Environmental Sciencefrom Satellites

Jeff Dozier, UCSB

Radiation principles

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.E+08

0.1 1 10 100wavelength (µm)

rad

ian

ce (

Wm

-2µ

m-1

sr-1

)

Sun (5800K)

Scaled for Earth-Sun distance

Earth (288K)

Atmospheric absorption of solar and infrared radiation

NASA Goddard Institute for Space Studies http://www.giss.nasa.gov

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 () and coverage (min to max)– Narrow bands need bigger aperture, more detectors, longer

integration time

• Radiometric resolution (S/N, NE, NET) 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

1.0 100.4 1.5 153 5 8 120.5 0.80.6 4 6

1

10

100

1000

20

50

200

500

2

5

0.5

wavelength, m

IFO

V,

m

DigitalGlobe(Quickbird)

SPOT (Panchromatic)

SPOT (Multispectral)

Landsat (Panchromatic)ASTER (Multispectral)

Landsat (Multispectral) ASTER (Multispectral)Landsat,

ASTER (Thermal)

Landsat (Thermal)

MODIS (Visible through Infrared)

AVHRR

Aircraft hyperspectral data (AVIRIS)

0.4 m

2.5 m

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Data rate

2

2

2

bitsswath width velocity # bands band data rate(spatial resolution)

bitskmkm bandssec band Mbitssecm

e.g., Landsat 5

bitskm185 km 7.4 7 bands 8 sec band Mbits85 sec30 m

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Gulf Stream temperatures from MODIS, May 2, 2001

SeaWiFS image—global 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

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 > 0 degree days > 0

Where:

mq = Energy to water depth conversion, 0.026 cm W-1 m2 day-1

*2 2 *

0 0

0

10.622 0.622(ln / )

2 2a

r q h p aa

RHL de La m C k z z u C RH e

dt T

Melt Flux net q d rR m T a SCA

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

• A– Use

– Present

• B– Collect

– Retrieve

• C– Store

– Search

Collect

Store

Search

Retrieve

Use

Present

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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

– Can’t find somebody else’s data / they can’t find yours

» How good is your web site?

– Can’t remember what you did / how you did it

» How good are your notes?

– Your conclusions are in the archival literature;where 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

(ESIP—Earth 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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SST

tu SST

Combining sensors: sea-surface temperature and height

CD-ROM

TeraScan

calibrate,de-cloud,aggregate

extrapolate

SST gridSST “super data”AVHRR imageHRPT downlink

extrapolatecalibrate,aggregate

MGDRcracker

ocean height“super data”

ocean heightgrid

TOPEX points

GOAL:Local and advective components of upper ocean heat balance:

HRPT Data Handling HRPT Gridding

TOPEXGridding

TOPEXData Handling

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avhrr_handNav

AVHRR telemetry ingest

AHVRR Level 1Bproduct

AVHRR Level 1B:navigatedMulti-channel

sea surfacetemperaturealgorithm

AVHRR Level 0 product

avhrr_copyNav

Hand navigation details

Sea surface temperature (SST)

Copynavigatedimage

SST: navigated

avhrr_sstModel

avhrr_navd_sst

Hand navigationprocedure

avhrr_ingest

avhrr_navd_l1b

avhrr_L0

avhrr_l1b

avhrr_sst

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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_temp=$base_temp temp_step=$temp_step ... $L1B $SST"); # start recording ESSW metadata beginXMLBld($ENV{USER}, "PRODUCTION"); # get metadata for input file $L1B_ID = findSciObjFromFile($L1B);

AHVRR Level 1Bproduct

Multi-channelsea surfacetemperaturealgorithm

Sea surfacetemperature

(SST)

avhrr_sstModel

avhrr_l1b

avhrr_sst

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# create metadata for SST experiment $exp = createExperimentMetadata("avhrr_sstModel");$exp_step = 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();

AHVRR Level 1Bproduct

Multi-channelsea surfacetemperaturealgorithm

Sea surfacetemperature

(SST)

avhrr_sstModel

avhrr_l1b

avhrr_sst

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