mapping terrestrial water and carbon fluxes using amsr-e and modis john kimball 1,2 1 numerical...
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![Page 1: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/1.jpg)
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
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• 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
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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
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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
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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).
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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)
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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
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Estimating ET from MODIS-AMSR-E Inputs
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Satellite Based Daily ET Algorithm Flow Chart
Source: Mu, Q. et al., 2007. Rem. Sens. Environ. 111.
MODIS
GMAO
ModelInputs
AMSR-E
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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
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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).
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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)
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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
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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).
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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
Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).
NSA-OBS (ENLF) Barrow (Tundra) Lethbridge (Grassland)
Mean Annual ET
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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.
Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).
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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.
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Back-up Slides
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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
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
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.
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
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
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
0 - WAT1 - ENLF5 - MXF7 - OSB8 - WSV10 - GRS13 - CRP13 - URB16 - BRNStation
ATQ BRO UPD
IVO
IARC LTH OAS OBS
HPV
0 - WAT1 - ENLF5 - MXF7 - OSB8 - WSV10 - GRS13 - CRP13 - URB16 - BRNStation
0 - WAT1 - ENLF5 - MXF7 - OSB8 - WSV10 - GRS13 - CRP13 - URB16 - BRNStation
ATQ BRO UPD
IVO
IARC LTH OAS OBS
HPV
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Source: Kimball et al., 2008. TGRS (In press)
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
BRO ATQ UPD HPV IVO IARC OBS OAS LTH
BGC TCF Tower
Rtot (g C m-2 yr-1)
-400
-300
-200
-100
0
100
BRO ATQ UPD HPV IVO IARC OBS OAS LTH
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
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
BRO ATQ UPD HPV IVO IARC OBS OAS LTH
BGC TCF Tower
Rtot (g C m-2 yr-1)
-400
-300
-200
-100
0
100
BRO ATQ UPD HPV IVO IARC OBS OAS LTH
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
-400
-300
-200
-100
0
100
BRO ATQ UPD HPV IVO IARC OBS OAS LTH
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
Carbon Model Results Comparison over Tower Sites
![Page 22: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/22.jpg)
Source: Kimball et al., 2008. TGRS (In press)
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%)
![Page 23: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/23.jpg)
AM
SR
-E6.
9 G
Hz
![Page 24: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/24.jpg)
19-Feb 10-Apr 30-May 19-J ul 07-Sep 27-Oct0
50
100
Date J un- 2002 to Dec- 2004
SM
[%
Sat
]
19-Feb 10-Apr 30-May 19-J ul 07-Sep 27-Oct0
50
100
Date J un- 2002 to Dec- 2004
SM [
%Sa
t]
19-Feb 10-Apr 30-May 19-J ul 07-Sep 27-Oct0
50
100
Date J un- 2002 to Dec- 2004
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
![Page 25: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/25.jpg)
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)
![Page 26: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/26.jpg)
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
Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).
![Page 27: Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball 1,2 1 Numerical Terradynamic Simulation Group, University of Montana, USA](https://reader035.vdocuments.us/reader035/viewer/2022070411/56649f4d5503460f94c6d4dd/html5/thumbnails/27.jpg)
Source: Kimball et al., 2008. TGRS (In press)
MODIS-AMSR-E Estimated Surface Soil Organic Carbon
(≤10cm depth, 2002-2004)
0
2000
4000
6000
8000
10000
12000
BRO ATQ UPD HPV IVO IARC OBS OAS LTH
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