Environmental Science from Satellites

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Environmental Sciencefrom Satellites

• Jeff Dozier, UCSB

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Radiation principles
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Atmospheric absorption of solar and infrared
radiation
NASA Goddard Institute for Space Studies
http//www.giss.nasa.gov
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Measurement scale constrained by physics and
technology (and money)

• Spatial resolution (IFOV/GSD) and coverage
  (field-of-view/regard)
• Optical diffraction sets minimum aperture size
• Spectral resolution (Dl) and coverage (lmin to
  lmax)
• Narrow bands need bigger aperture, more
  detectors, longer integration time
• Radiometric resolution (S/N, NEDr, NEDT) and
  coverage (dynamic range)
• Aperture size, detector size, number of
  detectors, integration time
• Temporal resolution (revisit) and coverage
  (repeat)
• Pointing agility, period for full coverage

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Spatial, spectral characteristics of some
multispectral sensors
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Aircraft hyperspectral data (AVIRIS)
0.4 ?m
?
2.5 ?m
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Data rate
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Gulf Stream temperatures from MODIS, May 2, 2001
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SeaWiFS imageglobal chlorophyll July 1997 - Sept
1998
Provided by the SeaWiFS Project, NASA/Goddard
Space Flight Center and ORBIMAGE
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Fractional snow cover, Sierra Nevada, March 7
2004
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Surface elevation from interferometric radar
(full resolution image is available from the JPL
SRTM image website)
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Applications snowmelt modeling(Molotch et al.,
GRL, 2004)
Snow Covered Area
net radiation gt 0
degree days gt 0
Where mq Energy to water depth conversion,
0.026 cm W-1 m2 day-1
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Surface wetness with AVIRIS, Mt. Rainier, 6/14/96
AVIRIS image, 409, 1324, 2269 nm
precipitable water, 1-8 mm
liquid water, 0-5 mm path absorption
vapor, liquid, ice (BGR)
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Information Lifecycle
Collect

• A
• Use
• Present
• B
• Collect
• Retrieve
• C
• Store
• Search

Store
Present
Search
Use
Retrieve
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2005 status

• Universal connectivity (except Africa, South
  America)
• Internet
• Web
• Comprehensive analysis environments
• GIS, image analysis (ArcGIS, ENVI)
• Matrix manipulation (IDL, MATLAB, )
• Standard formats
• Metadata (FGDC, )
• Data (XML, GeoTIFF, )

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2005 report card

• A
• Do complex analyses, many with off-the-shelf
  tools
• B
• Retrieve/publish data from/to the Web
• C
• Cant find somebody elses data / they cant find
  yours
• How good is your web site?

• Cant remember what you did / how you did it
• How good are your notes?
• Your conclusions are in the archival
  literaturewhere are your data?
• How good are your backups?

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Major Missing Pieces

• Local
• Storage management
• Everything onlineand accessible
• Metadata management
• Everything documented and findable
• Everything connected to where it came from

• Global
• Federation
• Distributed data centers look like a single
  system
• Service management
• Discover, query, describe, and retrieve
  information
• Namespace management
• Same names for same things

? Digital libraries
? Personal data centers
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Earth System Science Workbench conceptual model

• (ESIPEarth Science Information Partners)
• Experiment
• Network of models
• ingesting / synthesizing data
• generating products
• Notebook
• Persistent storage that can be queried
• Keeps track of all experiments
• Laboratory
• Experiment execution environment

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ESSW Software

• Lab Notebook
• Client API (Perl library)
• called by wrappers
• Daemon
• accepts API commands
• creates XML documents
• sends docs to Database
• Database
• XML metadata
• DTDs
• documents
• tabular metadata
• subset of XML
• lineage links

• ND-WORM filesystem(No Duplicate - Write Once
  Read Many)
• File digest
• digital signature
• Dedicated disk storage area
• actual filename digest
• Same digest same file, dont save twice
• Database
• file metadata
• virtual filename(s)

