Global LAnd Surface Satellite (GLASS) Products: Characteristics

Global LAnd Surface Satellite (GLASS) Products: Characteristics

Global LAnd Surface Satellite (GLASS) Products:
Characteristics and Preliminary Applications
Shunlin Liang & GLASS data production team
University of Maryland and Beijing Normal University
GV2M, Avignon, Feb. 6, 2014
Acknowledgements
 More than 100+ people contributing to the project for generating the GLASS products;
 Funding supports mainly from the 863 program of China, managed by the National Remote Sensing Center of China (NRSCC), Ministry of Science and Technology
Outline
 Overview of the GLASS products
 (1) Shortwave broadband albedo
 (2) Leaf Area Index (LAI)
 (3) Longwave broadband emissivity
 (4) Shortwave radiation
 (5) Photosynthetically Active Radiation (PAR)
 Summary
Global LAnd Surface Satellite (GLASS) Products
Products
Temporal Spatial Temporal range
resolution
resolution
Leaf area index (LAI)
1981-2013
1km, 5km
8 days
Longwave emissivity
1981-2013
1km, 5km
8 days
Shortwave albedo
1981-2013
1km, 5km
8 days
Incident shortwave radiation
2008-2010
5km
3 hours
Incident PAR
2008-2010
5km
3 hours

FREE
GLASS Product Distribution

Beijing Normal University (BNU) Center for Global Change data
Processing and Analysis
 http://www.bnu-datacenter.com

