SMAP Science Objectives

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Characterization of Land Surface Freeze/Thaw State, Temperature and Moisture Controls on Ecosystem Productivity:

Carbon Cycle Science Addressed with NASA’s Proposed Soil Moisture Active/Passive (SMAP) Mission

Kyle C. McDonaldDepartment of Earth and Atmospheric Sciences

The City College of New York, New York, NY, USAand

Jet Propulsion Lab, California Institute of TechnologyPasadena, California, USA

John S. KimballUniversity of Montana

Missoula, Montana, USA

International Geoscience and Remote Sensing SymposiumJuly 25-29, 2011, Vancouver, BC, Canada

Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology under contract to the National Aeronautics and Space

Administration. This work has been undertaken in part within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data were provided by JAXA EORC.

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SMAP Science ObjectivesSMAP Science Objectives

Primary Science Objectives:

• Global, high-resolution mapping of soil moisture and its freeze/thaw state to: Link terrestrial water, energy and carbon

cycle processes

Estimate global water and energy fluxes at the land surface

Quantify net carbon flux in boreal landscapes

Extend weather and climate forecast skill

Develop improved flood and drought prediction capability

Soil moisture and freeze/thaw state are primary surface controls on Evaporation and Net Primary Productivity

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Conceptual relationship between landscape water content and associated environmental constraints to ecosystem processes including land-atmosphere carbon, water and energy exchange and vegetation productivity. The SMAP mission will provide a direct measure of changes in landscape water content and freeze/thaw status for monitoring terrestrial water mobility controls on ecosystem processes.

Terrestrial Water Mobility Constraints to Terrestrial Water Mobility Constraints to Ecosystem ProcessesEcosystem Processes

Landscape Water Content

Su

rfac

e R

esi

sta

nce

Thawed

Frozen

High

High

LowLow

Snow Accumulation

Increasing Biological Constraints

Freeze - Thawcycles

Landscape Water Content

Su

rfac

e R

esi

sta

nce

Thawed

Frozen

High

High

LowLow

Snow Accumulation

Increasing Biological Constraints

Freeze - Thawcycles

Freeze - Thawcycles

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““Link Terrestrial Water, Energy and Carbon Link Terrestrial Water, Energy and Carbon Cycle Processes”Cycle Processes”

Do Climate Models Correctly Represent the Landsurface Control on Water and Energy Fluxes?

What Are the Regional Water Cycle Impacts of Climate Variability?

Landscape Freeze/Thaw Dynamics Constrain Boreal Carbon Balance[The Missing Carbon Sink Problem].

Water and Energy Cycle

Soil Moisture Controls the Rate of Continental Water and Cycles

Carbon Cycle

Are Northern Land Masses Sources or Sinks for Atmospheric Carbon?

Surface Soil Moisture [% Volume] Measured by L-Band Radiometer

Campbell Yolo Clay Field Experiment Site, California

Soi

l Eva

pora

tion

Nor

mal

ized

by

Pot

entia

l Eva

pora

tion

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SMAP Measurement ApproachSMAP Measurement Approach

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• L-band radiometer provides coarse-resolution (40 km) high absolute accuracy soil moisture measurements for climate modeling and prediction

SMAP Mission UniquenessSMAP Mission Uniqueness

SMAP is the first L-band combined active/passive mission providing both high-resolution and frequent revisit observations

• L-band radar provides high resolution (1-3 km) observations at spatial scales necessary to accurately measure freeze/thaw transitions in boreal landscapes

• Combined radar-radiometer soil moisture product at intermediate (10 km) resolution provides high resolution and high absolute accuracy for hydrometeorology and weather prediction

• Frequent global revisit (~3 days, 1-2 days for boreal regions) at high spatial resolution (1-10 km) enables several critical applications in water balance monitoring, basin-scale hydrologic prediction, flood monitoring and prediction, and human health

Comparison of SMAP coverage with other L-band missions

SMAP is the only microwave mission providing consistently high resolution and frequent revisits for the global land area

Range bars show the maximum and minimum parameters for the corresponding mission.

SAR missions do not allow for complete global coverage.

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Normal to late thaw& Carbon Source

[1995, 1996, 1997]

Source: Goulden et al. Science, 279.

Early thaw & Carbon Sink

[1998]

Spring thaw dates5/7 5/27 5/26 4/22

Primary thaw dates

Ecological Significance of the F/T SignalEcological Significance of the F/T Signal

Seasonal frozen temperatures constrain vegetation growth and land-atmosphere CO2 exchange for ~52% (66 million km2) of the global land area. Spring thaw signal coincides with growing season initiation and influences land boreal source/sink strength for atmospheric CO2.

