We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis

Gopalan, A.

Khayat, M.

Leptoukh, G.

Zubko, V.

NASA Technical Reports Server

I Source of Acquisition 
’ NASA Goddard Space Flight Center 
Remote Sensing Data Visualization, Fusion and Analysis via Giovanni 
G. Leptoukh ’, V. Zubkob, A. Gopalanb, M. Khayatb 
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA - Grenorv.Leptoukh@,nasa.gov 
RS Information Systems, Inc - (vzubko, agopalan, mkhayat@pop600.gsfc.nasa.gov) 
Abstract -We describe Giovanni, the NASA Goddard 
developed online visualization and analysis tool that allows 
users explore various phenomena without learning remote 
sensing data formats and downloading voluminous data. Using 
MODIS aerosol data as an example, we formulate an 
approach to the data fusion for Giovanni to further enrich 
online multi-sensor remote sensing data comparison and 
analysis. 
Keywords: Remote sensing, Data distribution, Data tools, Data 
fusjo~, V;&ie-acided servkes, Aeyosoll ogtjcal,th!c!cness, MOFDIS, 
Terra, Aqua, Giovkni 
1. INTRODUCTION 
The NASA Earth Observing System (EOS) multi-satellite data 
archives are indispensable for studying regional or global 
atmospheric phenomena. Until recently, using this data required 
being able to locate and retrieve the relevant data coupled with a 
detailed understanding of the data’s complicated internal structure. 
Consequently, this data was largely unusable to the public at large 
as gaining the knowledge required to carry out the data reduction 
is a time-consuming task which must be undertaken well in 
advance. Even for experienced users analysis of multi-sensor data 
sets that are typically in different formats, structures, and 
resolutions is a daunting task. 
The NASA Goddard Earth Sciences Data and Information 
Services Center (GES DISC) has recognized this complexity and 
has taken a major step towards developing a user friendly Web 
interface that allows users to perform interactive analysis online 
without downloading any data, or needing to understand 
complicated data structures. The Goddard Interactive Online 
Visualization and Analysis Infrastructure or “Giovanni” addresses 
these objectives (Acker and Leptoukh, 2007). Giovanni 
(hitD://piovanni.psfc.nasa.gov) has successfully demonstrated its 
utility as an interactive, online, analysis tool for data users to 
facilitate a wide spectrum of users in research, education, and the 
curious internet surfer. 
One of the expressed interests of users worldwide has been to 
combine and fuse data from multiple sensors using Giovanni. 
GES DISC as a significant data archive location is uniquely 
positioned to address this need using data fusion (DF) techniques. 
DF is the intelligent merging or integration of data from multiple 
sources to extract more or better information than would be 
possible from the individual sources. With the vast quantity of 
satellite data sets available from numerous missions and sensors, 
many of which are complementary to each other, there is an 
increasingly critical need to combine to derive the most optimal 
benefits from the data. Often, information provided by an 
individual sensor may be incomplete, inconsistent, inadequate, 
and/or imprecise. Fusing of multi-sensor data, e.g., Aerosol 
Optical Thickness (AOT), can potentially create more consistent, 
reliable, and complete picture of the space-time evolution of the 
underlying geophysical process. Missing data from one sensor 
could be intelligently “filled in” with available co-located data 
from another sensor to produce a better estimate of geophysical 
parameters. 
The work described in this paper is part of the larger effort to 
enable DF in Giovanni. We provide a quick overview of Giovanni 
capabilities with emphasis on our plans for the added DF 
-capability, using data from the Moderate Rcsokution Irnaghg 
Spectroradiometer (MODIS) Terra and Aqua gridded daily mean 
AOT products. Our objective here is on increasing the spatial 
coverage: filling orbital and other gaps through DF and 
incorporating the simplest DF algorithm of the Terra and Aqua 
AOTs. 
2. GIOVANNI 
Giovanni offers a user access, visualization, and analysis tools 
with in a user friendly GUI. With a few mouse clicks, the user can 
easily obtain various remote sensing or model information from 
around the globe. Users become explorers of data interactively and 
online without the overhead of first having to download the data. 
Giovanni also eliminates the need to understand complicated data 
formats before one is able to initiate the intended analysis, Access 
is provided through a common Web browser, so the user does not 
need special applications beyond what is available on a typical 
personal computer. 
From a web page the user is able to select the spatial area “box” 
for the desired region via a Java image map applet or manually 
enter the coordinates defining the bounding box. The user also 
selects the temporal range for the data, one or more parameters 
from this data set, and the desired output type (ASCII or one of 
several plot types). For the plot selections, several color options 