University of Maryland (UMD) Global Land Cover Facility
 http://glcf.umiacs.umd.edu
(1) Shortwave albedo product
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12
MODIS, MISR
POLDER
MERIS
GEOLAND2
GLOBALBEDO
GLASS
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., & Liu, S. (2014). Improved direct‐estimation algorithm for mapping daily land‐
surface broadband albedo from MODIS data. IEEE Transaction on Geoscience and Remote Sensing, 52(2):907‐919
Liu, N., Liu, Q., Wang, L., Liang, S., Wen, J., Qu, Y., & Liu, S. (2013a). Mapping spatially‐temporally continuous shortwave albedo for global land surface from MODIS data. Hydrology and Earth System Sciences, 17, 2121‐2129
Liu, Q., Wang, L., Qu, Y., Liu, N., Liu, S., Tang, H., & Liang, S. (2013b). A Preliminary Evaluation of GLASS Albedo Product.
International Journal of Digital Earth, 6, 69‐95
AB1
Intermediate
product
AB2
Intermediate
product
MODIS
data
2000—present
MCD43 albedo
product
1981—1999
AB1
STF
AB2
AVHRR
data
AB1
STF
Final
product
Statistics
data base
Intermediate
product
STF
Final
product
: Angular Bin (AB) — two inversion algorithms
Statistics-based Temporal Filtering (STF) — post-processing algorithm
First satellite land product based on the integration of multiple algorithms
Long‐term changes in land albedo
Mean albedo
Global albedo anomaly
Example 1: Calibrating/Validating the State‐of‐the‐art GCM Simulations
0.45
0.4
Shortwave albedo
0.35
BCC‐CSM1
CanCM4
CCSM4
CFSv2
CNRM‐CM5
FGOALS‐s2
GEOS‐5
GFDL‐CM2p1
HadCM3
IPSL‐CM5A‐LR
MIROC4h
MIROC5
MPI‐ESM‐LR
MPI‐ESM‐MR
MRI‐CGCM3
GEWEX
CERES
GLASS
MODIS
ISCCP
MERIS
0.3
0.25
0.2
0.15
0.1
1
2
3
4
5
6
7
8
9
10
11
12
Month
Inter-comparison of 30-year global albedo climatology derived from
satellite products and CMIP5 model outputs.
He, et al. 2014
Example 2
1981-2000
2000-2012
(2) Leaf area index (LAI) product
Temporal
LAI Products Spatial
resolution resolution
(day)
MODIS
1km
8
Temporal
range
CYCLOPES
1/112°
10
2010‐
present
1999‐2003
GLOBECARBON
1/11.2°
10
1998‐2007
Geoland2
1km, 0.05º
10
1981-2012
GLASS
1km, 0.05°
8
1981‐2013
General Regression Neural Networks
Input layer
Pattern layer
Output layer
…
…
…
GRNN
yk
…
xj
…
…
ym
One-year LAI profiles
…
…
y1
x2
…
MODIS reflectance data from an
entire year
x1
Summation layer
xn
A GRNN with a multi-input–multi-output architecture
Xiao, Z., Liang, S., Wang, J., Chen, P., & Yin, X. (2014). Use of General Regression Neural Networks for
Generating the GLASS Leaf Area Index Product from Time Series MODIS Surface Reflectance. IEEE
Transactions on Geoscience and Remote Sensing, 52(1):209-223
(3). GLASS emissivity product
Emissivity is one of the important components in
land surface energy balance and has significant
impacts on model simulations；
R
n
 R
s
n
 R
l
n
 ( 1   ) F d s   F dl    T
4
Insolation
albedo
Emissivity
Longwave downward radiation Skin temperature
No global broadband emissivity satellite product
yet；
Most GCMs assume constant emissivity values
Land surface spectral emissivity products have
large errors
Jan. 2003
Broadband emissivity from MODIS
Aug. 2003
Jin, M., and S. Liang, (2006), Impacts of the MODIS broadband
emissivity on GCM simulation, J. Climate, 19:2867-2881.
Figure 14: Emissivity impacts on surface air temperature
differences in the coupled CAM2/CLM2 model: default
emissivity values and MODIS emissivity maps
products
resolutions, temporal range
wavelengths
CERES emissivity map Global, 10’×10’, no temporal 12 spectral bands and one
variation
broadband（5‐100 µm）
MODIS‐based
baseline fit database
Global, 0.05º, monthly, 2003‐ 10 wavelengths
2011
（3.6 – 14.3 µm）
AIRS
30ºN‐30ºS, monthly ， April 3.7 – 14 µm
2003‐March 2006
（ 0.05
µm
resolution）
30ºN‐30ºS, monthly ， April 3.7 – 14 µm
2007‐March 2011
（ 0.05
µm
resolution）
Global discontinuous, 90m, 5 bands
2000‐present
IASI
ASTER
spectral
spectral
MODIS
Global coverage, 0.05º, 2000‐ 6 bands (3.8‐12.0µm)
present
GLASS
Global, 1km, 0.05º
1 broadband (8‐13.5µm)
GLASS Longwave Emissivity product
Unique features:
 Took advantage of MODIS shortwave reflectance and ASTER spectral emissivity。
 First broadband emissivity product from AVHRR data。
