The Soil Moisture Active/Passive (SMAP) Mission: Monitoring Soil Moisture and

Freeze/Thaw State

John Kimball,

Global Vegetation Workshop, June 16-19 2009

NTSG, The University of Montana

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SMAP Science ObjectivesSMAP Science Objectives

Primary Science Objectives:

• Global, high-resolution mapping of soil moisture and its freeze/thaw state to: Link terrestrial water, energy and carbon

cycle processes

Estimate global water and energy fluxes at the land surface

Quantify net carbon flux in boreal landscapes

Extend weather and climate forecast skill

Develop improved flood and drought prediction capability

SMAP is one of the four first-tier missions recommended by the NRC Earth Science Decadal Survey Report

Soil moisture and freeze/thaw state are major constraints to land-atmosphere

energy, water & carbon exchange

Source: Nemani et al. 2003. Science 300

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SMAP Instrument & Mission OverviewSMAP Instrument & Mission Overview

• Science Measurements Soil moisture and freeze/thaw state

• Orbit: Sun-synchronous, 6 am/6pm equatorial crossing 670 km altitude

• Instruments: L-band (1.26 GHz) radar

Polarization: HH, VV, HV SAR mode: 1-3 km resolution (degrades over center

30% of swath) Real-aperture mode: 30 x 6 km resolution

L-band (1.4 GHz) radiometer Polarization: V, H, U 40 km resolution

Instrument antenna (shared by radar & radiometer) 6-m diameter deployable mesh antenna Conical scan at 14.6 rpm incidence angle: 40 degrees

Creating Contiguous 1000 km swath Swath and orbit enable 2-3 day revisit

• Mission Ops duration: 2013 launch; 3 year baseline

SMAP has significant heritage from Hydros ESSP mission concept and Phase A studies

Sample SMAP Coverage Plot

Radiometer,Low-Res Radar

High-Res Radar

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““Link Terrestrial Water, Energy and Carbon Link Terrestrial Water, Energy and Carbon Cycle Processes”Cycle Processes”

Do Climate Models Correctly Represent the Land surface Control on Water and Energy Fluxes?

What Are the Regional Water Cycle Impacts of Climate Variability?

Landscape Freeze/Thaw Dynamics Constrain the Boreal Carbon Balance

Water and Energy Cycle

Soil Moisture Controls the Rate of Continental Water and Energy Cycles

Carbon Cycle

Are Northern Land Masses Sources or Sinks for Atmospheric Carbon?

Surface Soil Moisture [% Volume] Measured by L-Band Radiometer

Campbell Yolo Clay Field Experiment Site, California

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““Estimate Global Water and Energy Fluxes Estimate Global Water and Energy Fluxes at the Land Surface”at the Land Surface”

Li et al., (2007): Evaluation of IPCC AR4 soil moisture simulations for the second half of the twentieth century, Journal of Geophysical Research, 112.

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ΔSMΔT

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Relative soil moisture changes (%) in IPCC models for scenario from 1960-1999 to 2060-2099

SMAP soil moisture observations will help constrain model parameterizations of surface fluxes and improve model performance

• IPCC models currently exhibit large differences in soil moisture trends under simulated climate change scenarios

• Projections of summer soil moisture change (ΔSM) show disagreements in Sign among IPCC AR4 models

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““Quantify Net Carbon Flux in Boreal Quantify Net Carbon Flux in Boreal Landscapes”Landscapes”

Primary thaw day (DOY)

McDonald et al. (2004): Variability in springtime thaw in the terrestrial high latitudes: Monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interactions 8(20), 1-23.

SMAP will provide important information on environmental constraints to land-atmosphere carbon source/sink dynamics. It will provide more than 8-fold increase in spatial resolution over existing moderate resolution microwave sensors.

(r = 0.550; P = 0.042)

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SSM/I thaw date CO2 Spring drawdown

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SSM/I thaw date CO2 Spring drawdown

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Annual comparison of pan-Arctic thaw date and high latitude growing season onset inferred from atmospheric CO2 concentrations, 1988 – 2001

Mean growing season onset for 1988 – 2002 derived from coarse resolution SSM/I data

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“Extend Weather and Climate Forecast Skill”

Predictability of seasonal climate is dependent on boundary conditions such as sea surface temperature (SST) and soil moisture – Soil moisture is particularly important over continental interiors.

Difference in Summer Rainfall: 1993 (flood) minus

1988 (drought) years

Observations

Prediction driven by SST and soil moisture

Prediction driven by SST

-5 0 +5 Rainfall Difference [mm/day]

(Schubert et al., 2002)With Realistic Soil Moisture

24-Hours Ahead High-Resolution

Atmospheric Model Forecasts

Observed Rainfall0000Z to 0400Z 13/7/96(Chen et al., 2001)

Buffalo CreekBasin

High resolution soil moisture data will improve numerical weather prediction (NWP) over continents by accurately initializing land surface states

Without Realistic Soil Moisture

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““Develop Improved Flood and Drought Develop Improved Flood and Drought Prediction Capability”Prediction Capability”

“…delivery of flash-flood guidance to weather forecast offices are centrally dependent on the availability of soil moisture estimates and observations.”

“SMAP will provide realistic and reliable soil moisture observations that will potentially open a new era in drought monitoring and decision-support.”

