Developing a Global Land Parameter Database for Terrestrial Ecosystem Studies using AMSR-E

John S. Kimball

Flathead Lake Biological Station, Division of Biological Sciences, The University Flathead Lake Biological Station, Division of Biological Sciences, The University of Montanaof Montana

CollaboratorsCollaborators: Lucas Jones, Yonghong Yi, Youngwook Kim & Matt Jones (UMT); : Lucas Jones, Yonghong Yi, Youngwook Kim & Matt Jones (UMT); Kyle McDonald, Eni Njoku & Steven Chan (JPL); Rolf Reichle (GSFC); Rama Kyle McDonald, Eni Njoku & Steven Chan (JPL); Rolf Reichle (GSFC); Rama Nemani (Ames); Craig Ferguson & Eric Wood (Princeton).Nemani (Ames); Craig Ferguson & Eric Wood (Princeton).

Recent Activities:Recent Activities:

Release of a new AMSR-E global daily land parameter database (2002-08) including surface air temperature (T), freeze-thaw status (FT) , fractional open water cover (Fw), precipitable water vapor (V), soil moisture (SM) & vegetation optical depth (VOD); available at the NSIDC DAAC (http://nsidc.org/data/nsidc-0451.html );

Global Implementation & testing of a terrestrial carbon flux model (TCF) using AMSR-E, MERRA and MODIS inputs;

Verification of RS based land parameters using global biophysical measurement networks, synergistic data from other satellite products & model reanalysis;

Application of the AMSR-E VOD parameter for global phenology analysis;

Development of a global Data Record for the FT state parameter using ensemble satellite microwave remote sensing time series;

Algorithm development & refinement for the NASA Soil Moisture Active Passive mission (SMAP, 2014 launch).

Global Implementation of a Terrestrial Carbon Flux (TCF) ModelGlobal Implementation of a Terrestrial Carbon Flux (TCF) Model11 using using AMSR-E and MODIS Inputs AMSR-E and MODIS Inputs

)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]

1Kimball, J.S., L.A. Jones, et al. 2009. IEEE TGARS, 47(2), 569-587; 2Mildrexler et al. 2009. RSE 112: 2103-2117.

Soil and Litter Carbon (3-pools) from spin-up and disturbance/landcover maps.

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

Northern Old Black Spruce, Manitoba

Incorporate disturbance and recovery

Mildrexler et al. (2009)

2

0 2 4 6 8 10≤0 50 100 150 200 250 300 350

0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1

Temperature Multiplier (1=optimal) Moisture Multiplier (1=optimal)

Frozen/Snow Covered Season Constraint [days] TCF Model Steady State SOC [kg m-2]

Mean (2003 - 2006) annual surface (<10cm depth) soil temperature and moisture constraints (top) to heterotrophic respiration derived using TCF model with AMSR-E and MODIS (GPP) inputs. (a) The dimensionless temperature multiplier [Tmult; 0-1] is an exponential function of mean daily soil temperature from AMSR-E. (b) The soil moisture multiplier (Wmult) is a convex-parabolic function of soil wetness from AMSR-E. Wmult is assigned a value of 1 where AMSR-E (10.7GHz) vegetation optical depth >1.5. (c) The frozen season constraint is derived from AMSR-E 36V GHz Tb series. (d) The TCF model steady state surface SOC content is derived for conditions described by (a) – (d).

AMSR-E Derived Environmental Constraints to TCF Soil Carbon Estimates

Calibration & Verification of TCF Soil Carbon Stocks from Global Inventory DataCalibration & Verification of TCF Soil Carbon Stocks from Global Inventory Data

AMSR-E Steady State SOC1 [kg C m-2]

Soil Pit Data2 SOC1 [ kg C m-2]

1Adjusted to surface (<10 cm) layer and shown as medians for 2 ° × 2° grid cells; 2Source: Zinke et al. (1998), http://www.daac.ornl.gov.

0 5 10 15 20 250

100

200

300

Site Obs. SOC [kg C m-2]

freq

.

0 5 10 15 20 250

100

200

300

Model Steady State SOC [kg C m-2]

freq

.

0 5 10 15 20 250

100

200

300

Site Obs. SOC [kg C m-2]

freq

.

