Global Biophysical Datasets from NASA Missions Steven W. Running Univ. Of Montana / USA IPCC – GEOSS Workshop Feb 2, 2011.

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

Su

rfa

ce R

esis

tan

ce

Thawed

Frozen

High

High

LowLow

Snow Accumulation

Freeze - Thawcycles

Landscape Water Content

Su

rfa

ce R

esis

tan

ce

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