NASA Soil Moisture
Active Passive (SMAP)
Mission Formulation
IGARSS’11
Session WE1.T03.1
Paper #3178
Dara Entekhabi
Eni Njoku
Peggy O'Neill
Kent Kellogg
Jared Entin
(MIT)
(JPL Caltech/NASA)
(GSFC/NASA)
(JPL Caltech/NASA)
(NASA HQ)
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Talk Outline
1. Traceability of SMAP Basic and Applied Science Applications to the
NRC Earth Science Decadal Survey
2. Key Upcoming Milestones and Activities
3. Latest on Data Products and Latencies
4. Key Algorithm Development and Testing Activities
5. Community Engagement With Project Elements Through Working Groups
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Project Milestones and Upcoming Activities
2007 US National Research
Council Report: “Earth Science
and Applications from Space:
National Imperatives for the Next
Decade and Beyond”
Tier 1: 2010–2013 Launch
Soil Moisture Active Passive (SMAP)
ICESAT II
DESDynI
CLARREO
Tier 2: 2013–2016 Launch
•
•
•
•
Feb 2008: NASA announces start of SMAP project
SMAP is a directed-mission with heritage from HYDROS
PDR Oct 10-12, 2011 Followed by KDP-C and
Implementation Phase
Major Ongoing Hardware Fabrication and Testing
SWOT
HYSPIRI
ASCENDS
GEO-CAFE
ACE
Tier 3: 2016–2020 Launch
LIST
Ongoing and Upcoming:
- Focus on Working With Applications Users
- Independent ATBD Peer Review (Nov+ 2011)
- SMEX’12 Airborne Experiment in US and Canada
- Algorithm Testbed: End-to-End Simulation
- in situ Testbed Cal/Val Instruments Testing
PATH
GRACE-II
SCLP
GACM
3D-WINDS
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Pathways of Soil Moisture Influence
on Weather and Climate
May 10 Dry soil, clear, mild winds. (LE≈H)
Dry
Surface
May 18 90 mm Rain
Deep
Mixing up
to 1.5 km
Altitude
May 20 Moist soil, clear, mild winds. (LE>H)
Moist
Surface
Dry Soil
5°C
Moist Soil
CASES’97 Field Experiment,
BAMS, 81(4), 2000.
Shallow
Mixing to
1.0 km
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Key Determinants of Land Evaporation
Latent heat flux
(evaporation) links the
water, energy, and carbon
cycles at the surface.
E

Ep
Closure relationship, yet
virtually unknown.
Lack of knowledge of soil
moisture control on surface
fluxes causes uncertainty in
weather and climate
models.
Source: Cahill et al., J. Appl. Met., 38
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
What Do We Do Today?
NOAH
Dirmeyer et al., J. Hydromet., 7,
1177-1198, 2006
CLM
Atmospheric model
representations of this
function are
essentially “guesses”,
given scarcity of soil
moisture and
evaporation data.
National Aeronautics and
Space Administration
Science Requirements
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
DS Objective
Weather Forecast
Climate Prediction
Drought and
Agriculture
Monitoring
Flood Forecast
Improvements
Human Health
Boreal Carbon
Application
Initialization of Numerical Weather Prediction (NWP)
Boundary and Initial Conditions for Seasonal Climate Prediction
Models
Testing Land Surface Models in General Circulation Models
Seasonal Precipitation Prediction
Regional Drought Monitoring
Crop Outlook
River Forecast Model Initialization
Flash Flood Guidance (FFG)
NWP Initialization for Precipitation Forecast
Seasonal Heat Stress Outlook
Near-Term Air Temperature and Heat Stress Forecast
Disease Vector Seasonal Outlook
Disease Vector Near-Term Forecast (NWP)
Freeze/Thaw Date
Science Requirement
Hydrometeorology
Hydroclimatology
Hydroclimatology
Hydrometeorology
Hydroclimatology
Hydrometeorology
Hydroclimatology
Hydrometeorology
Freeze/Thaw State
HydroMeteorology
HydroClimatology
Carbon
Cycle
Resolution
4–15 km
50–100 km
Refresh Rate
2–3 days
Accuracy
4–6% **
Requirement
(*) % classification accuracy (binary Freeze/Thaw)
(**) [cm3 cm-3] volumetric water content, 1-sigma
(1)North
of 45N latitude
Baseline Mission
Soil
Moisture
Freeze/Thaw
1–10 km
10 km
3 km
3–4 days
2–3 days(1)
3 days
2 days(1)
4–6%**
80–70%*
4%**
80%*
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Hydrometeorology Applications: NWP
Trends in Short-Term Weather (0-14 Days) NWP Resolution
SMAP
Sources:
Global Forecast/Analysis System Bulletins
http://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html
The ECMWF Forecasting System Since 1979
http://ecmwf.int/products/forecasts/guide/The_general_circulation_model.html
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Operational Flood and Drought Applications
Current: Empirical Soil Moisture Indices Based on Rainfall and Air Temperature
( By Counties >40 km and Climate Divisions >55 km )
Future: SMAP Soil Moisture Direct Observations of Soil Moisture at 10 km
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
SMAP Mission Concept
• L-band Unfocused SAR and Radiometer
System, Offset-Fed 6 m Light-Weight
Deployable Mesh Reflector. Shared Feed For