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R2 = 0.745; P < 0.0001

y = -4.0926x

-80

-60

-40

-20

0

20

40

60

80

-15 -10 -5 0 5 10 15

Spring thaw anomaly (days)

NP

P a

no

ma

ly (

g C

m-2

yr-1

)

Mean annual variability in springtime thaw is on the order of ±7 days, with corresponding impacts to annual net primary productivity (NPP) of approximately ±1% per day.

Day of Primary Thaw

45N

75N

180W120W

150W

60N

Day of Primary Thaw

45N

75N

180W120W

150W

60N

Spring Thaw vs Northern Vegetation Productivity AnomaliesSpring Thaw vs Northern Vegetation Productivity Anomalies

NPP (g C m-2 yr-1)NPP (g C m-2 yr-1)

Mean Annual NPP(1988-2000)

Mean Primary Thaw Date (SSM/I, 1988-2000)

Mean Annual NPP (AVHRR, 1988-2000)

Early thaw (- sign) promotes larger (+) NPPLater thaw (+ sign) promotes lower (-) NPP

Source: Kimball et al., Earth Interactions 10 (21)

AK Regional Correspondence Between SSM/I Thaw Date and Annual NPP

9

-8

-6

-4

-2

0

2

4

6

8

1988 1990 1992 1994 1996 1998 2000

Th

aw

An

om

aly

(d

ay

s)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Atm

. CO

2 a

no

ma

ly (

pp

m)

Spring Thaw Timing (SSM/I) Max. Annual CO2 drawdown

Freeze/thaw link to carbon source-sink activity: Early thaw years enhance growing season uptake (drawdown) of atmospheric CO2 by NPP; Later thaw years reduce NPP and CO2 drawdown.

NOAA CMDL Observatory at Barrow

Julian Day

Mean Thaw Date (SSM/I, 1988-2001)

R = 0.63, p = 0.015

Spring Thaw Regulates Boreal-Arctic Spring Thaw Regulates Boreal-Arctic Sequestration of Atmospheric COSequestration of Atmospheric CO22

Earlier thaw & larger CO2 drawdown (- sign)Later thaw & smaller CO2 drawdown (+ sign)

Source: McDonald et al., Earth Interactions 8(20)

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Define F/T Affected RegionsDefine F/T Affected Regions

FT Affected Regions Defined by Cold Temperature Constraints Index & long-term reanalysis (GMAO) data

FT domain: Vegetated areas where CCI ≥ 5 d yr-1

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Microwave Remote Sensing for F/T DetectionMicrowave Remote Sensing for F/T Detection

C-bandC-band

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Algorithm Parameterizations:

– Seasonal frozen and thawed reference states• Varies with topography and landcover

– Threshold reference (T)• Selected based on difference in seasonal

frozen and thawed states

Approach for Assignment of Parameters:- Seasonal frozen and thawed reference states may be initially assigned using prototype

SAR datasets and radar backscatter modeling over representative test sites. - Ancillary landcover and topography information may be used to interpolate reference

states across the product domain.- The threshold reference (T) depends on landcover and topography.

Setting initial algorithm parameters is a key application of the algorithm testbed. - Final parameterization will be performed using the SMAP L2 radar data as part of

reprocessing.

SMAP L3_FT_HiRes AlgorithmSMAP L3_FT_HiRes Algorithm

Baseline Algorithm

(t) = 0(t) -0fr] / [0

th -0fr]

0fr =frozen reference

0th= thawed reference

T = threshold

(t) > T (Thawed)

(t) T (Frozen)

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Seasonal Threshold

(t) = 0(t) -0fr] / [0

th -0fr]

0fr =frozen reference

0th= thawed reference

T = threshold

(t) > T (Thawed)

(t) T (Frozen)

50 100 150 200 250

Vege

tatio

nTe

mpe

ratu

re (C

)

-10

10

20

30

0

Bac

ksca

tter

(dB)

-15

-14

-13

-12

-11

-10

-9

-8

-7

-6

-5

(b)(a)

(c)(d)

Frozen ThawedStem TemperatureMean Backscatter

-1 L-band SAR landscape freeze-thaw classification

Backscatter (dB)

< -2

-4

-6

-8

-10

-13

< -18

Frozen

Water

ClassifiedState

17 Feb. (Day 48) 1 April (Day 91) 3 April (Day 93)

JERS -1 L- -

Backscatter (dB)

< -2

-4

-6

-8

-10

-13

< -18

Frozen

Water

ClassifiedState

Thawed

SMAP Freeze/Thaw Algorithm

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Source: Kim et al. 2010. Developing a global record of daily landscape freeze/thaw status using satellite passive microwave remote sensing. IEEE TGARS, DOI: 10.1109/TGRS.2010.2070515.

