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Global Biophysical Datasets from NASA Missions Steven W. Running Univ. Of Montana / USA IPCC – GEOSS Workshop Feb 2, 2011. CEOS ECV (Essential Climate Variables) from GCOS – 138, Aug 2010. Albedo Landcover FAPAR LAI Biomass (is NPP better?) Soil Carbon (from satellite?) Fire Disturbance - PowerPoint PPT Presentation
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Global Biophysical Datasetsfrom NASA Missions
Steven W. RunningUniv. Of Montana / USA
IPCC – GEOSS WorkshopFeb 2, 2011
CEOS ECV (Essential Climate Variables)from GCOS – 138, Aug 2010
• Albedo• Landcover• FAPAR• LAI• Biomass (is NPP better?)• Soil Carbon (from satellite?)• Fire Disturbance • Soil Moisture
Thematic standards Reference database (GLC2000)
Comparative validation & assessment
Probability
LANDCOVER INTERCOMPARISON
6
Global Fires for 10 Days
MODIS Annual Disturbance Index
Mildrexler et al 2009
Global Net Primary Production trend (2000-2009)
Zhao & Running 2010, Science
NPP Anomaly compared to Inverted Atm CO2 AnomalyR = 0.81
Global Trend in NPP (1982 – 2009)AVHRR + MODIS with EOS algorithm
Consistency between MODIS NDVI and NPP (1982-2009)
Zhao and Running 2010
Non-Frozen Season Trend (1979-2008)Non-Frozen Season Trend (1979-2008)(SSM/I)(SSM/I)
Mean Northern Hemisphere trend
Days yr-1
Multi-Year Trend in Estimated Mean Annual ET and P-ET (1983-2006Multi-Year Trend in Estimated Mean Annual ET and P-ET (1983-2006) )
ET
P-ET
~73% of the global domain shows a positive ET trend;
BUT
~51% of the domain shows a negative water balance (P-ET) trend.
Global Annual MaximumMODIS Radiometric Surface Temp
Global Flux Tower Network
AERONET
• Aerosol Optical Properties Research & Enabling Project• Program of long term systematic network measurements
• Mission Objectives• Validation of Satellite Aerosol Retrievals • Characterization of aerosol optical properties• Synergism with Satellite obs., Climate Models
Expanding to in situ Ocean Color & possibly total column CO2
Aerosol Robotic Network
18
Missions in Formulation and Implementation – 12/2010
OCO-22/2013
Global CO2
GLORY2/2011
Aerosols, TSI
NPP10/2011
w/NOAA, DoDEOS cont., Op Met.
AQUARIUS6/2011
w/CONAE; SSS
LDCM12/2012
w/USGS; TIRS
GPM7/2013, 11/2014w/ JAXA; Precip
SMAP11/2014
w/CSASoil Moist., Frz/Thaw
ICESat-II10/2015
Ice Dynamics
Unsustainable groundwater withdrawal Depletion rate 4cm/yr
Groundwater withdrawals as % of recharge, 2002-2008. Rodell et al Nature 2009
20
SMAP Science Objectives
Landscape Water Content
Surf
ace
Res
ista
nce
Thawed
Frozen
High
High
LowLow
Snow Accumulation
Freeze - Thawcycles
Landscape Water Content
Surf
ace
Res
ista
nce
Thawed
Frozen
High
High
LowLow
Snow Accumulation
Freeze - Thawcycles
Freeze - Thawcycles
SMAP measurements of soil moisture and freeze-thaw cycles will provide an integrated measure of critical controls on surface water mobility and associated constraints to ecosystem processes.