1.26 GHz Radar at 1-3 km (HH, VV, HV)
(30% Nadir Gap)

1.4 GHz Polarimetric Radiometer at 40 km
(H, V, 3rd & 4th Stokes)
• Conical Scan at Fixed Look Angle
• Wide 1000 km Swath With 2-3 Days Revisit
• Sun-Synchronous 6am/6pm Orbit (680 km)
• Launch 2014
• Mission Duration 3 Years
National Aeronautics and
Space Administration
Data Products
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Product
Description
Resolution
Latency
L1A_TB
Radiometer Data in Time-Order
-
12 hrs
L1A_S0
Radar Data in Time-Order
-
12 hrs
L1B_TB
Radiometer TB in Time-Order
36x47 km
12 hrs
L1B_S0_LoRes
Low Resolution Radar σo in Time-Order
5x30 km
12 hrs
L1C_S0_HiRes
High Resolution Radar σo in Half-Orbits
1-3 km
12 hrs
L1C_TB
Radiometer TB in Half-Orbits
36 km
12 hrs
L2_SM_A
Soil Moisture (Radar)
3 km
24 hrs
L2_SM_P
Soil Moisture (Radiometer)
36 km
24 hrs
L2_SM_A/P
Soil Moisture (Radar+Radiometer)
9 km
24 hrs
L3_F/T_A
Freeze/Thaw State
3 km
50 hrs
L3_SM_A
Soil Moisture (Radar)
3 km
50 hrs
L3_SM_P
Soil Moisture (Radiometer)
36 km
50 hrs
L3_SM_A/P
Soil Moisture (Radar+Radiometer)
9 km
50 hrs
L4_SM
Soil Moisture (Surface and Root Zone )
9 km
7 days
L4_C
Carbon Net Ecosystem Exchange (NEE)
9 km
14 days
Instrument Data
Science Data
(Half-Orbit)
Science Data
(Daily Composite)
Science
Value-Added
SMAP is Taking Aggressive Hardware & Softwate Measures to Detect & Partially Mitigate RFI
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
L-band Active/Passive Assessment
 Soil Moisture Retrieval Algorithms Build
on Heritage of Microwave Modeling and
Field Experiments
MacHydro’90, Monsoon’91, Washita92,
Washita94, SGP97, SGP99, SMEX02,
SMEX03, SMEX04, SMEX05, CLASIC,
SMAPVEX08, CanEx10
 Radiometer - High Accuracy (Less Influenced by
Roughness and Vegetation) but
Coarser Resolution (40 km)
 Radar - High Spatial Resolution (1-3 km) but More
Sensitive to Surface Roughness and Vegetation
Combined Radar-Radiometer Product Provides
Blend of Measurements for Intermediate Resolution
and Intermediate Accuracy
L2_SM_AP
Radar-Radiometer Algorithm
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Temporal Changes in TB and σpp are Related.
Based on PALS Observations From:
Relationship Parameter β is Estimated at
SGP99, SMEX02, CLASIC and SMAPVEX08
Radiometer-Scale Using Successive Overpasses.
  Slope TB , pp C
Heterogeneity in Vegetation and Roughness
Conditions Estimated by Sensitivities Γ in
Radar HV Cross-Pol:
TB-Disaggregation Algorithm is:
TB p M j   TB p C  