 Cheng, J., S. Liang, Y. Yao, X. zhang (2013). "Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation." IEEE Geoscience and Remote Sensing Letters 10(2): 401‐405.
 Ren, H., S. Liang, G. Yan, J. Cheng (2013). "Empirical algorithms to map global broadband emissivities over vegetated surfaces." IEEE Transactions on Geoscience and Remote Sensing: 51(5):2619‐2631.
 Cheng, J. and S. Liang (2013). "Estimating global land surface broadband thermal‐infrared emissivity from the Advanced Very High Resolution Radiometer optical data." International Journal of Digital Earth: 6,34‐49
 Cheng, J., & S. Liang, (2014). Effects of thermal‐infrared emissivity directionality on surface broadband emissivity and longwave net radiation estimation. IEEE Geoscience and Remote Sensing Letters, 11(2):499‐503
 Cheng, J., S. Liang, Y. Yao, B. Ren, L. Shi, H. Liu, (2014), A comparative study of three land surface broadband emissivity datasets from satellite data, Remote Sensing, 4(1):111‐134 GLASS Longwave emissivity product
Product intercomparison and validation
Comparison with JPL/ASTER in the summer，(a)GLASS; (b)ASTER; (c) differences; (d) Histogram of the differences.
Comparison with JPL/ASTER in the winter，(a) GLASS; (b)ASTER; (c) differences; (d) Histogram of the differences.
In‐situ validation
(4)‐(5) shortwave radiation and PAR products
current global incident shortwave radiation satellite products Insolation Spatial
Temporal Temporal
Products resolution resolutio range
n
ISCCP
280km
3‐hour
1983‐2008
GEWEX‐
1°
3‐hour
1983‐2007
SRB
CERES
140km
3‐hour
1997‐present
GLASS
5km
3‐hour
2008‐2010
WMO requirements for surface downward shortwave irradiance Uncertainty Uncertainty Horizontal Goal
threshold resolution goal
(Wm‐2)
(Wm‐2)
(km)
Global NWP
1
20
10
Agricultural N/A
N/A
1
Meteorology
Climate‐
5
10
25
AOPC
Horizontal resolution
Theshold
(km)
100
20
100
Polar Orbiting:
MODIS
Geostationary:
GOES-W
GOES-E
MSG
MTSAT
FY2C
Solar radiation on Nov. 11, 2008
PAR in July 2008
Validation along with other products
CERES‐MODIS‐CALIPSO‐
GLASS Insolation
ISCCP-FD
CloudSat (CCCM)
Site
Model B
Enhanced
R2
Bias
RMSE
R2
Bias
RMSE
R2
Bias
RMSE
R2
Bias
RMSE
Bondville
0.87
14.68
104.97
0.71
-7.06
149.88
0.84
12.9
119.5
0.82
-0.5
126.16
FortPeck
0.84
10.51
102.75
0.69
9.61
150.37
0.81
5.3
112.40
0.80
2.3
115.02
Goodwin Creek
0.91
-6.29
99.54
0.64
12.61
184.11
0.69
14.3
172.0
0.66
-3.8
179.35
Penn State
0.85
18.17
109.3
0.7
5.92
152.88
0.87
6.9
107.0
0.86
-8.6
111.18
Sioux Falls
0.81
11.52
114.41
0.65
37.83
168.85
0.62
-11.4
167.4
0.58
-37.8
178.77
Boulder
0.81
-12.8
126.38
0.72
6.49
154.96
0.34
-12.0
249.3
0.47
-43.0
214.41
DesertRock
0.92
-52.4
112.94
0.87
-42.4
125.27
0.52
-24.2
198.0
0.49
-26.6
206.38
Impacts of improved PAR products on calculating gross primary productivity (GPP)
GLASS
ISCCP
Princeton
 6 radiation products
 Same model for
calculating GPP
ECWMF
MERRA
NCEP
 in-situ measurements at
12 sites in China
GLASS radiation product
produces the best GPP
values
Cai, W., W. Yuan, S. Liang, et al., (2014), Improved Estimations of Gross Primary
Production Using Satellite-derived Photosynthetically Active Radiation, Journal of
Geophysical Research – Biogeosciences, doi: 10.1002/2013JG002456
Figure 7. Annual
mean GPP
estimates of 2008
and 2009 driven
by six radiation
products: The
grayscale image in
each plot is the
standard deviation
of the annual GPP.
Phase‐II GLASS products
(Dec. 2013‐Nov. 2016)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Incident shortwave radiation
Incident PAR
Broadband albedo
Broadband emissivity
Longwave net radiation
All‐wave net radiation
Skin temperature
Evapotranspiration
Leaf area index
Fraction of absorbed PAR by green vegetation
Fraction of green vegetation coverage
Gross primary productivity
Summary
 Long-term high-quality satellite products are essential in
detecting and assessing global environmental changes
 Global LAnd Surface Satellite (GLASS) products with
unique features have been extensively validated

Sample products and the pdf files of the papers are in the DVD
 Examples are given to demonstrate that they are very
useful for environmental change studies
 Everyone is warmly welcome to evaluate and utilize
them.

Web addresses of the data centers are in the booklet