Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages)

Girotto, Manuela

NASA Technical Reports Server

Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Improving Soil Moisture Estimation through 
the Joint Assimilation of SMOS and GRACE 
Satellite Observations
George Mason University, Department of Civil, Environmental, and Infrastructure Engineering
March 28th 2018
manuela.girotto@nasa.gov
Manuela Girotto
Manuela Girotto
https://ntrs.nasa.gov/search.jsp?R=20180002232 2019-08-29T17:44:44+00:00Z
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Outline
• Introduction & Motivations
• GRACE-DA (Downscaling GRACE Observations)
• SMOS(SMAP)-DA
• Joint Assimilation of SMOS+GRACE
• Conclusions & Future Directions
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Importance of Soil Moisture and Groundwater 
Agricultural Productivity
Weather & Climate Forecasts • Enhance weather 
and climate forecast 
skills
• Improve agricultural 
practices
• Improve flood 
prediction and 
drought monitoring  
• Economic impacts
• Link between water, 
energy, carbon at the 
land surface
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Importance of Soil Moisture and Groundwater 
A look at these from Space? Soil Moisture  SMOS/SMAP
Groundwater  GRACE [?]
Soil Moisture (SM) vs. Groundwater (GW):
SM smaller volumes & more temporally dynamic than GW
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Soil Moisture From Space
Advantages:
• Tb (L-band,1.4GHz) depends on soil
moisture
• Frequent observations ( e.g., global 
coverage every 2-3 days) 
• Good horizontal resolution (40km)  
Disadvantages:
• Only sensitive to soil moisture of 
surface layer (i.e., ~<5cm)
 L-band (active)/passive
 Launch: 31 Jan 2015
 (3)-40 km resolution
 L-band at multiple incidence angles
 Lauched: Nov. 2009
 ~40 km resolution
Soil Moisture and Ocean
Salinity (SMOS) Mission
Soil Moisture Active Passive (SMAP)
What about rootzone
and groundwater?
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Groundwater from Space: GRACE?
• Gravity = f(mass)
• Gravity varies in space (e.g., mountains = more mass)
• Gravity can be measured with two satellite one running
after the other [range-rate observations] GRACE!
ADD VIDEO OF GRACE 
SATELLITE APPROACHING 
THE MOUNTAINS ?? 
BART
GRACE = Gravity Recovery and Climate Experiment
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Groundwater from Space: GRACE?
• Gravity varies in time
• Water changes the 
Earth’s mass
• Mass changes the 
gravity field (in space
and time)
• GRACE observations: 
monthly TWS
anomalies 
Gravity (GRACE) can monitor where the water is now and how it is changing over time
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Groundwater from Space: GRACE?
TWS = Terrestrial Water Storage
[sum of groundwater, unsaturated soil moisture 
profile, snow, vegetation storage] 
Advantages:
• Unique Mission: can see beyond the surface
Applications:
• Ice Melt Loss [e.g., Antarctica & Greenland]
• Droughts [e.g., Texas, California]
• Groundwater Depletion [e.g., India]
• Sea Level Rise
 Scales used for global mass balances
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Groundwater from Space: GRACE?
Disadvantages:
• Column integrated [no partitioning into storages]
• Coarse horizontal resolution [300-400 km] 
• Coarse temporal resolution [monthly] 
• Strong spatial error correlations
Scales used for global mass balances
Scales that are more useful for hydrological 
applications 
?
Downscaling:
• Horizontal
• Temporal
• Vertical
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Groundwater from Space: GRACE?
