Justice, Chris

Vermote, Eric

NASA Technical Reports Server

Sentinel 3 Science Products: A US Contribution 
Eric Vermote(1) and Chris Justice(2)
(1)NASA GSFC Code 619
Eric.f.vermote@nasa.gov
(2)University of Maryland, Department of Geographical Sciences
.
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
https://ntrs.nasa.gov/search.jsp?R=20140005691 2019-08-29T14:06:18+00:00Z
A Land Climate Data Record
Multi instrument/Multi sensor Science Quality Data Records used to 
quantify trends and changes
Emphasis on data consistency – characterization  
rather than degrading/smoothing the data 
AVHRR
81 82 83 84 85 87 88 89 90 9186 92 93 94 95 96 98 99 00 01 0297 04 0503 07 09 100806
NPP
11  12  13  14  15  16  17
VIIRS
N07 N09 N11 N14
N16 N17
N09
MODIS
Terra
N16
SPOT  VEGETATION
JPSS 1
METOP
AVHRR (GAC) 1982-1999 + 2003-2006
MODIS (MO(Y)D09 CMG) 2000-present
VIIRS 2011 – 2025
SPOT VEGETATION 1999-2000
Sentinel 3  2014 
Sentinel 3
Aqua
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
El Chichon Pinatubo 
Degradation in channel 1
(from Ocean observations)
Channel1/Channel2 ratio
(from Clouds observations)
BRDF CORRECTIONCALIBRATION
ATMOSPHERIC
CORRECTION
Land Climate Data Record (Approach)
Needs to address geolocation,calibration, atmospheric/BRDF correction issues
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
MODIS used as a reference for past and future 
land data record (example NDVI) 
Evaluation over AERONET (2003)
0.007 <Precision < 0.017
FOREST
CROPS
SAVANNA
Precision=0.016
Precision=0.013
Precision=0.01
Independent evaluation of the precision 
Over 2000-2004 CMG daily time series
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
MODIS product and validation methodology 
used to evaluate other surface reflectance 
product: example LANDSAT TM/ETM+
• WELD (D. Roy) 120 acquisitions over 23 AERONET 
sites (CONUS) 
Junchang Ju, David P. Roy, Eric Vermote, Jeffrey Masek, Valeriy Kovalskyy, Continental-scale validation of 
MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods, Remote Sensing of Environment
(2012), Available online 10 February 2012, ISSN 0034-4257, 10.1016/j.rse.2011.12.025.
• GFCC: Comparison with MODIS SR products
– GLS 2000 demonstration
Min Feng, Chengquan Huang, Saurabh Channan, Eric F. Vermote, Jeffrey G. Masek, John R. Townshend, 
Quality assessment of Landsat surface reflectance products using MODIS data, Computers & Geosciences,
Volume 38, Issue 1, January 2012, Pages 9-22, ISSN 0098-3004, 10.1016.
– GLS 2005 (TM and ETM+)
Min Feng Joseph O. Sexton, Chengquan Huang, Jeffrey G. Masek, Eric F. Vermote, Feng Gao, Raghuram
Narasimhan, Saurabh Channan, Robert E. Wolfe, John R. Townshend ,Global, long-term surface reflectance 
records from Landsat: a comparison of the Global Land Survey and MODIS surface reflectance datasets.
Remote Sensing of the Environment (in review)
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
WELD/LEDAPS results (Red-band3)
LEDAPS WELD uses MODIS aerosol
Top of the atmosphere
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
MODIS Reflectance time series
• Reflectance time series show high-frequency variability
• The “noise” is partly due to directional effects.
• Selection of specific geometries decreases temporal coverage
• Can we correct for the directional effect and retain the original 
temporal resolution ?
V
i
s
i
b
l
e
 
 
 
 
 
 
 
N
e
a
r
 
I
R
Orig.
Corrected
François-Marie Bréon, LSCE 
Maignan et al., Rem. Sens. Env., 2004
Analytical model and correction
Linear models : 
(s, v, ) = k0 + k1 F1(s, v, ) + k2 F2(s, v, )
 
 Several choices for F1 and F2 
  F1: Model surface effects (soil roughness) 
  F2: Model volume effects (R.T. within canopy) 
  
(s, v, ) = k0(t) [ 1+ k1/k0 F1(s, v, ) + k2/k0   F2(s, v, ) ]
= k0(t) [ 1+ R F1(s, v, ) + V F2(s, v, ) ]
Correction:
cor(s, v, ) [ 1+ R F1(,	
	) + V F2(
	