8

hv
RVI





2

hh
vv
hv
  pp M j    pp C  
 
   pq M j    pq C 





TB( Mj ) is Used to Retrieve Soil
Moisture at 9 km


  Slope  pp M j , pq M j C
8

hv
RVI





2

hh vv
hv
National Aeronautics and
Space Administration
Active-Passive Algorithm Performance
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
0.4
Minimum Performance Algorithm
3 cm-3]
3
RMSE:
0.055
[cm
RMSE:
0.055
[cm 3/cm
]
Baseline Algorithm [cm3/cm3]
Minimum Performance [cm3/cm3]
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
3
3
Average of Field Measurements [cm /cm ]
Active-Passive Algorithm
3
3 cm-3]
RMSE:
0.0330.033
[cm 3/cm[cm
]
RMSE:
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
3
3
Average of Field Measurements [cm /cm ]
SGP99, SMEX02, CLASIC and SMAPVEX08
WE2.T03.2
Paper #: 3398
Title: Evaluation of the SMAPCombined Radar-Radiometer Soil Moisture Algorithm
Authors: N. Das, D. Entekhabi, S. Chan, S. Kim, E. Njoku, R. Dunbar, J.C. Shi
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
SMAP Applications Activities
•
Using the SMAP Testbed to Develop Value-Added Products in the Simulation
Environment
•
Making Available Basic SMAP Products with Moderate Latencies
•
Establishing a Community of Early-Adopters Through a Competitive,
Peer-Reviewed NASA Announcement of Opportunity
•
Steering End-Users to NASA Applied Sciences Program (ASP) Solicitations With
Specific Mention of SMAP Product Applications
•
2nd AppWG Workshop in DC October 11-12, 2011
WE1.T03.2
Paper #2906
Title: The Soil Moisture Active Passive (SMAP) Applications Aactivity
Authors: M. Brown, S. Moran, V. Escobar, D. Entekhabi, P. O'Neill, E. Njoku
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
SMAP Algorithm Testbed
Simulated products generated with prototype algorithms on the SDS Testbed
L1C_S0_Hi-Res Radar
Backscatter Product (1-3 km)
L1C_TB Radiometer
Brightness Temperature Product (36km)
TB (K)
L3_SM_A Radar
Soil Moisture Product (3 km)
L2_SM_P Radiometer
Soil Moisture Product (36 km)
L2_SM_AP Combined
Soil Moisture Product (9 km)
WE2.T03.1
Paper #2069
Title: Utilization of ancillary data sets for SMAP Algorithm Development and Product Generation
Authors: P. O'Neill, E. Podest, E. Njoku
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
SMAP Working Groups
Working Groups Have Been Established to Facilitate Broad Science Participation
in the SMAP Project. The Working Groups Communicate via Workshops, E-Mail
and at Conferences and Other Venues.
Currently There are Four Working Groups:
1. Applications Working Group (AppWG)
2nd Workshop in Oct. 2011; Early-Adopter DCL
2. Calibration & Validation Working Group (CVWG)
2nd Workshop in May 2011; Core-Sites DCL
3. Algorithms Working Group (AWG)
4. Radio-Frequency Interference Working Group (RFIWG)
http://smap.jpl.nasa.gov/science/wgroups/
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Back-Up Slides
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Mission Science Objective
Global mapping of Soil Moisture and Freeze/Thaw state to:

Understand Processes That Link the Terrestrial Water, Energy & Carbon Cycles

Estimate Global Water and Energy Fluxes at the Land Surface
Quantify Net Carbon Flux in Boreal Landscapes
Enhance Weather and Climate Forecast Skill
Develop Improved Flood Prediction and Drought Monitoring Capability



Primary Controls on
Land Evaporation and
Biosphere Primary
Productivity
Soil
Moisture
Freeze/
Thaw
Radiation