IEEE Geoscience and Remote Sensing Magazine - December 2015 - 16

LP DAAC
Domain

User Domain

Data
Discovery

User
N
LA

Data
Request

WAN
LP DAAC Archive
User Download
Get Granules
Internet
1,000's of Files
10's GB

Graphical
Representation
of Data

LAN
User Workstation
User Processing
Un-Package, Ingest
Process, Export
1,000's of Files
10's of GB

User Post-Processing
Data Exploration
Statistical Analysis
1's of Files
100's of KB

User Processing
Sub-Set
Data Extraction
100's of Files
100's of MB

Data Download Processing Model
Hours, Days, Weeks

FiguRe 9. Traditional Model for Data Download, Processing and Analysis Workflows.

Built-in AppEEARS visualizations allow users to view
and interact with returned data from sample requests before download. Figure 8 displays time-series plots of Normalized Difference Vegetation Index (1 kilometer spatial
resolution, 16 day composite) from the MOD13A2 product
for a single pixel location spanning years 2005 to 2015. The
geographic location is one of many flux tower locations
used as MODIS satellite data calibration sites. The sites provide continuous measurement of local atmospheric variables and physical parameters used for ground-based comparison with MODIS measurements. These flux towers are
stationary points where site characteristics are recorded on
a regular basis, providing information specific to a single
ecosystem type or condition.
When a sample request is processed and completed, the
user is then able to view data visualizations within the
AppEEARS interface (Figure 8). The interactive visualizations give users the ability to explore and interrogate requested data before downloading. This ability allows the
user to: determine if data are present for their particular
geographic points of interest; determine if the data meet
their expectations; identify potential trends in the data;
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and identify possible correlations between variables. If the
returned data are satisfactory, users may choose to download for more rigorous analysis. In these cases, users will
receive a compressed ZIP file containing all of the requested data in CSV format.
6. AnAlysis ReAdy dAtA
Ideally, a user should be able to select the data set they
are interested in, and choose the products or information
they would like to see, without knowledge of the middleware services necessary to access, reduce, aggregate, summarize, or visualize the data - a data processing workflow. Today's users want to retrieve results, information,
and answers to their science questions, not large quantities of extraneous raw data to be discarded after they are
"processed". This concept requires "Analysis Ready Data"
to be on-line and available to services that can translate
the raw archive data into a user's request for information.
For the MODIS science use case examples described in
Section 6, creating access to additional data attributes
such as time series aggregations is a characteristic of
"Analysis Ready Data".
ieee Geoscience and remote sensing magazine

december 2015

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2015