	) ] /
[ 1+ R F1(s, v, ) + V F2(s, v, ) ]
Which is the “best” model ?
• Look for the parameters for a best fit (invert the model) 
• Compute error of fit 
• Among the 6 tested models, RossLiHS allows the best fit 
• Clear improvement when using Hot Spot correction 
Cumulative histogram of reflectance [%] error of fit.
670 nm 865 nm
Maignan et al., Rem. Sens. Env., 2004
BRDF model inversion
• R and V are linear function of the NDVI
• We look at the difference between 
successive measurements
• Notations used here for an easy 
inversion of the model parameters
• Matrix writing:
Data location
• MODIS data are distributed as “tiles” (10° of lat.)
• To limit data volume, we focus on a single tile
• Select a tile over Eastern Australia for (i) variety of surface 
cover, (ii) number of clear observations, (iii) low aerosol load
BRDF parameters: R and V
• Analyzed the BRDF parameters distributed in the official 
MODIS products
• Parameters show very unrealistic temporal variations.
• Our method shows more realistic results
Visible band
Near IR band
___ Official
*   Our method
Quantification of time series noise
• For each triplet of observations, one can estimate middle one 
from the earlier and later:
One can then compute a “noise” from the quadratic sum of the 
difference between the measurement and their interpolated 
counterpart:
We use this definition in the following to quantify the time series 
quality
Impact of spatial scale
•The noise of the corrected 
time series is much larger than 
that we obtained earlier using 
CMG (Climate Modeling Grid : 
5 km) lower resolution data.
•We show here a comparison 
of the noise obtained at the full 
resolution against that 
obtained when aggregating 
5x5 pixels.
Noise vs Spatial heterogeneity
•There is a very strong 
correlation between the 
spatial heterogeneity 
(quantified here as the 3x3 
standard deviation) and the 
noise on the corrected time 
series.
•Clearly, the spatial 
heterogeneity affects the 
quality of the time series and 
there is an easy explanation 
for that
Impact of spatial scale
• The “noise” of the time 
series decreases when 
the spatial aggregation 
increases.  There 
seems to be an optimal 
scale at 2 km (4x4 
pixels)
Conclusions (1/2)
• Directional effects on the Earth reflectances are large (factor of 
2 to 4 depending on wavelength)
• There are simple analytical models (3 parameters) that 
reproduce accurately the observed signatures
• The reflectance is modelled as the product of a normalized 
reflectance, that may vary rapidly, and a BRDF model (2 
parameters) that varies more slowly.
• The two model parameters can be parameterized as a linear 
function of the NDVI.
• We have developed a method to estimate easily these 
parameters from the reflectance time series
• Corrected time series are much smoother than their original 
counterpart, and can be used to extract fine signal
Conclusions (2/2)
• The official MODIS BRDF parameters are unreliable
• The time series at the full (500 m) resolution appear noisier 
than at lower resolution
• Spatial heterogeneity of the reflectance is the driving factor for 
this additional noise
• We suggest an optimal resolution of 2 km for the use of MODIS 
data time series
Use of BRDF correction for 
product cross-comparison
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
Cross-Calibration of NOAA 16 AVHRR
The coeffic ients
were  cons is tent with in
les s  than  1%
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
Use of BRDF corrected reflectance for cloud 
mask evaluation of AVHRR Time Series
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
CLAVR (Pathfinder II) cloud mask
AVHRR Time series LTDR V3.0 cloud mask
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
Use of BRDF corrected reflectance for cloud 
mask evaluation of AVHRR Time Series
Using Direct comparison with MODIS Aqua for validation
Comparison of MODIS Aqua and 
NOAA16 AVHRR data, A (Red) 
,B (NIR) ,C (NDVI) are observed 
over AERONET sites for 2003-
2004, D (Red), E(NIR), F(NDVI) 
are simulated using a vegetation 
model that account for spectral 
difference between MODIS and 
AVHRR bands. G shows over 
the AERONET sites MODIS 
NDVI versus corrected AVHRR 
NDVI computed from spectrally 
adjusted AVHRR surface 
reflectance.
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
One of the VIIRS First light images generated by UMD/NOA
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
Use of BRDF correction (VIIRS)
VIIRS SR product Aqua SR product
A ~50km x 50km site in Australia
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
VIIRS calibration is being monitored on a 
continuous basis (selected daily obs)
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  
Conclusions
• Surface reflectance algorithm is mature and pathway 
toward validation and automated QA is clearly identified.
• Algorithm is generic and tied to documented validated 
radiative transfer code enabling easier inter-comparison 
and fusion of products from different sensors 
(MODIS,VIIRS,AVHRR, LDCM, Landsat, Sentinel 2 …)
Land Product Validation and Evolution Workshop, Frascati, Italy, January 28-30, 2014  


Sentinel 3 Science Products: A US Contribution

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