NASA Soil Moisture Active Passive (SMAP) Mission Formulation Dara Entekhabi (MIT) Eni Njoku (JPL...

NASA Soil Moisture Active Passive (SMAP)

Mission Formulation

Dara Entekhabi (MIT)Eni Njoku (JPL Caltech/NASA)Peggy O'Neill (GSFC/NASA)Kent Kellogg (JPL Caltech/NASA)Jared Entin (NASA HQ)

IGARSS’11Session WE1.T03.1

Paper #3178

National Aeronautics and Space Administration

Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, 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



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


SWOT

HYSPIRI

ASCENDS

GEO-CAFE

ACE


LIST

PATH

GRACE-II

SCLP

GACM

3D-WINDS

Project Milestones and Upcoming Activities

• 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

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



Dry Soil

Moist Soil

5°C

Dry Surface

Moist Surface

Deep Mixing up to 1.5 km Altitude

Shallow Mixing to 1.0 km

May 10 Dry soil, clear, mild winds. (LE≈H)

May 18 90 mm Rain

May 20 Moist soil, clear, mild winds. (LE>H)

Pathways of Soil Moisture Influence on Weather and Climate

CASES’97 Field Experiment, BAMS, 81(4), 2000.



Source: Cahill et al., J. Appl. Met., 38

Key Determinants of Land Evaporation

Latent heat flux (evaporation) links the water, energy, and carbon cycles at the surface.

Closure relationship, yet virtually unknown.

Lack of knowledge of soil moisture control on surface fluxes causes uncertainty in weather and climate models.

pE

E



NOAH

CLM

What Do We Do Today?

Dirmeyer et al., J. Hydromet., 7, 1177-1198, 2006

Atmospheric model representations of this function are essentially “guesses”, given scarcity of soil moisture and evaporation data.



RequirementHydro-

MeteorologyHydro-

ClimatologyCarbon Cycle

Baseline Mission

Soil Moisture

Freeze/Thaw

Resolution 4–15 km 50–100 km 1–10 km 10 km 3 km

Refresh Rate 2–3 days 3–4 days 2–3 days(1) 3 days 2 days(1)

Accuracy 4–6% ** 4–6%** 80–70%* 4%** 80%*(*) % classification accuracy (binary Freeze/Thaw) (**) [cm3 cm-3] volumetric water content, 1-sigma

Science Requirements

(1)North of 45N latitude

DS Objective Application Science RequirementWeather Forecast Initialization of Numerical Weather Prediction (NWP) Hydrometeorology

Climate PredictionBoundary and Initial Conditions for Seasonal Climate Prediction Models HydroclimatologyTesting Land Surface Models in General Circulation Models

Drought and Agriculture Monitoring

Seasonal Precipitation PredictionHydroclimatologyRegional Drought Monitoring

Crop Outlook

Flood Forecast Improvements

River Forecast Model InitializationHydrometeorology

Flash Flood Guidance (FFG)NWP Initialization for Precipitation Forecast

Human Health

Seasonal Heat Stress Outlook HydroclimatologyNear-Term Air Temperature and Heat Stress Forecast HydrometeorologyDisease Vector Seasonal Outlook HydroclimatologyDisease Vector Near-Term Forecast (NWP) Hydrometeorology

Boreal Carbon Freeze/Thaw Date Freeze/Thaw State



Sources:Global Forecast/Analysis System Bulletinshttp://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html

The ECMWF Forecasting System Since 1979http://ecmwf.int/products/forecasts/guide/The_general_circulation_model.html

Trends in Short-Term Weather (0-14 Days) NWP Resolution

Hydrometeorology Applications: NWP

SMAP



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

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





Product Description Resolution Latency

L1A_TB Radiometer Data in Time-Order - 12 hrs

Instrument Data

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 hrsScience Data (Half-Orbit)

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

Science Data (Daily Composite)

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 Science Value-AddedL4_C Carbon Net Ecosystem Exchange (NEE) 9 km 14 days

Data Products

SMAP is Taking Aggressive Hardware & Softwate Measures to Detect & Partially Mitigate RFI



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 ProvidesBlend of Measurements for Intermediate Resolutionand Intermediate Accuracy

L2_SM_AP Radar-Radiometer Algorithm

Heterogeneity in Vegetation and Roughness Conditions Estimated by Sensitivities Γ in Radar HV Cross-Pol:

Cjpqjpp MM , Slope

TB( Mj ) is Used to Retrieve Soil Moisture at 9 km

TB-Disaggregation Algorithm is:

CM

CM

CTMT

pqjpq

ppjpp

BjB pp



hvvvhh

hv

2

8RVI

hvvvhh

hv

2

8RVI

CppBT , Slope

Temporal Changes in TB and σpp are Related.

Relationship Parameter β is Estimated at Radiometer-Scale Using Successive Overpasses.

Based on PALS Observations From: SGP99, SMEX02, CLASIC and SMAPVEX08



0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

Average of Field Measurements [cm3/cm3]B

asel

ine

Alg

ori

thm

[cm

3/c

m3]

RMSE: 0.033 [cm3/cm3]

0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

Average of Field Measurements [cm3/cm3]

Min

imu

m P

erf

orm

an

ce [

cm3/c

m3] RMSE: 0.055 [cm3/cm3]

SGP99, SMEX02, CLASIC and SMAPVEX08

WE2.T03.2 Paper #: 3398Title: Evaluation of the SMAPCombined Radar-Radiometer Soil Moisture AlgorithmAuthors: N. Das, D. Entekhabi, S. Chan, S. Kim, E. Njoku, R. Dunbar, J.C. Shi

Active-Passive Algorithm Performance

Minimum Performance AlgorithmRMSE: 0.055 [cm3 cm-3]

Active-Passive AlgorithmRMSE: 0.033 [cm3 cm-3]



• 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

SMAP Applications Activities

WE1.T03.2 Paper #2906Title: The Soil Moisture Active Passive (SMAP) Applications AactivityAuthors: M. Brown, S. Moran, V. Escobar, D. Entekhabi, P. O'Neill, E. Njoku

SMAP Algorithm Testbed

TB (K)

L2_SM_AP CombinedSoil Moisture Product (9 km)

L2_SM_P Radiometer Soil Moisture Product (36 km)

L3_SM_A RadarSoil Moisture Product (3 km)

L1C_S0_Hi-Res Radar Backscatter Product (1-3 km)

L1C_TB RadiometerBrightness Temperature Product (36km)

Simulated products generated with prototype algorithms on the SDS Testbed



WE2.T03.1 Paper #2069Title: Utilization of ancillary data sets for SMAP Algorithm Development and Product Generation Authors: P. O'Neill, E. Podest, E. Njoku



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)

SMAP Working Groups

http://smap.jpl.nasa.govsmap.jpl.nasa.gov/science/wgroups/



Back-Up Slides



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

Mission Science Objective

Primary Controls on Land Evaporation and Biosphere Primary Productivity

Freeze/Thaw

Radiation

Soil Moisture

NASA Soil Moisture Active Passive (SMAP) Mission Formulation Dara Entekhabi (MIT) Eni Njoku (JPL...

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