are also available. The user is then able to refine this analysis and 
download the results. ASCII output is useful for GIS or other user 
applications, and the plots generated can be extracted into the 
user’s final report or paper. For more detailed analysis, links to 
the data are available so the user can download the entire data set 
for further local analysis. Depending on the choice of parameters, 
the majority of users will see the online results in a matter of 
seconds while online manipulation of larger amounts of data 
(either spatially or temporally) may take several minutes. Even in 
this more extreme case, the time from the inception of an analysis 
idea to actually seeing the results is drastically reduced and the 
most tedious aspects of the analysis are issues that the user 
bypasses in its entirety! 
The MODIS Online Visualization and Analysis System (MOVAS) 
is an operational instance available through Giovanni since 
September 2003. MOVAS allows scientists and researchers to 
https://ntrs.nasa.gov/search.jsp?R=20070032682 2019-08-30T01:43:36+00:00Z
easily access, visualize and analyze MODIS Level-3 atmospheric 
daily and monthly products helping them for example to 
understand seasonal-to-inter-annual variation of atmospheric 
parameters ranging from aerosol to clouds. MOVAS can provide 
information at every single point and in any rectangular area 
within the data domain, which allows researchers to conduct 
nearly unlimited investigations. 
MOVAS as a Giovanni instance includes advanced features with 
capabilities for performing intercomparison analyses between 
parameters extracted from MODIS instruments onboard two 
different satellites Terra and Aqua, as well as those from the 
Goddard Chemistry Aerosol Radiation and Transport (GOCART) 
model. As such, this instance is an excellent candidate for the 
implementation and fielding of an initial DF capability. 
3. DATA FUSION 
3.1 Data: MODIS Terra and Aqua AOT 
The L&ek-3, gridded daily fnean AOT data used in "this study' 
(Terra and Aqua MODIS Collection 005 Daily Global Gridded 
Products MOD08-D3.005 and MYD08-D3.005) were acquired by 
MODIS onboard the NASA Terra and Aqua satellites. Both 
satellites are polar-orbiting, with Terra on a descending orbit 
(southward) over the equator at about 10.30 local sun time and 
Aqua on an ascending orbit (northward) over the equator at about 
13.30 local sun time. Terra MODIS has been making global 
aerosol measurements since February 2000 and Aqua MODIS 
since July 2002. The daily Level-3 data are space-time aggregated 
from the Level-2 data (nominal resolution of 10 km x 10 km) to a 
1" x 1" resolution (Remer et al., 2005). MODIS uses reflectance 
measurements made in the visible portion of the spectrum to 
retrieve aerosol information. Thus, MODIS aerosol measurements 
are available for daytime only. The daily Level-3 data contain 
statistics derived from the Level-2 atmospheric products. Any 
Level-2, standard 5-minute data file that overlaps any part of a 
data day (0000 to 2400 UTC) is included in the statistics (various 
moments of the AOT distribution, e.g., quality-weighted mean and 
standard deviation, number of counts), which are computed within 
1" x 1" grid boxes on an equal-angle latitude-longitude projection. 
The standard deviation is a measure of the variability of AOT in 
that grid box over that time scale. We used it in the current study 
as a conservative estimate of the AOT variability for the merging 
of data from Terra and Aqua MODIS. 
Giovanni can be used to rapidly and efficiently create and 
visualize daily global 1" x 1" maps of AOT (at 0.55 micron) using 
Terra and Aqua MODIS Level-3 data products. Actually, we used 
Giovanni during the course of this study to identify interesting 
cases for data fision. The typical large gaps (especially near the 
equatorial regions) in the AOT daily mean field for both Terra 
MODIS and Aqua MODIS result from a combination of factors, 
including gaps between swaths from different orbits, and problems 
in AOT retrievals due to sun glint (over water), cloud cover, or 
very bright surfaces like deserts (Hsu et a!., 2004). 
This paper focused on two, 20' latitude by 30" longitude regions, 
each corresponding to a MODIS subset scene. Subsets were used 
because our fusion algorithm (see next section) used Optimal 
Interpolation (01) to fill in the data gaps, and 01 involved the 
inversion of matrices of large dimensions and was, thus, 
computationally expensive to apply to the entire global grid. These 
two scenes (Subsets 1 and 2) were selected for their variations in 
the spatial gradients of the AOT field and in the fractional size and 
distribution of gaps. Subset I (Atlantic Ocean off the coast of 
western Africa) contained mostly regular gaps, whereas Subset 2 
(west-central Africa) contained mostly irregular gaps. 
3.2 Fusion Algorithm 
Our approach to data fusion is (1) to merge the data and then (2) 
interpolate to fill the gaps. This sequence is optimal in the sense 
that it preserves original data information most (least distortion). 