Decadal Survey:

• Current Status: Indirect soil moisture indices are based on rainfall and air temperature (by county or ~30 km)

• SMAP Capability: Direct soil moisture measurements – global, 3-day, 10 km resolution

NOAA National Weather Service Operational Flash Flood Guidance (FFG)

Operational Drought Indices Produced by NOAA and National Drought Mitigation Center (NDMC)

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Satellite Global Biospheric Monitoring & The Problem with Clouds…Satellite Global Biospheric Monitoring & The Problem with Clouds…

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SMAP Science, Instrument and Mission RequirementsSMAP Science, Instrument and Mission Requirements

Scientific Measurement Requirements

Instrument Functional Requirements Mission Functional Requirements

Soil Moisture: ~4% volumetric accuracy in top 5 cm for vegetation water content < 5 kg m-2; Hydrometeorology at 10 km; Hydroclimatology at 40 km

L-Band Radiometer: Polarization: V, H, U; Resolution: 40 km; Relative accuracy*: 1.5 K L-Band Radar: Polarization: VV, HH, HV; Resolution: 10 km; Relative accuracy*: 0.5 dB for VV and HH Constant incidence angle** between 35 and 50

Freeze/Thaw State: Capture freeze/thaw state transitions in integrated vegetation-soil continuum with 2-day precision, at the spatial scale of landscape variability (3 km).

L-Band Radar: Polarization: HH; Resolution: 3 km; Relative accuracy*: 0.7 dB (1 dB per channel if 2 channels are used); Constant incidence angle** between 35 and 50

DAAC data archiving and distribution.

Field validation program.

Integration of data products into multisource land data assimilation.

Sample diurnal cycle at consistent time of day Global, 3-4 day revisit Boreal, 2 day revisit

Swath Width: 1000 km Minimize Faraday rotation (degradation factor at L-band)

Orbit: 670 km, circular, polar, sun-synchronous, ~6am/pm equator crossing

Observation over a minimum of three annual cycles

Minimum three-year mission life Three year baseline mission***

* Includes precision and calibration stability, and antenna effects ** Defined without regard to local topographic variation *** Includes allowance for up to 30 days post-launch observatory check-out

SMAP requirements were developed under Hydros and refined through extensive community interaction - The July ’07 NASA SMAP Science Workshop confirmed that these requirements satisfy the SMAP mission science objectives

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Baseline Science Data ProductsBaseline Science Data Products

Data Product Description

L1B_S0_LoRes Low Resolution Radar σo in Time Order

L1C_S0_HiRes High Resolution Radar σo 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

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SMAP L4_Carbon product: Land-atmosphere COSMAP L4_Carbon product: Land-atmosphere CO22 exchange exchange

• Motivation/Objectives: Quantify net C flux in boreal landscapes; reduce uncertainty regarding missing C sink on land;

• Approach: Apply a soil decomposition algorithm driven by SMAP L4_SM and GPP inputs to compute land-atmosphere CO2 exchange (NEE);

• Inputs: Daily surface (<5cm) soil moisture & T (L4_SM) & GPP (MODIS/NPP); • Outputs: NEE (primary/validated); Reco & SOC (research/optional);

• Domain: Vegetated areas encompassing boreal/arctic latitudes (≥45°N);

• Resolution: 10x10 km;

• Temporal fidelity: Daily (g C m-2 d-1);

• Latency: Initial posting 12 months post-launch, followed by 14-day latency;

• Accuracy: Commensurate with tower based CO2 Obs. (RMSE ≤ 30 g C m-2 yr-1).

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Prototype L4_C Product ExamplePrototype L4_C Product Example

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Carbon Model with AMSR and MODIS BGC Tower

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NEE for NSA-OBS Ameriflux Site

L4_C algorithm using MODIS - AMSR-E inputsBIOME-BGC simulations using local meteorologyTower CO2 eddy flux measurement results

C source (+)

C sink (-)

L4_C application using MODIS GPP (MOD17) & AMSR-E (SM & T) inputs. The graph (above) shows 2004 seasonal pattern of daily NEE for a mature boreal conifer stand from L4_C, ecosystem model and tower measurements. SMAP L4_C resolution/sampling will allow characterization of surface processes approaching scale & accuracy of tower flux measurements: ~10km resolution, daily repeat, NEE ≤ 30 g C m-2 yr-1 RMSE. NEE (g C m-2) DOY 177, 2004

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Mean Daily net CO2 Exchange (NEE)

Source: Kimball et al. 2009 TGARS 47.

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Global Biophysical Station Networks

SMAP Calibration and Validation activitiesSMAP Calibration and Validation activities

Pre-launch (2009-2013): - Development, testing & selection of baseline

algorithms;

- Development of algorithm software test-bed for algorithm testing & sensitivity studies;

- Verify algorithm sensitivity & accuracy requirements using available satellite, in situ and model based data & targeted field campaigns;

- Initialization/calibration/optimization of algorithm parameters (e.g. BPLUT, SOC pools);

Post-launch (2013-2015):

- Verify product accuracy through focused field campaigns and global observation networks;

- Model assimilation based value assessment (GMAO, TOPS, CarbonTracker);

Pre-launch L4_C Test using MODIS & AMSR-E Inputs

Kimball et al. TGARS 2009

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

Opportunities for Community Involvement

• Community workshops (Events)

• SMAP SDT Working Groups (Team): - Algorithms - Calibration & Validation - Applications