Disadvantages:
• Column integrated [no partitioning into storages]
• Coarse horizontal resolution [300-400 km] 
• Coarse temporal resolution [monthly] 
• Strong spatial error correlations
Scales used for global mass balances
Scales that are more useful for hydrological 
applications 
Downscaling:
• Horizontal
• Temporal
• Vertical
DATA 
ASSIMILATION
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
• Introduction & Motivations
• GRACE-DA (Downscaling GRACE Observations)
• SMOS(SMAP)-DA
• Joint Assimilation of SMOS+GRACE
• Conclusions & Future Directions
Outline
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Data assimilation (DA)
Phenomena/Event Observations, Data Collections 
Model 
Data Assimilation
Observe
Obs. & 
Obs. errorsPredict
The Key Idea of DA:
• Estimates of a Specific 
Phenomena can be obtained 
form Model & Observations
• Neither are perfect 
• Use them in combination to 
optimize the estimates
Reinitialize
Cost function 
min
Model forecast
& errors
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Data assimilation (DA) for hydrology 
Observe
Model forecast
& errors
Obs. & 
Obs. errorsPredict
Nature
Model
• Satellite Remote Sensing 
• Insitu Observations 
• Airborne Observations
Passive Active
Data Assimilation
Cost function 
min
Reinitialize
Observations
The Key Idea of DA:
• Estimates of a Specific 
Phenomena can be obtained 
form Model & Observations
• Neither are perfect 
• Use them in combination to 
optimize the estimates
Apply DA using SMOS and 
GRACE observations to 
estimate soil moisture profile
Phenomena/Event 
Model 
Observations, Data 
Collections 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE data assimilation (DA) as a downscaling approach
Catchment Land Surface Model (CLSM)
• “High” spatial and temporal resolutions
- 36 km (vs. 300-400 km)
- Hourly/daily (vs. monthly)
• MERRA (&MERRA-2) forcings
Koster et al., (2000)
• Soil Moisture Profile:
[1] catdef (i.e., groundwater)
[2] rzexc
[3] srfexc
• Other water storages:
[4-6] snow [7] canopy 
* Note: missing 
lakes and river storages
Modeled
(predicted)  TWS
DA
Take advantage of the model 
structures to downscale GRACE 
observations 
f ([1], [2], [3], [4-6], [7])
Observed 
(GRACE) TWS [1], [2], [3], [4-6], [7]
(Coarse scales) (model, fine scales)
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE DA: Two-Steps Ensemble Kalman Filter
[1] Conduct 1 month forecast 
ensemble integration without 
assimilation
[2] Calculate model terrestrial 
water storage (TWS) observation 
prediction (space and temporal 
aggregation)
[3] Calculate the increments via 
ensemble Kalman filter analysis
[4] Rewind and apply increments
repeat from [1] for the next 
month. 
Girotto et al., (2016) WRR
How to compute analysis 
for a monthly-averaged
observations? 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE DA: Temporal Aggregation and Downscaling
• Calculation of the increment as an average (i.e., 
“monthly increment”) 
• Application of the increment as an initial condition at 
the beginning of the month
• Downscaling the observed TWS from monthly to 
model temporal resolution (i.e., daily)
• Day-to-day variability [largest in surface soil moisture] 
DA should better represent the monthly signature of 
the assimilated GRACE-TWS observations
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE DA: Vertical Downscaling
• Catdef dominates profile increments 
(i.e., largest GRACE-DA impact in groundwater)
Use GRACE-DA to update catdef (i.e., the groundwater only!) 
Soil Moisture Profile:
[1] catdef
(i.e., groundwater 
level)
[2] rzexc
[3] srfexc
Other water storages:
[4-6] snow [7] canopy 
Koster et al., 
(2000)
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE DA: Vertical & Horizontal Downscaling
Scales used for global 
mass balances 
(~300-400 km)
DATA 
ASSIMILATION
Scales that are more useful for hydrological applications (36 km) 
• Horizontally 
downscaled TWS 
• Typical monthly 
increments:
srfexc =   0.63 mm
rzexc =   0.54 mm
catdef = 15.30 mm
• Largest impact in 
(catdef) groundwater
(residence time?)
*Scaling the observations prior to DA: 
Unbiased observations to match model 
climatology (long-term mean and standard 
deviation)
TYPICAL MONTHLY ABSOLUTE INCREMENTS (2003-2015)
Girotto et al., (2016) WRR
GRACE TWS observations
Obs – Forecasts TWS [z- M(x)]
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE Data Assimilation: Validation 
GRACE-DA
• Improves groundwater estimates
• Mixed results for root-zone and surface soil moisture 
(Short memory? Small increments?)
