Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical...

Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS

John Kimball1,2

1Numerical Terradynamic Simulation Group, University of Montana, USA.

2Flathead Lake Biological Station, Division of Biological Sciences, Univ. MT.

Joint AMSR Science Team Meeting; July 14-16 2008

with: Lucas Jones1,2, Ke Zhang1,2 and Qiaozhen Mu1

• Apply AMSR-E multi-frequency H/V Pol. Tb time series to quantify daily surface soil temperature and soil moisture over northern (>50°N) study sites;

• Utilize similar approach with AMSR-E AM/PM H/V Pol. Tb series to estimate daily air temperature and VPD.

• Utilize synergistic information from AMSR-E and MODIS to quantify land-atmosphere carbon fluxes and ET.

• Algorithm development and verification using biophysical measurements and ecosystem process model simulations from regional station networks.

Approach

Working Hypothesis

• Daily Tb measurements from AMSR-E are sensitive to near-surface temperature and moisture status of northern ecosystems and can be used for mapping the primary environmental constraints to land-atmosphere carbon and water exchange.

Goal• Improved measures of land-atmosphere water, energy and carbon exchanges and interactions for monitoring northern biosphere response to recent climate change

Year1975 1980 1985 1990 1995 2000 2005

Clia

mte

Moi

stur

e In

dex

(mm

yr-1

)

-100

0

100

200

300

400

500

Trend: -2.73 mm yr-1, P < 0.001

1Pan-Arctic Drying Trend (P-PET)(Surface Station Network)

Year1980 1985 1990 1995 2000 2005

NP

P a

nom

alie

s (g

C m

-2 y

r-1)

-40

-20

0

20

40

LAI

anom

alie

s(m

2 m

-2)

-0.30

-0.15

0.00

0.15

0.30

VP

D in

dex

0.00

0.05

0.10

0.15

Tm

in in

dex

0.40

0.45

0.50

0.55

Annual NPP GS LAI GS VPD indexGS T

min index

Mt.

Pin

atu

bo

1Drought Impacts to Vegetation Productivity(AVHRR PEM record)

Recent Changes to Pan-Arctic Water/Carbon Budgets

2Regional Drying Patterns

1Kang et al., 2008. J. Geophys. Res.; 2007. 2Geophys Res. Lett. 34, L21403

0

0.5

1

0 20 40 60 80 100

Soil Moisture (%)

Wm

ult (D

IM)

0

0.5

1

1.5

2

-10 -2 6 14 22 30 38

T (deg C)

Tm

ult (

DIM

)

GPP

Tmult WmultScalar Multipliers (DIM)

Decomp. Rates (d-1)

Flux Calc. (kg C m-2):

C Substrate Pools (kg C m-2)

Rh = (Kmet * Cmet + Kstr + Cstr + Krec * Crec)

Cmet = Cfract * NPPCstr = (1-Cfract) * NPPCrec = 0.7 * Cstr

Kmet = (Kmx * Tmult * Wmult)Kstr = 0.4 * Kmet

Krec = 0.01 * Kmet

Tsoil (deg C) 1*Soil Moisture (% Sat.) Land cover (BPLUT)

(AMSR-E) (MODIS)

NPP = GPP * (1-CUE)Ra = GPP - NPP

Cfract

CUE

Rh – NPP = NEE

SOC = (Cmet + Cstr + Crec) - RhOutputs:

Inputs:

Remote Sensing of Land-Atmosphere C Exchange

1Njoku, E.G. (2004). AMSR-E/Aqua Daily L3 Surface Soil Moisture, V001, NSIDC, Boulder, CO, USA. Digital Media* Scaled between max-min observations

Daily surface (<10cm depth) soil temperature retrievals (in degrees Celsius) using AMSR-E multi-frequency brightness temperatures; Remote sensing results are plotted against MODIS LST and site level measurements of soil temperature (Tsoil) and minimum daily air temperature (Tmin) from boreal forest and tundra monitoring sites.

Daily Surface Soil Temperature Retrieval from AMSR-E

Source: Jones et al., 2007.Trans. Geosci. Rem. Sens. 45(7).