Seasonal Threshold Approach:

Annual Definition of SSM/I (37V GHz) Tb F/T Reference States

Frozen Non-Frozen

Pixel-wise Calibration using Tmx/Tmn from Global Reanalysis

ΔTb

F/T Classification AlgorithmF/T Classification Algorithm

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McDonald et al.

Freeze/Thaw Algorithm: Other Considerations

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Apr 10

Jul 19

Dec 26

Daily Freeze-Thaw StatusSSM/I (37GHz, 25km Res.) 2004

Source: http://freezethaw.ntsg.umt.edu

• Daily F/T state maps:-Frozen (AM & PM), -Thawed (AM & PM), -Transitional (AM frozen, PM thaw), -Inverse-Transitional (AM thaw, PM frozen)

• Global domain - F/T affected areas:

- 66 million km2 or 52% of global vegetated

area);

L3_FT_A AM-PM Combined Product PrototypeL3_FT_A AM-PM Combined Product Prototype

Mean Seasonal F-T ProgressionSSM/I 1988-2007

Non-frozen

Transitional

Frozen

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Algorithm requirementsAlgorithm requirements

L3_F/T_A:Obtain measurements of binary F/T transitions in boreal (≥45N) zones with ≥80%

spatial classification accuracy (baseline); capture F/T constraints on boreal C fluxes consistent with tower flux measurements.

L4_Carbon:

Obtain estimates of land-atmosphere CO2 exchange (NEE) at accuracy level commensurate with tower based CO2 Obs. (RMSE ≤ 30 g C m-2 yr-1).

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Level 4 Carbon Algorithm Development for SMAP Level 4 Carbon Algorithm Development for SMAP

)1(*ˆ*** autopt fGPPCMoistTempkNEE

MODISMODISAMSR-E / MERRAAMSR-E / MERRA

3

0

ˆi

iiCkC

0

0.5

1

1.5

2

-10 -2 6 14 22 30 38

T (deg C)

Tm

ult (

DIM

)

0

0.5

1

0 20 40 60 80 100

Soil Moisture (%)

Wm

ult (D

IM)

[g C m-2]

(1)

(2)

Soil T Soil Moisture

Scalar Multipliers [0,1]

Tundra (2Samoylov Island, Siberia)

• A level 4 carbon product (L4_C) is being developed as part of the Soil Moisture Active Passive Mission (SMAP);

• Algorithm employs a 3-pool soil decomposition model (1TCF) with ancillary GPP, T & SM inputs;

• Initial L4_C global runs are driven by MODIS, AMSR-E & reanalysis (MERRA) inputs;

SMAP Mission: http://smap.jpl.nasa.gov

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The UMT AMSR-E Global Land Parameter DatabaseThe UMT AMSR-E Global Land Parameter Database

Surface Air Temperature [Tmx,mn; °C]

0 0.5 1 1.5

0 0.5 1.0 1.5

Vegetation Optical Depth (VOD)

0 0.1 0.2 0.3 0.4 0.5

Open Water Fraction [Fw]

Atm. Water Vapor [V, mm]

0 0 .0 5 0 .1 0 .1 5 0 .2 0 .2 5

0 0.05 0.1 0.15 0.20 0.25

Dense Vegetation

Soil Moisture [mv, vol.]Data Characteristics: Variables: Tmx,mn; mv (10.7, 6.9 GHz); Fw; VOD (10.7,

6.9, 18.7 GHz); V (total col.); Global, daily coverage; Period of Record: 2002 – 2008. Product maturity: 3-7 (TRL) Available online (NSIDC & UMT) Reprocessing planned

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Source: Kimball, J.S., L.A. Jones, et al., 2008. IEEE TGARS (in-press); 1Baldocchi, D., 2008. Aust. J. Botany 56, 1-26.

Satellite Mapping of Land-Atmosphere COSatellite Mapping of Land-Atmosphere CO22 Exchange using MODIS Exchange using MODIS

and AMSR-E: L4 Carbon Product Development for SMAPand AMSR-E: L4 Carbon Product Development for SMAP

• Application of MODIS - AMSR-E carbon model over boreal-Arctic tower sites indicates RMSE accuracies sufficient to determine NEE (net ecosystem exchange) to within ~31 g C m -2 yr-1, which is within 1estimated (30-100 gC m-2 yr-1) tower measurement accuracy.