 Add soil moisture (SMOS/SMAP)?
Soil Moisture:
- 157 SCAN (Soil and Climate Analysis Network)
- 95 USCRN (U.S. Climate Reference Network)
- 4 Cal/Val USDA sites
• Surface (0-5 cm)
• Rootzone (0-100 cm)
Groundwater:
- 136 USGS (Unconfined aquifer only)
Statistical Methods:
Skill: Anomalies Correlations
Monthly values Jan. 2003 - Dec. 2013
∆R = RDA – ROL [ BLUE = DA better than OL ]Bulk Statistics
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
• Introduction & Motivations
• GRACE-DA (Downscaling GRACE Observations)
• SMOS(SMAP)-DA
• Joint Assimilation of SMOS+GRACE
• Conclusions & Future Directions
Outline
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
SMOS(SMAP) to help with surface soil moisture?
De Lannoy and Reichle 2016
SMOS/SMAP Brightness Temperature (Tb) Models
• Catchment Land Surface Model
 Estimate surface soil moisture, temperature
• Radiative Transfer Model (De Lannoy et al., 2013)
 Estimate Tb [e.g., to compare with observed Tb]
Data Assimilation
For one location k, one time step i, one ensemble member j:
Soil moisture, temperature (signs -: forecasts, +analysis) 
Kalman gain
Tb SMOS/SMAP observations
Tb forecast in observation space
Continuous estimates of 
surface, rootzone, soil 
moisture (+ others)
Every 3-hrs, 9 km  (SMAP_L4) 
- 36 km (this presentation)
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
SMOS(SMAP) to help with surface soil moisture?
De Lannoy and Reichle 2016
Differences in satellite-observed and simulated brightness 
temperatures (Tb) result in updates to model variables:
• Surface and rootzone soil moisture
• Soil temperature
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
SMOS(SMAP) to help with surface soil moisture?
SMOS-DA
• Beneficial for surface and root zone soil moisture 
• But has degraded groundwater
What if we incorporate both GRACE+SMOS 
observations together?
∆R = RDA – ROL [ BLUE = DA better than OL ]Bulk StatisticsSoil Moisture:- 157 SCAN (Soil and Climate Analysis Network)
- 95 USCRN (U.S. Climate Reference Network)
- 4 Cal/Val USDA sites
• Surface (0-5 cm)
• Rootzone (0-100 cm)
Groundwater:
- 136 USGS (Unconfined aquifer only)
Statistical Methods:
Skill: Anomalies Correlations
Monthly values Jan. 2003 - Dec. 2013
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE Data Assimilation: Validation 
GRACE-DA
• Improves groundwater estimates
• Mixed results for root-zone and surface soil moisture 
(Short memory? Small increments?)
 Add soil moisture (SMOS/SMAP)?
∆R = RDA – ROL [ BLUE = DA better than OL ]Bulk StatisticsSoil Moisture:- 157 SCAN (Soil and Climate Analysis Network)
- 95 USCRN (U.S. Climate Reference Network)
- 4 Cal/Val USDA sites
• Surface (0-5 cm)
• Rootzone (0-100 cm)
Groundwater:
- 136 USGS (Unconfined aquifer only)
Statistical Methods:
Skill: Anomalies Correlations
Monthly values Jan. 2003 - Dec. 2013
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
• Introduction & Motivations
• GRACE-DA (Downscaling GRACE Observations)
• SMOS(SMAP)-DA
• Joint Assimilation of SMOS+GRACE
• Conclusions & Future Directions
Outline
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Method
Girotto et al., (in prep.)
What if we incorporate both GRACE+SMOS observations together?
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Vertical Structure of the Updates 
Girotto et al., (in prep.)