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Combining sensors sea-surface temperature and
height
HRPT Data Handling
HRPT Gridding
calibrate, de-cloud, aggregate
TeraScan
extrapolate
HRPT downlink
SST grid
SST super data
AVHRR image
GOAL Local and advective components of upper
ocean heat balance
ocean height grid
TOPEX Gridding
TOPEX Data Handling
ocean height super data
TOPEX points
calibrate, aggregate
MGDR cracker
extrapolate
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TOPEX/Poseidon view of El Niño / La Niña,
1997-1998
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Database server
Lab Notebook
Client
LN _Database
LN_Client API
Client machine
ND-WORM
Lab Notebook
_Client
Server
queries, XML
documents
metadata values
JDBC/ODBC
Perl
scripts
LN_API
XML DTDs, metadata
templates
Application server
ND-WORM storage
instructions
LN_Console
LN_Daemon
ND-WORM
Application server
_Database
ND-WORM
_Server
ND-WORM services
experiment files
ND-WORM disk
storage area
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avhrr_L0
AVHRR Level 0 product
AVHRR telemetry ingest
avhrr_ingest
Hand navigation details
avhrr_l1b
AHVRR Level 1B
product
Hand navigation
avhrr_
procedure
handNav
avhrr_
AVHRR Level 1B
navd_l1b
navigated
Multi-channel
sea surface
avhrr_
temperature
sstModel
algorithm
avhrr_sst
Sea surface temperature (SST)
Copy
avhrr_
navigated
copyNav
image
avhrr_
navd_sst
SST navigated
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SST experiment wrapper   L1B is the input
Level 1B AVHRR image file SST is the output
SST image file   run legacy command "nitpix"
creates SST image from L1B image   base_temp
5.0 temp_step 0.1 ...   system("nitpix
base_tempbase_temp temp_steptemp_step ...
L1B SST")   start recording ESSW
metadata   beginXMLBld(ENVUSER,
"PRODUCTION")   get metadata for input
file   L1B_ID findSciObjFromFile(L1B)
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create metadata for SST experiment   exp
createExperimentMetadata("avhrr_sstModel") exp_s
tep createExpStepMetadata(exp,
"avhrr_sstExpStp")   addValue(exp_step,
"avhrr_sstExpStp.base_temp", base_temp) addValue
(exp_step, "avhrr_sstExpStp.temp_step",
temp_step) ...   saveToDB(exp_step,
"avhrr_sstExpStp") closeMetadata(exp_step)
connect input and output images to
experiment   registerExperimentInputs(exp,
L1B_ID) registerExperimentOutputs(exp,
SST_ID)   finish recording ESSW
metadata   endXMLBld()
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Global Names Hierarchical

• General form server/authority/name
• Server interprets names
• Knows how to contact authority
• If web server then name is a URL
• Authority assigns names
• Must be well-known
• Name unique within authority
• Can include any semantics the Authority wants
• (based on Kunzes ARK proposal)

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Global Name Example MODster

• MODIS granules have standard names
• E.g. MOD03.A2001001.1550.002.2001017185332
• Create a NASA global name authority
• E.g. NASA/MODIS
• Set up a global name server
• E.g. http//globalnames.esipfed.org
• If you have a MODIS granule,tell name server
  where (e.g. URL) it lives
• Name server redirects
• http//globalnames.esipfed.org/NASA/MODIS/MOD
• to your URL

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Recommendation Demonstrator Data Centers

• XML Schema with standard formats definitions
• Multiple avenues of real-time network access to
  data
• Enhanced and publish metadata
• Subsetting tools that run on host
• Commodity-level hardware and software where
  possible
• Access statistics and user feedback

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Conclusion If This All Works

• Personal data centers (enabled by, e.g., ESSW)
  will dramatically improve local information
  management
• Digital libraries (enabled by, e.g., ADEPT) will
  dramatically improve global information
  management
• Add to
• Your tools
• the Web
• and get Everything, All The Time