS and ASTER data from 2000 through 2010. (a) NDVI;
(b) MODIS spectral albedo; (c) ASTER BBE; (d) GLASS BBE.
(a) (b)
(c) (d)
4.2.3. Time Series
The spatial-temporal matched GLASS BBE and UWIREMIS BBE were used to calculate the time
series from 2003 through 2010. For GLASS BBE, there were regular periodic variations globally for
crop, forest, grass, shrub and barren. The change trend for savanna was not significant. Regarding
UWIREMIS BBE, there were regular periodic variations globally for crop. For forest, grass and shrub,
the times series did not show periodic variations. The change trend of time series for savanna was
similar to that derived from the GLASS BBE. The regular periodic variations for barren were quite
weak. The periodic variations of GLASS BBE were stronger than that of UWIREMIS BBE, as we can
clearly see from Figure 9. Figure 10 displays the temporal variations of GLASS BBE globally for a
few major land cover types randomly selected from areas with relatively homogeneous land cover.
There were some minor disagreements of BBE from AVHRR and MODIS data, but overall the
long-term values were stable and consistent. In comparison, the UWIREMIS BBE values had much
larger variations for most land cover types.
Remote Sens. 2014, 6 127
Figure 10. Time series of mean GLASS BBE and UWIREMIS BBE from years 2000–2010.
(a) Forest; (b) Grass; (c) Crop; (d) Shrub; (e) Savanna; (f) Barren.
(a)
(b)
(c)
(d)
Remote Sens. 2014, 6 128
Figure 10. Cont.
(e)
(f)
Figure 11. Long-term global BBE of five land cover types from GLASS BBE and
UWIREMIS BBE product. (a) Forest; (b) Grass; (c) Crop; (d) Savanna; (e) Barren.
(a)
(b)
Remote Sens. 2014, 6 129
Figure 11. Cont.
(c)
(d)
(e)
5. Conclusions
In this study, we compared three land surface BBE datasets. The first is a new global land surface
BBE dataset known as GLASS BBE. The left two are NAALSED BBE and UWIREMIS BBE, which
were calculated from two independent narrowband emissivity products, respectively. NAALSED BBE
was taken as the reference for the good validation performance of ASTER narrowband emissivity
product to evaluate the GLASS BBE and UWIREMIS BBE. These two BBE were more complete than
NAALSED BBE, especially during the winter season. There were almost no gaps in these two BBE
whereas the gaps in the NAALSED BBE can be easily seen elsewhere. The GLASS BBE was in good
agreement with the NAALSED BBE for both the summer season and winter season. The bias and
RMSE were −0.001 and 0.007 for the summer season, −0.001 and 0.008 for the winter season,
respectively. The difference between UWIREMIS BBE and NAALSED BBE was larger than that
between GLASS BBE and NAALSED BBE. The bias and RMSE were −0.006 and 0.009 for summer
Remote Sens. 2014, 6 130
season, −0.008 and 0.011 for winter season, respectively. GLASS BBE was more accurate than
UWIREMIS BBE.
The spatial distributions of GLASS BBE and UWIREMIS BBE in 2003 for six land cover types
were compared. The BBE difference between vegetated areas (e.g., crop, forest, savanna, grass and
shrub) and non-vegetated (barren) can be easily seen. However, the seasonal variation of BBE for
vegetated areas was hard to find. The snow cover variation for four seasons could be reflected from the
BBE variations. The monthly average BBE for six land cover types were calculated from UWIREMIS
BBE and GLASS BBE ranging from 2003 through 2006, based on which we analyzed the seasonal
pattern of two BBE datasets. For barren, GLASS BBE can reflect its seasonal variation while
UWIREMIS BBE failed. Regarding vegetated areas, the seasonal variation of GLASS BBE was more
reasonable that that of UWIREMIS BBE even GLASS BBE cannot reflect the seasonal variation of
fractional vegetation cover for most vegetated land cover types. The time series were calculated from
GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of
GLASS BBE were stronger than those of UWIREMIS BBE. The temporal variations of GLASS BBE
globally for a few major land cover types randomly selected from areas with relatively homogeneous
land cover. There were some minor disagreements of BBE from AVHRR and MODIS data, but overall
the long-term values were stable and consistent. In comparison, the UWIREMIS BBE values had
much larger variations for most land cover types.
In conclusion, GLASS BBE is the first global long time series land surface BBE dataset of high
quality, and can be used in calculating surface longwave net radiation and incorporated in land surface
models for improving model simulation results. We are improving the algorithm to better characterize
the seasonal variation of vegetated land cover types.
Acknowledgments
The NAALSED emissivity is obtained from http://emissivity.jpl.nasa.gov. The UWIREMIS
emissivity is obtained from http://cimss.ssec.wisc.edu/iremis/. This work was supported by the
National Natural Science Foundation of China via Grant 41371323, the National High Technology
Research and Development Program of China via Grant 2013AA121201 and Beijing Youth
Fellowship Program via Grant YETP0233.
Conflicts of Interest
The authors declare no conflict of interest.
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