0 5 10 15 20 250

100

200

300

Model Steady State SOC [kg C m-2]

freq

.

SOC Probability Density Function (PDF)

TCF Model Calibration: NEE Response to Soil MoistureTCF Model Calibration: NEE Response to Soil Moisture

Jan 1 Jun 10 Nov 17 Apr 26 Oct 3 Mar 12 Aug 19 Jan 26 Jul 5 Dec 12-40

-20

0

20

40

NE

E [

g C

m-2

d-1

] TT

Jan 1 Jun 10 Nov 17 Apr 26 Oct 3 Mar 12 Aug 19 Jan 26 Jul 5 Dec 12-40

-20

0

20

40

NE

E [

g C

m-2

d-1

] AT

Jan 1 Jun 10 Nov 17 Apr 26 Oct 3 Mar 12 Aug 19 Jan 26 Jul 5 Dec 12-40

-20

0

20

40

NEE

[g C

m-2

d-1

] AM

Woody Savannah (Tonzi Ranch, CA1)

1D. Baldocchi is PI of the Tonzi and Vairi Ameriflux sites; 2R. Scott is PI of the Santa Rita Site

0 20 40 60 80 1000

0.5

1

Soil Moisture [% Sat]

Wm

ult [

dim

]

Original

Tonzi Vaira Santa Rita2

σ [g

C m

-2 d

-1]

0

1

2

3TM AMATTT

TT : Tower Met. + Tower GPPAT : AMSR-E Met. + Tower GPPTM: Tower Met. + MODIS GPPAM: AMSR-E Met. + MODIS GPP

Flux tower obs.

LTHOAS

NOBSATQ

Site Met + Site GPP AMSR-E Met + MODIS GPP

2×Error Stdev. 95 % Prediction Posterior Spread

NEE

NPP

Rh

NEE

NPP

Rh

Mar 21 J un 9 Aug 28 Nov 16 Feb 4 Apr 25 J ul 14 Oct 2 Dec 210

1

2

3

4

5

6

Rh [

g C

m-2

d-1

]

LTHOAS

NOBSATQ

Site Met + Site GPP AMSR-E Met + MODIS GPP

2×Error Stdev. 95 % Prediction Posterior Spread

NEE

NPP

Rh

NEE

NPP

Rh

LTHOAS

NOBSATQ

Site Met + Site GPP AMSR-E Met + MODIS GPP

2×Error Stdev. 95 % Prediction Posterior Spread

NEE

NPP

Rh

NEE

NPP

Rh

LTHOAS

NOBSATQ

Site Met + Site GPP AMSR-E Met + MODIS GPP

2×Error Stdev. 95 % Prediction Posterior Spread

NEE

NPP

Rh

NEE

NPP

Rh

Prediction Median

NEE

[g C

m-2

8d-1

]

Correspondence Between AMSR-E and MERRA T and SM Time Series

WMO weather stations

USA Biophysical stations (SCAN, Ameriflux, …)

Combining AMSR-E and MERRA for Improved Soil Moisture Accuracy

• Kalman Smoother (KS) method: AMSR-E and MERRA anomalies are combined with

an empirical random walk (AR(1)) model using the KS. Respective climatologies are averaged.

AMSR-E improves short-term wetting/drying dynamics

Walnut Gulch Grasslands, AZ (2003-2004)2

Soil

Moi

stur

e [c

m3 c

m-3

]

1Vienna University X-band soil moisture data (L2A Res 1 swath, 25 km threshold distance); 2In situ soil moisture data provided by Ameriflux

Walnut Gulch Grasslands

Lethbridge, AB Grasslands (Plot not Shown)

KS AMSR-E1 MERRA

Symbol

R (anom) 0.61 0.60 0.21

R 0.67 0.61 0.60

RMSE (anom) 0.018 0.020 0.031

RMSE 0.029 0.032 0.034

KS AMSR-E1 MERRA

R (anom) 0.51 0.38 0.33

R 0.55 0.38 0.49

RMSE (anom) 0.025 0.031 0.054

RMSE 0.066 0.087 0.078

Global TCF Simulations using MODIS-MERRA InputsGlobal TCF Simulations using MODIS-MERRA Inputs

Latitudinal-zone average of NEE and GPP

Model validation using global tower eddy covariance CO2 flux network (FLUXNET) & and atmospheric transport model inversions (CarbonTracker).