Data Merging. For merging the data sets, we used weighted 
averaging, which is a family of methods based on arithmetic 
combinations of input values, such as linear combinations, 
weighted multiplication or ratios, and maximum likelihood 
estimate (MLE). The MLE emphasizes the use of different sources 
of data using statistics such as mean, standard deviation, and 
number of counts. For isotropic uncertainty, the MLE can provide 
a ,good-appmximatioE- cf the,ac$al estimate of a feature from 
multiple observations. The MLE requires minimal a priori 
information, and it is easy to incorporate user-supplied weights for 
the data sources (Chu and Aggarwal, 1993). For a set of N 
independent observations (Fk )  of the same parameter, the MLE 
estimate is: 
where gk is the variance of the Gaussian noise affecting the 
observations. The is computed for each cell selected for fusion, 
and the expected estimate of F is calculated using Eq. 1. 
SDatial Internolation for Filling Data Gam. For filling data gaps, 
we used the method of Optimal Interpolation (OI), which takes 
into account spatial correlations in the data. The approach was first 
introduced to obtain a more accurate objective analysis of 
meteorological and oceanographic data (Bretherton et al., 1976). It 
is based on the Gauss-Markov theorem, which determines that an 
unbiased estimate of the field of interest is linear in the data and 
has the minimum variance, given the expected value and 
covariance of both the field and the data. The method assumes that 
the observational data are spatially correlated, i.e., data that are 
close to each other are highly Correlated, and also takes into 
account the observation-to-background error variance. It also 
requires a specification of the spatial correlation length. The 
method is "optimal" because, if the correlation function is an 
accurate model of the spatial relationship of the data and if the 
assumption about noise accurately reflects the level of actual noise 
in the data, then the method yields the least expected error of the 
linear estimate. Thus, the data estimate at the analysis point is a 
linear combination of data at the points of availability: 
N 
F; = c w , , F ,  
,= I  
where F' is the estimate at the analysis point j (i=l m, where m 
is the number of  analysis points), 8 is the data value at point i. 
Note that the coefficients w,, are found by solving a linear 
system, which requires inverting the covariance matrix. Thus the 
01 method can be quite time consuming for large data sets. 
J 
3.3 Results 
The advantages of merging the daily mean AOT fields from 
MODIS Terra and Aqua first and then using 0 1  to fill the gaps are 
illustrated in Figures 1 and 2 for, respectively, Subset 1 (mostly 
regular gaps) and Subset 2 (mostly irregular gaps). When the 
original Terra (Figure la) or Aqua (Figure 2a) AOT fields were 
interpolated individually using 01 to obtain a complete field, the 
interpolation errors (Figures IC or 2c and Figures If or 2 0  
associated with the gap regions were large, especially around the 
gaps centers (farthest away from the available measurements). The 
errors decreased towards the gap edges. The advantage of merging 
two data sets is shown in Figures 1 g and 2g by a large reduction in 
the percentage of pixels with no data. Figures 1 h and 2h show the 
AOT fields resulted from the respective merged ones after 
applying the 01 inside the gaps in the merged fields. Compared 
with the interpolation errors of individually interpolated Terra or 
Aqua AOT fields (Figures l e  or If and Figures 2c or 29, the 
errors of the merged and then interpolated AOT fields (Figures l i  
and 2i) were much smaller. 
4. CONCLUSION‘S 
The benefit of first merging data and then interpolating to fill in 
the remaining gaps was demonstrated to account for the variability 
of a measured parameter, compared to interpolating in gaps in 
single datasets and then merging the results. This multi-sensor 
data fusion approach will be incorporated in Giovanni. 
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J. Acker, and G. Leptoukh, “Online analysis enhances use of 
NASA earth science data,” EOS, Transactions, American 
Geophysical Union, vol. 88, p.p. 14-17,2007. 
F.P. Bretherton, R.E. Davies, and C.B. Fandry, “A technique for 
objective analysis and design of oceanographic experiments 
applied to MODE-73,’’ Deep-sea Research, vol. 23, p.p. 559-582, 
1976 
C.C. Chu, and J.K. Aggarwal, “The integration of image 
segmentation map using region and edge information,” IEEE 
Transactions on Pattern Analysis and Machine Intelligence, vol. 
15, #12, P.P. 1241-1252, 1993. 
N.C. Hsu, S-C.Tsay, M.D. King, and J.R. Herman, “Aerosol 
properties over bright-reflecting source regions,” IEEE 
Transactions on Geoscience and Remote Sensing, vol. 42, #3, p.p. 
L.A. Remer, Y.J. Kaufman, -D. Take, S. Mattgo, D.A. Chu, 
J.V.,Martins, R.-R. Li, C. Ichoku, R.C. Levy, R.G. Kleidman, T.F. 
Eck, E. Vermote, and B.N. Holben, “The MODIS aerosol 
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Sciences, vol. 62, p.p. 947-973, 2005. 
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