Source: Kimball et al., 2008. TGRS (In press)

NEE (g C m-2) DOY 177, 2004

>7 4 2 0 -2 -4 <-7

NEE (g C m-2) DOY 177, 2004

>7 4 2 0 -2 -4 <-7

Mean Daily net CO2 Exchange

RMSE [g C m-2 d-1] accuracy relative to Tower Obs: 0.8-1.8 (GPP); 0.4-0.9 (Rtot); 0.6-1.7 (NEE)

MODIS-AMSR-E Carbon Model

Results

NEE

-4

-2

0

2

4

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower TCF

NEE

-3

-2

-1

0

1

2

3

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower_1

TCF Tower_2GPP

0123456789

10

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower TCF

GPP

0

1

2

3

4

5

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower 1

TCF Tower 2

BRO Tundra site OBS ENLF site

Rtot

0

0.5

1

1.5

2

2.5

3

3.5

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower 1

TCF Tower 2

Rtot

0123456789

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower TCF

NEE

-4

-2

0

2

4

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower TCF

NEE

-3

-2

-1

0

1

2

3

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower_1

TCF Tower_2GPP

0123456789

10

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower TCF

GPP

0

1

2

3

4

5

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower 1

TCF Tower 2

BRO Tundra site OBS ENLF site

Rtot

0

0.5

1

1.5

2

2.5

3

3.5

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower 1

TCF Tower 2

Rtot

0123456789

J-02M

-02S-02

J-03M

-03S-03

J-04M

-04S-04

(g C

m-2

d-1

)

BIOME-BGC Tower TCF

Tundra (BRO) Boreal Forest (OBS)

0 2 4 6 8 10 12 14 16 18 208

9

10

11

Tsoil [ C]

NE

E [

g C

m-2

y-1

]0 2 4 6 8 10 12 14 16 18 20

0

50

100

NE

E [

%]

0 10 20 30 40 50 60 70 80 90 1008

9

10

11

SM [% Sat]

NE

E [

g C

m-2

y-1

]

Tsoil = 1 CTsoil = 2 CTsoil = 3 C

0 10 20 30 40 50 60 70 80 90 1000

50

100

NE

E [

%]

Carbon Model Error Sensitivity

Source: Kimball et al. 2008. Trans. Geosci. Rem. Sens. (in press)2Baldocchi, D., 2008. Australian Journal of Botany 56.

Estimated carbon model RMSE uncertainty from MODIS (1GPP) and AMSR-E (Ts and SM) inputs indicates MODIS/AMSR-E accuracies (GPP~1.2 g C m-2 d-1; Ts < 3.5 K; SM < 40 % [~20 % vol]) sufficient to resolve NEE to within ~7-31 g C m-2 yr-1. This is within the 1reported (30-100 g C m-2 yr-1) range of accuracy for tower measurements.

1Assumed constant GPP error of 1.2 g C m-2 d-1; average GPP = 500 g C m-2 y-1

SM = 30 % Sat

Tsoil = 10 °C

Estimating ET from MODIS-AMSR-E Inputs

Satellite Based Daily ET Algorithm Flow Chart

Source: Mu, Q. et al., 2007. Rem. Sens. Environ. 111.

MODIS

GMAO

ModelInputs

AMSR-E

1Veg. Water Content/Roughness [kg m-2]

0 2 4 6 8 100

20

40

60

80

100

So

il E

mis

sio

n A

bo

ve

Ca

no

py

[%

]

Canopy Water Content [kg m-2]

18.7 GHz

10.7 GHz

6.9 GHz

1.4 GHz1

1 Frequency dependence of canopy loss from Njoku & Chan Rem. Sens. Environ. (2006)

Vegetation Biomass Constraints on Microwave RS Observations of Soil Processes

Linear correlation between AMSR-E uncorrected Tbv values for various frequencies and in situ temperature measurements for selected tundra (HPV), grassland (LTH) and boreal forest (NOBS, OAS) sites.

Source: Jones et al., 2007.Trans. Geosci. Rem. Sens. 45(7).