Soil moisture and freeze/thaw state are primary environmental controls on water mobility and associated constraints to evaporation and Net Primary Productivity
Julian Day
Mean Thaw Date (SSM/I, 1988-2001)
Dry Spring Soil Moisture Wet Spring Soil Moisture
Summer Air Temperature Anomaly [ºC]
OCO Measuring CO2 from Space• Retrieve variations in the
column averaged CO2 dry air mole fraction, XCO2 over sunlit hemisphere
• Collect NIR spectra of CO2 and O2 absorption in reflected sunlight
• Validate measurements to ensure XCO2 accuracy of 1 - 2 ppm (0.3 - 0.5%)
Flask
Aircraft
FTS
OCO/AIRS/GOSAT
Tower
Initial Surf/Atm
State
Generate Synthetic Spectrum
Instrument Model
Difference Spectra
Inverse Model
New State (inc. XCO2)
XCO2
DESDynI Radar and Lidar Capabilities for Biomass and Aboveground Carbon Storage
22
Upland coniferLowland coniferNorthern hardwoodsAspen/lowland deciduousGrasslandAgricultureWetlandsOpen waterUrban/barren
Vegetation Type
Multi-beam Lidar – accurate biomass and canopy profiles (along-track) at 25 m resolution, extend spatially with radar
Vegetation 3D Structure &
Biomass: Radar and Lidar
L-band Radar – high resolution mapping of low forest biomass and disturbance, extend sensitivity with lidar
High: 30 kg/m2
Biomass
Low: 0 kg/m2
Terrestrial Carbon Storage and Changes
NRC Decadal Survey HyspIRI Visible ShortWave InfraRed (VSWIR) Imaging Spectrometer
+ Multispectral Thermal InfraRed (TIR) Scanner
VSWIR: Plant Physiology and Function Types (PPFT)
Multispectral TIR Scanner
Red tide algal bloom in Monterey Bay, CA
Map of dominant tree species, Bartlett Forest, NH
Linkages between International Programs concerned with Terrestrial Earth Observation
24
LPV Objective & Goals
To foster and coordinate quantitative validation of higher level global land products derived from remotely sensed data, in a
traceable way, and to relay results so they are relevant to users
•To increase the quality and efficiency of global satellite product validation by developing and promoting international standards and protocols for:
– Field sampling– Scaling techniques– Accuracy reporting– Data / information exchange
•To provide feedback to international structures (GEOSS) for:– Requirements on product accuracy and quality assurance (QA4EO)– Terrestrial ECV measurement standards – Definitions for future missions
25
Focus Groups
26
Focus Group North America Europe / Other Listserv
Land Cover * Mark Friedl (Boston University)
Martin Herold(Wageningen University, NL)
137
Fire*(Active/Burned Area)
Luigi Boschetti (University of Maryland)
Kevin Tansey(University of Leicester, UK)
73
Biophysical(LAI*, APAR*)
Richard Fernandes (NR Canada)
Stephen Plummer(Harwell, UK)
72
Surface Radiation(Reflectance, BRDF, Albedo*, Snow/Ice*)
Crystal Schaaf (Boston University)
Gabriela Schaepman(University of Zurich, SW)
41
Land Surface Temperature
Simon Hook (NASA JPL)
Jose Sobrino(University of Valencia, SP)
65
Soil Moisture* Tom Jackson (USDA)
Wolfgang Wagner(Vienna Uni of Technology, AT)
48
Land Surface Phenology
Jeff Morisette (USGS)
Jadu Dash (University of Southampton, UK)
76
* ECV
KEY FINDINGS
• Many global datasets now exist• Need validation and intercomparison amongst
sensors• Need to unify formats, gridding, units • Coordinate data distribution• Continuity to new sensors
EXTRA SLIDES
IPCC WORKING GROUP II IMPACTS SUMMARY 2007
CO2 Emissions from Land Use Change
Friedlingstein et al. 2010, Nature Geoscience; Data: RA Houghton, GFRA 2010
CO2 e
miss
ions
(PgC
y-1)
1990s Emissions: 1.5±0.7 PgC
2000-2005Emissions: 1.3±0.7 PgC
2006-2010:Emissions: 0.9±0.7 PgC
CO2 em
issions (PgCO2 y
-1)
Fire Emissions from Deforestation ZonesFi
re E
miss
ions
from
de
fore
stat
ion
zone
s (T
g C
y-1)
Global Fire Emissions Database (GFED) version 3.1
Year
van der Werf et al. 2010, Atmospheric Chemistry and Physics Discussions
0
200
400
600
800
1000
1200
1400
1997 99 01 2003 05 07 2009
AmericaAfricaAsiaPan-tropics
Modelled Natural CO2 Sinks
Updated from Le Quéré et al. 2009, Nature Geoscience
Land
sin
k (P
gCy-1
)
5 m
odel
s
Oce
an s
ink
(PgC
y-1)
4
mod
els
Time (y)
1960 20101970 1990 20001980
0
2
-2
-4
-6
1960 20101970 1990 20001980
0
2
-2
-4
-6
34
NASA Operating Missions (International Collaboration)
35
Future Orbital Flight Missions – 2010 – 2022
(International contributions)
Gravity Recovery & Gravity Recovery & Climate ExperimentClimate Experiment
500 km orbit500 km orbit
220 km separation220 km separation
Distance accuracy Distance accuracy 0.001 mm0.001 mm
Focus Group Responsibilities
• Engage community members (via listserv/website)
• Update on progress, relevant meetings
• Report back to LPV group on activities, meetings, new products,
potential funding mechanisms
• Organize at least 1 topical workshop within leadership term
• Expand LPV activities, field sites, collaboration globally
• Lead product inter-comparison activities
• Lead the development and writing of “best practice” land
product validation protocols
• Define product error definitions for ECV’s, LTDR’s for the climate
modeling community37
Global Water Availability Risk
Vorasmairty et al Nature 2010