US Remote Sensing Capabilities

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US Remote Sensing Capabilities

• Chris Justice and John Townshend

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NASAs Earth Observing System Related
Satellites
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Next Generation Missions
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Relevance to Land of US assets

• In fact relatively small number of assets
  directly relevant to land
• MODIS
• VIIRS
• Landsat
• Also we need a VCL type instrument for the
  vertical dimension in vegetation
• Just possibly may be revived.
• Plus we need regular very high resolution data
  for scaling and validation which potentially
  could be provided by US commercial satellites

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VIIRS EDR Priorities Performance
Imagery (with four
ARRs
)
IA
Sea Surface Temp
Aerosol Optical Thickness
Aerosol Particle Size
Suspended Matter
Cloud Cover/Layers
Cloud Effective Particle Size
Cloud Optical Thickness
Cloud Top Height
Cloud Top Pressure
Cloud Top Temperature
IIA
Albedo
Land Surface Temperature
Vegetation Index
Snow Cover/Depth
Surface Type (ST)l
Fresh Water Ice(Sea Ice ARR)
Visible/IR Imager Radiometer Suite
Ice Surface Temperature
Ocean Color/Chlorophyll
Sea Ice Characterization
Active Fires (ST ARR)
IIB
Precipitable
Water
Cloud Base Height
IIIB
Net Heat Flux
Soil Moisture
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AVHRR-VIIRS Transition Schedule
CY
99
00
11
12
13
14
15
16
17
18
03
08
09
10
01
02
07
04
05
06
NPOESS
C1
VIIRS
0930 - 1030
AVHRR
METOP-AVHRR
1030
NPP
EOS-Terra
1330
VIIRS
AVHRR
Local Equatorial Crossing Time
S/C Deliveries
S/C delivery interval driven by 15 month IAT
schedule
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Last Modified Dec 1, 2001
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Landsat data

• Landsat class data with the Landsat 7 acquisition
  strategy (LTAP) has been highly successful in
  satisfying multiple user needs
• GLCF has regular downloads of 25,000 scenes per
  month.
• But Landsat now has major problems.

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Landsat 7 has significant problems for change
detection.

• Scan Line Corrector failure means that only the
  central 28 kms has no missing data.
• In fact this still means that 78 of the data is
  collected.
• But the 22 not collected is NOT the same 22 on
  each image and hence change detection is
  significantly compromised.

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Landsat 7 fixes

• 1. Enhanced SLC-off Browse Image
• The Landsat 7 browse image displayed on all data
  ordering interfaces has been modified to allow
  users to estimate the width of potential SLC-off
  scan gaps over their area of interest.
• 2. User-Selected Interpolation
• Users will have the ability to select the number
  of pixels that are interpolated across the data
  gaps during Level 1G processing. This will allow
  potential production of a fully populated image
  when specified by the user.
• 3. SLC-off Data Available through NLAPS
• Users will have the option to purchase National
  Landsat Archive Production System (NLAPS)
  processing if desired.
• 4. Gap-filled product - Phase 1 (SLC-off / SLC-on
  Merge)
• An initial (Phase 1) gap-filled image product
  will be generated by replacing the missing data
  of an SLC-off scene with pixel values derived
  from a coregistered, histogram-matched SLC-on
  scene. product.
• 5. Gap-Filled Product - Phase 2 (SLC-off /
  SLC-off Merge)
• A second (Phase 2) gap-filled image product will
  be generated from the merge of two or more
  SLC-off scenes to produce a single image product.
  
• 6. Inclusion of band-specific Gap Mask

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Can Landsat be replaced with existing assets?

• SPOT HRV and IRS can provide data with ground
  receiving capability.
• Some efforts already to do this, though very slow
  response from the US.
• Unclear if resultant products are truly
  interoperable, but probably acceptable.
• But far from global coverage and the quality of
  acquisition strategy will be below that of
  Landsat.

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IRS-1C/1D LISS-3 data archive of RD Center
ScanEx (February, 2004)
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Availability of the historical record

• Almost complete global coverage for the early
  90s and 2000 available through NASA/Earthsats
  Geocover initiative
• Available on-line through the GLCF and through
  TRFIC
• Made available to countries (though UNEP and FAO)
• Very valuable data set but with significant
  limitations.
• Timing of acquisition varies substantially
• Varying phenology hinders change detection

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What is the quality of the data analysis for
southern Africa of the quality of FAO proposed 1
degree sample 10km squares
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Analysis of suitability of GeoCover images for
1990 for Southern Africa by GLCF

• Total Number 308
• Fully acceptable 180
• Restricted value due to cloud etc 89
• Unacceptable 39
• Hence historical record is less satisfactory than
  at first appears

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What can we learn from these issues?