Daily Air Temperature (Tmn, Tmx, Tav) Estimation from AMSR-E day/night Tbs

Method 1: Multiple Regression

Uses vertically polarized AM/PM (Asc/Desc)Tb data at 10.7, 18.7, and 89 GHz frequencies, and H/V polarization ratios of the 6.9 GHz and 89 GHz channels

Method 2: Emissivity Triangle RT-model

Vertical (Profile)

Horizontal (footprint)

Each pixel represents a mixture of open water and vegetated soil:

Uses 6.9, 10.7, 18.7, 36.5 GHz polarization ratios to iteratively solve for open water fraction and vegetation/roughness parameters and uses 36.5 GHz V-pol. AM/PM Tbs to solve for Tmn/Tmx

Tbh vs.Tbv (6.9 GHz; J une- Aug 2003)

50 100 150 200 250 300

160

180

200

220

240

260

280

300

320

Tbh [K]

Tbv

[K

]

Forested Regions

Desert Regions

Open Ocean

RFI

1:1

50 100 150 200 250 300

160

180

200

220

240

260

280

300

320

Tbh [K]

Tbv

[K

]

Forested Regions

Desert Regions

Open Ocean

RFI

1:150 100 150 200 250 300

Tbh [K]

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

Northern HemisphereRelative Freq.

%(1- K bins)

Estimating Daily Vapor Pressure Deficit

Uses AMSR-E Tmx/Tmn retrievals to calculate mean daily air temperature

Assumes Tmn = Dewpoint temperature1

Relatively Robust for northern regions with low night-time temperatures and high surface water storage (low surface evaporative resistance)

An arid region correction can be applied1

1Source: Kimball et. al. Ag. For. Meteor. (1997) 85.

Tmn Tmx VPDkPa

°C

July 9, 2003

R2 = 0.90RMSE = 2.85 °CMR = 1.54 °C

R2 = 0.84RMSE = 3.27 °CMR = 0.52°C

R2 = 0.69RMSE = 2.96 °CMR = -0.31 °C

R2 = 0.72RMSE = 3.40 °CMR = 1.68 °C

R2 = 0.70RMSE = 0.30 kPaMR = 0.07 kPa

R2 = 0.53RMSE = 0.40 kPaMR = 0.01 kPa

Comparison of AMSR-E and GMAO meteorological variables to tower observations at all sites; solid lines represent the linear least-square regression line, while dashed lines represent a 1:1 relationship.

Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).

0

10

20

30

40

50

60

70

ATQ BRWO BRWY LTH NOBS OAS

Roo

t Mea

n S

quar

e E

rror

(W/m

2) RMSE: tower met

RMSE: AMSRE met

RMSE: GMAO met

Tower vs Model Based ET


NSA-OBS (ENLF) Barrow (Tundra) Lethbridge (Grassland)

Mean Annual ET

20 40 60 800

10

20

30

40

50 L

E [

W m

-2]

LE [W m-2]

T = 2 C

T = 3 C

T = 4 C

0 0.5 1 1.5 2

VPD [kPa ]

0

20

40

60

80

100

RE

LE

[%

]

Absolute error (solid black lines; W/m2) and relative error (dashed gray lines; %) propagated to model derived latent energy flux (LE) for three error levels of AMSR-E derived air temperatures. Meaningful LE information is derived when LE > 7-26 W/m2 (ET > 0.13 – 1.33 mm/d) given observed MODIS/AMSR-E input and model uncertainty. Meteorological inputs contribute 28-65% of total model LE error and translate to ~3-7% relative error in cumulative ET over a 100-day growing season.

RS-ET Error 1Sensitivity

1LAI, dew point temperature, net incoming solar radiation, and error in net incoming solar radiation are held at constant, moderate values of 3 m2 m-2, 0 °C, 300 W/m2, and 70 W/m2 (~20%), respectively. Tmax varies from 0 to 30 °C. Soil evaporation is considered negligible.


Results Summary

• AMSR-E Tb data provide reasonable estimates of surface Ta and VPD across wide range of surface/climate conditions; results similar to or better than alternative measures from station corrected reanalysis (GMAO) meteorology;

• Use of MODIS GPP and AMSR-E Tsoil, SM within a simple carbon model captures regional patterns and variability SOC stocks and C-fluxes relative to site measurements and ecosystem model simulations. Model results within range of tower measurement error; • MODIS-AMSR-E based ET results similar to tower measurements and alternate results using local and reanalysis (GMAO) based daily meteorology;

• Processing of these data continues from 2002-present and spans all Northern Hemisphere vegetated land areas;

• Results provide basis for assessing northern carbon-water cycle interactions and ecosystem response to recent warming.