Apr 10

Oct 27

Dec 26

Daily FT Dynamics(AMSR-E AM/PM 36V GHz, 2004)

Developing a Global Data Record for Landscape Freeze/Thaw Status

2004 Non-Frozen Period(AMSR-E 36V GHz)

Goal: 1) Build a global, long-term (30+ yr) record of daily landscape freeze-thaw state dynamics with well quantified accuracy for climate change studies; 2) Inform development of similar algorithms & products under NASA SMAP mission.

Methods: Temporal change classification of ensemble satellite active & passive microwave remote sensing series; accuracy assessment and uncertainty analysis using global in situ station networks and ancillary geospatial data.

Global Mean Annual Non-Frozen Period(1979-2009)

Mean Annual Accuracy (AMSR-E, 2004) FT_ESDR QA/QC Map

Developing a Global Earth System Data Record for Landscape Freeze/Thaw Status

Mean Annual FT Classification AccuracyMean Daily FT Classification Accuracy

Source: Y. Kim et al. IEEE TGARS (In-review)

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

The Soil Moisture Active Passive Mission (SMAP)

Data Product Description

L1B_S0_LoRes Low Resolution Radar σ o in Time Order

L1C_S0_HiRes High Resolution Radar σ o 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-only Soil Moisture on Earth Grid

L2/3_SM_40km Radiometer-only Soil Moisture on Earth Grid

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

L4_Carbon Carbon Model Assimilation on Earth Grid

L4_SM_profile Soil Moisture Model Assimilation on Earth Grid

Data Product Description

L1B_S0_LoRes Low Resolution Radar σ o in Time Order

L1C_S0_HiRes High Resolution Radar σ o 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-only Soil Moisture on Earth Grid

L2/3_SM_40km Radiometer-only Soil Moisture on Earth Grid

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

L4_Carbon Carbon Model Assimilation on Earth Grid

L4_SM_profile Soil Moisture 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

NASA Tier 1 Decadal Survey mission (2014 launch); L-band radar & radiometer suite with global 1-3 day repeat coverage & 3-40km resolution; Prelaunch L4_Carbon & L3_F/T algorithm development, testing using AMSR-E, MODIS and MERRA land products and model drivers;

AMSR-EVOD

AB

C

Percent of Total(2003-2008)

0 – 20%21-40%41-60%61-80%81-100%

Highest QC Data Availability

MODIS EVI

A B C

Global Phenology Monitoring using Vegetation Optical Depth (VOD) from AMSR-E

IGBP Barren Land Cover Class

R-value

-1.0 -.75 -.5 -.25 .25 .5 .75 1.0

R-value

• AMSR-E VOD (10.7GHz) is well correlated with MODIS LAI, EVI and NDVI

• Microwave provides enhanced data availability, especially over cloud dominated regions, resulting in complete vegetation phenologies when optical-IR VIs are unavailable or saturated

• AMSR-E VOD provides a unique and complimentary phenology dataset.

MODIS LAI & AMSR-E VOD Correlation8-Day Data2003-2008

Source: M. Jones et al. RSE (In-review)

AMSR-E seasonal Fw changes:

Mapping open water fraction (Fw) from AMSR-EMapping open water fraction (Fw) from AMSR-E

JERS-1(100 m)

AMSR-E(25 km)

MODIS(1 km)

A

B

Jan 1 Feb 10 Mar 22 May 1 Jun 10 Jul 20 Aug 29 Oct 8 Nov 17 Dec 270

0.1

0.2

0.3

0.4

AM

SR

-E f

w

0

0.01

0.02

0.03

0.04

Yukon River, Delta (A)

Yukon River, Stevens Village (B)

Comparison of Alaska fw maps derived from AMSR-E and relatively fine scale JERS-1 and MODIS land cover classifications (a). The AMSR-E fw product produces similar results (b), but with enhanced capabilities for near-daily monitoring of this dynamic variable (c); fw is a bi-product of the AMSR-E surface air temperature retrievals and is useful for global water, energy & carbon cycle studies.