• We do not have the final solution.
• We need an operational fine resolution (20-50m)
  land observing system
• one that is guaranteed in the long-term
• POLO Polar Orbiting Land Observer
• Possible platform - NPOESS Lite
• Having the assets in orbit is not sufficient an
  excellent acquisition strategy is vital
• Avoid moving parts if at all possible.

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Enhancements to ETM are needed.

• LDCM Science Team for Resource 21made the
  following recommendations
• Add bands especially a Cirrus band (1380nm
  Goetz, Gao et al)
• Alter bandwidths (lessons learnt from MODIS)
• 10nm reduction (to 680 nm) in upper bound of red
  band
• Narrow and reposition NIR band to avoid water
  vapor,
• Narrowing and repositioning of SWIR2 to the
  1560nm to 1660 nm region to avoid water vapor
  attenuation.
• Shift the SWIR3 to the 2100 nm to 2300 nm region
  to reduce water vapor absorption impact.
• Improve the MTF (e.g., average 9x10m bands)
• Improve frequency of acquisition (e.g. to 2
  days).

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Conciliating spatial and temporal resolutions
towards an operational concept for land
environment argues for more frequent
observations at Landsat/SPOT resolutions
Landsat
SPOT 5
SPOT
ERS
Pléiades
MERIS
MODIS
POLDER
VGT
MSG
 Gap 
10-20 m spatial resolution 8-12 spectral bands2
days revisit Full and operational observation of
continents
Source H. Jeanjean
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Recent developments

• US has at last accepted that a Landsat class
  capability be regarded as an operational
  necessity.
• Proposal in recent RFI is to place an ETM like
  instrument on NPOESS.
• But this could be 2009 or later. Hence
  possibility of a major gap.
• May be a earlier launch but resources may not be
  available.
• International effort should be launched to use
  existing international assets to satisfy LTAP.
• Frequency would drop to once every 17 days.
• Suggested enhancements
• Significantly widen swath (2-3 times)
• Additional low cost free-flyers (Surrey Satellite
  model?) with possibly simpler sensors to improve
  temporal resolution and as operational back-ups.
• Need reflectance products not DNs.
• Also orthorectified products.

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Extra slides

• One on a new crop data set though coarse
  resolution
• Two extra JAXA slides if you need them

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Global Distribution of Wheat
Global Data Set of 18 Major Crops
Derived by merging remotely-sensed global land
cover data with crop census data.

• Leff, B., N. Ramankutty, and J. Foley, Geographic
  distribution of major crops across the world,
  Global Biogeochemical Cycles, 18, GB1009, 2004.
• Center for Sustainability and the Global
  Environment Nelson Institute for Environmental
  Studies University of Wisconsin-Madison

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GRFM/GBFM Data Sets

• SEA-1 (2 CD set)
• Mainland South-East Asia
• Dual-season (Dry/rainy)
• Mosaicking SAR processing by NASDA

AFR-1 (3 CD set) West Central Africa and
Madagascar Dual-season (Low water/high
water) Mosaicking by JRC SAR processing by NASDA
AM-1 (4 CD set) South America/Amazon Dual-season
(Low water/high water) Mosaicking by JPL SAR
processing by ASF NASDA
NA-1 (DVD) Boreal North America Dual-season
(Summer/winter) Mosaicking by JPL SAR processing
by ASF
AM-3 (2 CD set) Central America/Pantanal Mosaickin
g by JPL SAR processing by ASF NASDA
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New Data Sets in the pipeline

• SEA-2
• Insular South-East Asia
• (Kalimantan, Java, Sumatra, Sulawesi)
• Multi-annual (1994/1996/1998)
• Mosaicking SAR processing JAXA
• Target April 2004

SEA-3 Insular South-East Asia (Philippines, New
Guinea) Single season Mosaicking SAR processing
by JAXA TargetJune 2004
AU-1 Australia Single season Mosaicking SAR
processing by JAXA Target JFY 2004
AFR-2 Southern Africa Single season Mosaicking by
JPL SAR processing by JAXA
China Single season Mosaicking SAR processing
by JAXA Target JFY 2004
India Single season Mosaicking SAR processing
by JAXA Target JFY 2004
AM-4 Southern South America Single
season Mosaicking by JPL SAR processing by JAXA