Back-up Slides

Table 1: Boreal and Arctic tower flux sites used for model validation.

69.13N 148.83WTussock tundraOSBTLKHappy Valley, AK

70.47N 157.40WTussock tundraOSBATQAtqasuk, AK

64.87N 147.85WBoreal spruce forestENLFI ARCUAF-I ARC, AK

70.27N 148.88WWet-sedge tundraOSBUPADPrudhoe Bay, AK

53.63N 106.20WBoreal aspen forestMXFOASSSA-OAS, Sask. CN

71.32N 156.62WWet-sedge tundraOSBBROBarrow, AK

55.88N 98.48WBoreal spruce forestENLFOBSNSA NOBS, Manitoba CN

49.70N 112.93WGrasslandGRSLTHLethbridge, Alberta CN

68.47N 155.73WTussock tundraOSBI VOI votuk, AK

Lat. Lon.Local vegetationMODISLand cover

Site Abbrev.Site











Site Abbrev.Site

1MODI S (MOD12Q1) land cover classes overlying boreal-arctic test sites: OSB (open shrubland); GRS (grassland); ENLF (evergreen needle-leaf f orest); MXF (mixed evergreen needle-leaf and deciduous broadleaf f orest).

1

Table 1: Boreal and Arctic tower flux sites used for model validation.











Site Abbrev.Site











Site Abbrev.Site

1MODI S (MOD12Q1) land cover classes overlying boreal-arctic test sites: OSB (open shrubland); GRS (grassland); ENLF (evergreen needle-leaf f orest); MXF (mixed evergreen needle-leaf and deciduous broadleaf f orest).

1

Model Development and Validation Sites

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


0

200

400

600

800

1000

1200

1400

BRO ATQ UPD HPV IVO IARC OBS OAS LTH

BGC TCF Tower

GPP (g C m-2 yr-1)

0

200

400

600

800

1000

1200


BGC TCF Tower

Rtot (g C m-2 yr-1)

-400

-300

-200

-100

0

100


BGC TCF Tower

NEE (g C m-2 yr-1)

A

Sources of reported tower fluxes: BRO (this study, Kwon et al. 2006, Harazono et al. 2003); ATQ (this study, Kwon et al. 2006); UPD (Oechel et al. 1998); HPV (Vourlitis and Oechel 1999); IVO (this study); IARC (Ueyama et al. 2006); OBS (Dunn et al. 2007, Bergeron et al. 2007); OAS (this study, Barr et al. 2006); LTH (this study, Flanagan et al. 2002).

A

0

200

400

600

800

1000

1200

1400


BGC TCF Tower

GPP (g C m-2 yr-1)

0

200

400

600

800

1000

1200


BGC TCF Tower

Rtot (g C m-2 yr-1)

-400

-300

-200

-100

0

100


BGC TCF Tower

NEE (g C m-2 yr-1)

A


A

-400

-300

-200

-100

0

100


BGC TCF Tower

NEE (g C m-2 yr-1)

A


A

Carbon Model Results Comparison over Tower Sites


Relations Between TCF and BIOME-BGC Based Annual Carbon Fluxes

NEE (g C m-2 yr-1)

-200

-150

-100

-50

0

50

100

150

200

-200 -100 0 100 200TCF

BIO

ME

-BG

C

IVO LTH OBS

BRO OAS UPD

IARC ATQ HPV

Regression line

RMSE = 93.4 (162.9%)MR = -59.8 (65.8%)

1:1 lineLinear regression line

NEE (g C m-2 yr-1)

-200

-150

-100

-50

0

50

100

150

200

-200 -100 0 100 200TCF

BIO

ME

-BG

C

IVO LTH OBS

BRO OAS UPD

IARC ATQ HPV

Regression line

RMSE = 93.4 (162.9%)MR = -59.8 (65.8%)

1:1 lineLinear regression line

GPP (g C m-2 yr-1)