(a)

(c)

(b)

JERS-1 AMSR-E MODIS0

1

2

3

4

5

Op

en

Wa

ter

% o

f T

ota

l A

rea

Fw (25 km)

Jones, L.A., J.S. Kimball, E. Podest, K.C. McDonald, S.K. Chan, and E.G. Njoku, 2009. A method for deriving land surface moisture, vegetation optical depth and open water fraction from AMSR-E. Proceedings of the IEEE Int. Geosci. Rem. Sens. Symp. (IGARSS ‘09), 916-919.

SummarySummary

Verification and analysis of the AMSR-E land parameter database is ongoing; periodic updates (post-2008) will occur as additional data become available & posted to online archive (http://nsidc.org/data/nsidc-0451.html);

Accuracy documented for T, VOD, FT, Fw & SM; less so for V;

Ecological investigations of these data are ongoing, including FT & VOD phenology dynamics; T & SM driven assessment of NEE & global carbon source/sink activity, & Fw based wetland inundation dynamics.

AMSR-E T, SM series provide additional value over similar variables from MERRA reanalysis; AMSR-E SM accuracy & value decreases with increasing vegetation biomass;

TCF model calibration, testing and validation activities on-going in conjunction with L4 Carbon product development for SMAP.

1) Jones, L.A., C.R. Ferguson, J.S. Kimball, K. Zhang, S.K. Chan, K.C. McDonald, E.G. Njoku, and E.F. Wood, 2010. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE JSTARS 3(1), 111-123.

2) Jones, L.A., and J.S. Kimball, 2010. Daily Global Land Surface Parameters Derived from AMSR-E. Boulder Colorado USA: National Snow and Ice Data Center. Digital media (http://nsidc.org/data/nsidc-0451.html).

3) Jones, L.A., J.S. Kimball, E. Podest, K.C. McDonald, S.K. Chan, and E.G. Njoku, 2009. A method for deriving land surface moisture, vegetation optical depth and open water fraction from AMSR-E. Proceedings of the IEEE Int. Geosci. Rem. Sens. Symp. (IGARSS ‘09), Cape Town, South Africa, 916-919.

4) Jones, M., J.S. Kimball, K.C. McDonald, and L.A. Jones, 2010. Utilizing satellite passive microwave remote sensing for monitoring global land surface phenology. Rem. Sens. Environ. (In-review).

5) Kim, Y. J.S. Kimball, K.C. McDonald and J. Glassy, 2010. Developing a global data record of daily landscape freeze/thaw status using satellite microwave remote sensing. IEEE TGARS (In-review).

6) Kimball, J.S., L.A. Jones, K. Zhang, F.A. Heinsch, K.C. McDonald, and W.C. Oechel, 2009. A satellite approach to estimate land-atmosphere CO2 exchange for Boreal and Arctic biomes using MODIS and AMSR-E. IEEE TGARS, 47(2), 569-587.

7) Mu, Q., L.A. Jones, J.S. Kimball, K.C. McDonald, and S.W. Running, 2009. Satellite assessment of land surface evapotranspiration for the pan-Arctic domain. Water Resources Research 45, W09420, doi:10.1029/2008WR007189.

Recent Publications:

Vertical (Profile) Horizontal (footprint)

Each pixel is modeled as a mix of uniformly vegetated land and open water:

AMSR-E Algorithm Logic: Veg. Optical Depth and Soil Moisture

Given temperature data the H-V slope can be calculated:

wat

wat

eheh

eveva

Optical depth (VOD) is determined from a. Land fraction emissivity error increases with open water:

0.2 0.4 0.6 0.8 10.5

0.6

0.7

0.8

0.9

1

H pol. emissivityV

pol

. em

issi

vity

dry soil

wet soil

forest

1:1Open water

Fresnel

Emissivity Triangle

fwe landp

1

1,

Soil moisture is obtained by inverting the τ-ω equation with smooth optical depth and open water corrections.

Jones, L.A., et al. IGARSS ’09 Proceedings Cape Town, South Africa, 916-919.