• Sensitivity studies show SMAP will provide improved Ts and SM inputs, and resolve NEE to within ~13 g C m-2 over a ~100-day growing season.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

J an-02 J ul-02 J an-03 J ul-03 J an-04 J ul-04

(g C

m-2 d

-1)

BI OME-BGC Tower_1 C-model Tower_2

-4

-2

0

2

4


(g C

m-2 d

-1)

0

2

4

6

8

10

12


(g C

m-2 d

-1)

0

1

2

3

4

5

6

7

8

9

J an-02 J ul-02 J an-03 J ul-03 J an-04 J ul-04Rto

t (g

C m

-2 d

-1)

BI OME-BGC Tower C-model

Total Respiration

GPP

NEE

OBS Site (Mature Black Spruce Forest)

NEE

BRO Site (Wet- sedge Tundra)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5


(g C

m-2 d

-1)

BI OME-BGC Tower 1 C-model Tower 2

GPP

0

0.5

1

1.5

2

2.5

3

3.5


Rto

t (g

C m

-2 d

-1)


Total Respiration

*

*Courtesy Y. Harazono

*

*Courtesy S. Wof sy

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2


(g C

m-2 d

-1)

BI OME-BGC Tower_1 C-model Tower_2

-4

-2

0

2

4


(g C

m-2 d

-1)

0

2

4

6

8

10

12


(g C

m-2 d

-1)

0

1

2

3

4

5

6

7

8

9

J an-02 J ul-02 J an-03 J ul-03 J an-04 J ul-04Rto

t (g

C m

-2 d

-1)

BI OME-BGC Tower C-model

Total Respiration

GPP

NEE

OBS Site (Mature Black Spruce Forest)

NEE

BRO Site (Wet- sedge Tundra)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5


(g C

m-2 d

-1)


GPP

0

0.5

1

1.5

2

2.5

3

3.5


Rto

t (g

C m

-2 d

-1)


Total Respiration

*

*Courtesy Y. Harazono

*

*Courtesy S. Wof sy

Boreal Forest (OBS) Tundra (BRO)

NEE

GPP

Rtot

NEE

GPP

Rtot

0 - WAT1 - ENLF5 - MXF7 - OSB8 - WSV10 - GRS13 - CRP13 - URB16 - BRNStation

ATQ BRO UPD

IVO

IARC LTH OAS OBS

HPV


ATQ BRO UPD

IVO

IARC LTH OAS OBS

HPV



ATQ BRO UPD

IVO

IARC LTH OAS OBS

HPV

Boreal-Arctic Tower Test Sites

56 km

56 km

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Estimated Annual C Fluxes vs Site Ecosystem Model ResultsEstimated Annual C Fluxes vs Site Ecosystem Model Results

GPP (g C m- 2 yr- 1)

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

BI OME-BGC

MO

DIS

(M

OD

17A

2/3

)

I VO LTH OBS BRO OAS UPAD I ARC ATQ TLK

1:1RMSE = 25.3%MR = 7.1%

Rtot (g C m- 2 yr- 1)

0

200

400

600

800

1000

0 200 400 600 800 1000

BI OME-BGC

MO

DIS

-AM

SR-E

(C-M

odel

)

I VO LTH OBS BRO OAS UPAD I ARC ATQ TLK

1:1RMSE = 28.8%MR = 21.5%

• C-Model derived annual GPP and Rtot similar (RMSE<30%) to stand ecosystem process model results across latitudinal gradient of boreal-arctic tower sites.

• Uncertainty in residual NEE larger than component GPP/Rtot fluxes, especially for low productivity tundra sites.


ATQ BRO UPD

IVO

IARC LTH OAS OBS

HPV


ATQ BRO UPD

IVO

IARC LTH OAS OBS

HPV



ATQ BRO UPD

IVO

IARC LTH OAS OBS

HPV

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Daily T and SM Time Series from AMSR-E and MERRADaily T and SM Time Series from AMSR-E and MERRA

WMO weather stations

USA Biophysical stations (SCAN, Ameriflux, …)

Source: Yi, Kimball, Jones, Reichle, McDonald, 2011. Journal of Climate

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Prototype L4_C using MODIS-MERRA inputsPrototype L4_C using MODIS-MERRA inputs

Algorithm calibration and validation using FLUXNET tower CO2 (GPP, Reco, NEE) flux measurements across global range of land cover types.