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

MODIS (MOD17A2)

BIO

ME

-BG

C

R2 = 88.07%RMSE = 115.6 (25.3%)MR = 49.9 (7.1%)

GPP (g C m-2 yr-1)

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

MODIS (MOD17A2)

BIO

ME

-BG

C

R2 = 88.07%RMSE = 115.6 (25.3%)MR = 49.9 (7.1%)

Rtot (g C m-2 yr-1)

0

200

400

600

800

1000

0 200 400 600 800 1000TCF

BIO

ME

-BG

C

R2 = 89.03%RMSE = 86.7 (22.9%)MR = -9.8 (-4.3%)

Rtot (g C m-2 yr-1)

0

200

400

600

800

1000

0 200 400 600 800 1000TCF

BIO

ME

-BG

C

R2 = 89.03%RMSE = 86.7 (22.9%)MR = -9.8 (-4.3%)

AM

SR

-E6.

9 G

Hz

19-Feb 10-Apr 30-May 19-J ul 07-Sep 27-Oct0

50

100

Date J un- 2002 to Dec- 2004

SM

[%

Sat

]


50

100


SM [

%Sa

t]


50

100


SM

[%

Sat

]

AMSR-E Daily 1Soil Moisture Retrievals

NSA-OBS, CN (ENLF)

Barrow, AK (Coastal Tundra)

Lethbridge, CN (Grassland)

Site vs. AMSR-E SM for Tower Windows

Scaled L3 product

June 15,

2003

• AMSR-E soil moisture RMSE values range from 22 to 48 %; R2 range 0.59 to <0.01 for both methods.

• AMSR-E results similar to site (BIOME-BGC) modeled soil moisture accuracy (RMSE range from 22 to 44 %; R2 range 0.53 to <0.01).

• Retrieval error increases primarily under increasing biomass and water fraction

1Source: Njoku, E.G. (2004). AMSR-E/Aqua Daily L3 Surface Soil Moisture, V001, NSIDC, Boulder, CO, USA. Digital Media* Scaled between max-min observations

Surface Wetness % Sat

Site observed <10 cm SMBGC SM

AMSR-E L3 SMAMSR-E LSW

AMSR-E Temperature Algorithm

• Multiple regression method:

).. freqpol.,vftreqs ,f(TbT

• Emission Process method:

b

TbaTbT hvs

Uses normalized polarization ratio [ = (Tbv -Tbh)/(Tbv +Tbh)] to correct for surface water

Multiple V-pol. bands (6, 10, 23, 89 GHz) contribute additional information; separate coefficients for frozen and non-frozen conditions.

Assumes each pixel represents a mixture of open water and vegetated soil

Slope (a) and intercept (b) dependence on land surface emissivity described by simple RT equation and constant open water emissivity

Iterative minimization of Ts for adjacent bands allows simultaneous estimates of land emissivity and Ts.

Source: Jones, L.A., et al., 2007. Trans. Geosci. Rem. Sens. 45(7), 2004-2018.

Tbh vs.Tbv (6.9 GHz; J une- Aug 2003)

50 100 150 200 250 300

160

180

200

220

240

260

280

300

320

Tbh [K]

Tbv

[K

]

Forested Regions

Desert Regions

Open Ocean

RFI

1:1

50 100 150 200 250 300

160

180

200

220

240

260

280

300

320

Tbh [K]

Tbv

[K

]

Forested Regions

Desert Regions

Open Ocean

RFI

1:150 100 150 200 250 300

Tbh [K]

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

Northern HemisphereRelative Freq.

%(1- K bins)

the mean (2000-2006) seasonality of regional ET for the pan-Arctic domain as derived from the RS-ET algorithm and GMAO meteorology. Masked areas are shown in white.

Seasonality in MODIS Based ET



MODIS-AMSR-E Estimated Surface Soil Organic Carbon

(≤10cm depth, 2002-2004)

0

2000

4000

6000

8000

10000

12000


SO

C (

g C

m-2

)

TCF BGC IGBP-DIS Site

TCF = MODIS-AMSR-E C modelBGC = BIOME-BGC IGBP-DIS = Global SOC InventorySite = Tower site SOC Inventory

Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical...

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