• The uncertainty of top (surface) water storages is mostly reduced because of the assimilation of SMOS 
• The uncertainty of bottom water storages is mostly reduced because of the assimilation of GRACE 
• The combination of the two observations keeps the uncertainty reduction on both surface and deeper storages.
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Method
Girotto et al., (in prep.)
GRACE DA
SMOS DA
• Example loc in Idaho
• GRACE DA only 
increments in catdef
(i.e., groundwater)
• GRACE DA calculates 
year-round increments. 
• SMOS DA increments 
only for warm months 
(i.e., no frozen soil)
• Srfexc & rzexc
increments agree in 
terms of directions 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Method
Girotto et al., (in prep.)
Joint SMOS + GRACE DA
• catdef increments 
similar to GRACE DA
• srfexc & rzexc incr. 
similar to  SMOS DA
 Characteristics of the 
univariate assimilations 
are maintained in the joint 
system  
• Anti-correlation 
between increments 
brought about GRACE 
& SMOS
 Fighting observations? 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Validation 
Girotto et al., (in prep.)
Best 
estimates 
when both 
SMOS & 
GRACE 
obs. are 
assimilated! 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Validation 
GRACE DA
• Improves groundwater estimates
• Mixed results for root-zone and surface soil 
moisture 
SMOS DA 
• Improves surface and root zone soil moisture 
• It degrades groundwater
SMOS+GRACE DA 
• Improves surface and root zone soil moisture 
• it maintains high skills vs. TWS 
• It overcomes the degradation of groundwater
Girotto et al., (in prep.)
MERGING SMOS+GRACE LEAD TO THE BEST RESULTS! 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
• Introduction & Motivations
• GRACE-DA (Downscaling GRACE Observations)
• SMOS(SMAP)-DA
• Joint Assimilation of SMOS+GRACE
• Conclusions & Future Directions
Outline
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Soil Moisture Profile Estimates
GRACE DA as a Downscaling Method 
• Vertical: [from TWS to the various water storage 
compartments (e.g., groundwater, etc.)]
• Horizontal: [from 300-400 km to 36 km increments]
• Temporal: [from monthly to daily]
Impacts onto the Vertical Profile
• GRACE, SMOS, and the joint assimilations 
decrease model uncertainties
• GRACE affects deepest moisture storages
• SMOS affects shallower moisture storages
• There is an anti-correlation between increments 
brought about the GRACE and SMOS
GRACE DA
• GRACE DA primarily affects groundwater and has 
smaller impacts on soil moisture
• GRACE DA leads to improve groundwater 
SMOS DA 
• SMOS DA is mostly beneficial to improve surface 
soil moisture
• SMOS DA marginally improves rootzone soil 
moisture
• SMOS DA leads to minimal changes in the 
groundwater 
Joint SMOS+GRACE DA 
• The entire soil moisture profile is improved when 
both SMOS & GRACE observations are used jointly  
The best hydrology can be achieved for when both observation types are assimilated jointly
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Thanks for your attention!
Improving Soil Moisture Estimation through the Joint 
Assimilation of SMOS and GRACE Satellite Observations
manuela.girotto@nasa.gov
Manuela Girotto
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
Outline
• Introduction & Motivations
• Downscaling GRACE Observations (GRACE-DA)
• GRACE-DA & Anthropogenic Hydrological Processes
• Conclusions & Future Directions
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Vertical Structure of the Updates 
Girotto et al., (in prep.)
Grab this from the science snapshot?
Key points
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE DA: Vertical & Spatial downscaling
• Data Assimilation is better than Open Loop (model only) at this location 
Impact of TWS to the single storages (e.g., rootzone soil moisture)
DA
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE+SMOS Data Assimilation: Validation 
Girotto et al., (in prep.)
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
National Aeronautics and Space Administration
GRACE Data Assimilation: Trends and Anomalies in TWS
• GRACE DA fails to adjust for dry conditions [2011-2016]  known (model) depth to bedrock issue! 
• Improved anomalies agreement between assimilation and observed TWS (and GW) 
Improved 
anomalies 
correlation
TW
S


Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

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