L4_C and Tower Reco Comparison

FLUXNET Tower Eddy Covariance Measurement Network

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Quantifying Land Source-Sink activity for CO2

Initial conditions (1ESRL)

Final optimized C-flux (1ESRL)

Initial conditions (L4_C)

Final optimized C-flux (L4_C)

1http://www.esrl.noaa.gov/gmd/ccgg/carbontracker

July 2003• The L4_C NEE (g C m-2 d-1) outputs provide initial conditions for 1CarbonTracker inversions of terrestrial CO2 source/sink activity;

• Differences in final optimized monthly C-fluxes relative to 1ESRL baseline are strongly dependent on these initial “first guess” C-fluxes (right);

• Atm. inversions provide additional verification of L4_C NEE against global flask network Obs. & other land models;

• Results link C source-sink activity to underlying vegetation productivity & moisture/temperature controls.

Soil Moisture Active and Passive (SMAP) Mission

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Extra slides

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Prototype L4_C Implementation using MODIS-MERRA inputsPrototype L4_C Implementation using MODIS-MERRA inputs

Latitudinal-zone average of NEE and GPP

Annual NEE was estimated at a 0.5 degree spatial resolution globally over a 7-year record using daily time series MERRA (SM, T) & MODIS (GPP) inputs. Estimated global carbon (NEE) source (+) & sink (-) variability is strongly affected by tropical (EBF) areas (above); large source activity in the tropics is driven by regional drought-induced GPP decline.

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Tsoil (°C, <10cm)

GPP (g C m-2 d-1)Raut (g C m-2 d-1)

Rh (g C m-2 d-1)SOC (g C m-2 d-1)

Reanalysis (e.g. GMAO)

•R (W m-2)•Ta (°C)•VPD (Pa)

MODIS/AVHRR/VIIRS:

•EVI-NDVI•LAI-FPARSMAP:

•L1C_S0_HiRes (HH VV HV)•L1B/C_Tb (AM, K)•L3_FT_HiRes (DIM)•L3_SM_A/P (g m-2)

SMAP L4 Carbon Product Development

NEE (g C m-2 d-1)MODIS MOD17A2 Algorithm (Running et al. 2004)TCF Model (Kimball et al. 2008)SMAP L1/3 product streamsMicrowave RS based soil T (e.g. Jones et al. 07, Wigneron et al. 08)

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Nominal SMAP Mission OverviewNominal SMAP Mission Overview

• Science Measurements Soil moisture and freeze/thaw state

• Orbit: Sun-synchronous, 6 am/6pm nodal crossing 670 km altitude

• Instruments: L-band (1.26 GHz) radar

Polarization: HH, VV, HV SAR mode: 1-3 km resolution (degrades over center

30% of swath) Real-aperture mode: 30 x 6 km resolution

L-band (1.4 GHz) radiometer Polarization: V, H, U 40 km resolution

Instrument antenna (shared by radar & radiometer) 6-m diameter deployable mesh antenna Conical scan at 14.6 rpm incidence angle: 40 degrees

Creating Contiguous 1000 km swath Swath and orbit enable 2-3 day revisit

• Mission Ops duration: 3 years

SMAP has significant heritage from the Hydros mission concept and Phase A studies

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Climate Change:Monitoring of patterns, variations & anomalies in CO2 source/sink

activity; vegetation, moisture & temperature effects on carbon uptake and release.

Forestry and Agriculture:Carbon sequestration assessment and monitoring; net productivity;

drought impacts, disturbance & recovery; Spatial-temporal extrapolation of in situ observations.

Environmental Policy:Regional carbon budgets; carbon accounting and vulnerability

assessments.

Potential ApplicationsPotential Applications

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BackupBackup

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Baseline Science Data ProductsBaseline Science Data Products

Data Product Description

L1B_S0_LoRes Low Resolution Radar σo in Time Order

L1C_S0_HiRes High Resolution Radar σo on Earth Grid

L1B_TB Radiometer TB in Time Order

L1C_TB Radiometer TB on Earth Grid

L2/3_F/T_HiRes Freeze/Thaw State on Earth Grid

L2/3_SM_HiRes Radar Soil Moisture on Earth Grid

L2/3_SM_40km Radiometer Soil Moisture on Earth Grid

L2/3_SM_A/P Radar/Radiometer Soil Moisture on Earth Grid

L4_Carbon Model Assimilation on Earth Grid

L4_SM_profile Model Assimilation on Earth Grid

Global Mapping L-Band Radar and Radiometer

High-Resolution and Frequent-Revisit

Science Data

Observations + Models =Value-Added Science Data

Documents

SMAP Science Objectives