AMSR-E Algorithm Logic: Temperature and Open Water

0 20 40 60 800

20

40

60

80

Tbv

23 -

Tbh

23 [K

]

Tbv18 – Tbh18 [K]

V = 5 mm

V = 50 mm

0 0.2 0.4

1

1.2

1.4

1-Tbh18/Tbv18

Tbh

23/T

bh18

V = 5 mm

V = 50 mm

Separation of atmospheric water vapor and surface emissivity

Global 2003 Averages (AMSR-E, AM)

Surface Air Temperature [°C]

Atmos. Water Vapor [mm]

Jones, L.A., et al. 2010. IEEE JSTARS 3(1), 111-123.

Combining AMSR-E and MERRA Soil Moisture Time-series:A State-Space (Kalman Smoother) Approach (METHODS)

)()(1

1

)(

)(ttSM

tSM

tSMopt

merra

amsre υ

)()1()( ttSMtSM optopt

),0(~)( Rυ Nt

Observation Equation:

Random Walk Model Equation (AR(1)):

),0(~)( QNt

Maximum Likelihood Filter optimization:

')(2

1])log[det(

2

1

1

1

1t

n

tt

n

t

εΣεΣ tt

Method Steps:1)Break soil moisture time series into anomaly and climatology components (30-day moving average). 2)Scale AMSR-E anomalies to have the same variance as MERRA.3)Optimize KS observation (Q) and model (R) covariances using maximum likelihood. 4)Smooth anomaly components using the optimized KS.5)Compute simple average of climatology components6)Add smoothed anomaly and averaged climatology back together to form the new soil moisture time series.

In principle this provides normalized innovations that are independent with variance of unity. Additionally, the innovations should have no serial correlation.

Minimize filter innovation log likelihood with respect to R, and Q to initialize:

Tmin

Tmax

Daily Maximum and Minimum Land Surface Air Temperature from Daily Maximum and Minimum Land Surface Air Temperature from AMSR-E: Comparison with AIRS/AMSU temperature productAMSR-E: Comparison with AIRS/AMSU temperature product

(a) Mean latitudinal distribution of AMSR-E and AIRS/AMSU surface air temperatures. AMSR-E algorithm uses 18.7 and 23.8 H /V, asc./desc. Tb to retrieve temperatures by first estimating and removing atmospheric water vapor, vegetation optical depth, and open water fraction effects. (b) Corresponding maps of mean annual air temperatures from AMSR-E. Objective: Derive global daily surface air temperatures with sufficient accuracy to drive global hydrological and ecological process models.

Annual Means

T [°C]

AM PMAMSR-EAIRS

(a) (b)

Jones, L.A., C.R. Ferguson, J.S. Kimball, K. Zhang, S.K. Chan, K.C. McDonald, E.G. Njoku, and E.F. Wood, 2010. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE JSTARS 3(1), 111-123.

-5 10 25 400

10

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30

40

50

60

70

80

90

Lat

itu

de

N

Temperature [C]

0 2 4 6 8 10Temp. Stdev.

AM PMAMSR-EAIRS

V [mm]

AM

PM

Vertically integrated atmospheric water vapor from AMSR-E: Vertically integrated atmospheric water vapor from AMSR-E: Comparison with AIRS (880 mb) standard mixing ratioComparison with AIRS (880 mb) standard mixing ratio

Annual Means

0 20 40 600

10

20

30

40

50

60

70

80

90

V [mm]

Lat

itu

de

N

0 5 10 15

Mix. Ratio [kg]

0 10 20V [mm]

0 2 4 6

Mix. Ratio Stdev. [kg]

AM PMAMSR-EAIRS

(a) latitudinal distribution of mean annual integrated atmospheric water vapor (V) from AMSR-E and the AIRS lowest layer (880 mb) standard mixing ratio. (b) Corresponding maps of mean annual V from AMSR-E are also presented, where grey regions indicate dense vegetation where retrievals are not possible because of low polarization ratio. Daily atmospheric water vapor is derived as a bi-product of the AMSR-E surface air temperature retrievals and is useful for global water and energy cycle studies. Future research will include more detailed assessment of retrieval accuracy using radiosondes and the AIRS integrated water vapor product.

(a) (b)