Transcript Document

The Soil Moisture Active/Passive (SMAP)
Mission: Monitoring Soil Moisture and
Freeze/Thaw State
John Kimball,
NTSG, The University of Montana
Global Vegetation Workshop, June 16-19 2009
SMAP Science Objectives
SMAP is one of the four first-tier missions recommended by the NRC
Earth Science Decadal Survey Report
Primary Science Objectives:
• Global, high-resolution mapping of soil
moisture and its freeze/thaw state to:
Soil moisture and freeze/thaw state are
major constraints to land-atmosphere
energy, water & carbon exchange
 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
Source: Nemani et al. 2003. Science 300
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SMAP 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
Sample SMAP Coverage Plot
Radiometer,
Low-Res Radar
High-Res Radar
SMAP has significant heritage
from Hydros ESSP mission
concept and Phase A studies
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“Link Terrestrial Water, Energy and Carbon
Cycle Processes”
Soil Evaporation Normalized by
Potential Evaporation
Water and Energy Cycle
Carbon Cycle
Campbell Yolo Clay Field Experiment
Site, California
Surface Soil Moisture [% Volume]
Measured by L-Band Radiometer
Soil Moisture Controls the Rate of
Continental Water and Energy Cycles
Landscape Freeze/Thaw Dynamics
Constrain the Boreal Carbon Balance
Do Climate Models Correctly Represent the Land
surface Control on Water and Energy Fluxes?
What Are the Regional Water Cycle Impacts of
Climate Variability?
Are Northern Land Masses Sources or
Sinks for Atmospheric Carbon?
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“Estimate Global Water and Energy Fluxes
at the Land Surface”
• 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
ΔT
ΔSM
0
0
SMAP soil moisture observations will help
constrain model parameterizations of surface
fluxes and improve model performance
Li et al., (2007): Evaluation of IPCC AR4 soil moisture simulations for the second half of the twentieth
century, Journal of Geophysical Research, 112.
Relative soil moisture changes (%) in IPCC models
for scenario from 1960-1999 to 2060-2099
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“Quantify Net Carbon Flux in Boreal
Landscapes”
SMAP will provide important information on environmental constraints to landatmosphere carbon source/sink dynamics. It will provide more than 8-fold increase in
spatial resolution over existing moderate resolution microwave sensors.
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(r = 0.550; P = 0.042)
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6
4
4
2
2
0
0
-2
-2
-4
-4
-6
-6
-8
-8
1988 1990 1992 1994 1996 1998 2000
SSM/I thaw date
CO 2 drawdown anomaly (days)
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Growing season onset from
atmospheric CO2 samples
(difference from multi-year mean, days)
Annual comparison of pan-Arctic thaw date and
high latitude growing season onset inferred from
atmospheric CO2 concentrations, 1988 – 2001
Thaw day difference from
mean
multi-year
Thaw
anomaly
(days)(days)
Mean growing season onset for 1988 – 2002
derived from coarse resolution SSM/I data
CO2 Spring drawdown
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.
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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.
Prediction driven by SST
Difference in Summer
Rainfall: 1993 (flood) minus
1988 (drought) years
24-Hours Ahead
High-Resolution
Atmospheric
Model Forecasts
Without Realistic Soil Moisture
Observations
Prediction driven by SST
and soil moisture
Buffalo
Creek
Basin
Observed Rainfall
0000Z to 0400Z 13/7/96
(Chen et al., 2001)
(Schubert et al., 2002)
With Realistic Soil Moisture
-5
0
+5
Rainfall Difference [mm/day]
High resolution soil moisture data will
improve numerical weather prediction (NWP)
over continents by accurately initializing
land surface states
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“Develop Improved Flood and Drought
Prediction Capability”
Decadal
Survey:
“…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.”
NOAA National Weather Service Operational
Flash Flood Guidance (FFG)
Operational Drought Indices Produced by NOAA
and National Drought Mitigation Center (NDMC)
• 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
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Satellite Global Biospheric Monitoring & The Problem with Clouds…
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SMAP Science, Instrument and Mission Requirements
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
Scientific Measurement
Requirements
Soi l Moi sture:
~4% volum etric accuracy in top 5 cm for
vegetation water content < 5 kg m-2;
Hydrometeorology at 10 km ;
Hydroclim atology at 40 km
Freeze/Thaw State:
Capture freeze/th aw state transiti onsin
integrated vegetation-soi l continuum
with 2-day precision, at the spatialsc ale
of landscape variabili ty(3 km ).
Sampl e diurnal cycle at consistent tim e
of day
Global, 3-4 day revisit
Boreal, 2 day revisit
Observation over a minim um of three
annual cycles
Instrument Functional Requirements
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 ;
Rel ativ e accuracy*: 0.5 dB for VV and HH
Constant incidence angle** betwe
nd 5
L-Band Radar:
Polarization:HH; Resol ution: 3 km ; Relative
accuracy*: 0.7 dB (1 dB per channel if 2 channel s
are used);
Constant incidence angle** betwe
nd 5
Swath Width: 1000 km
Minim iz e Faraday rotation (degradation factor at
L-band)
Minim um three-year mission life
M ission Functional
Requirements
DAAC data archiving and
di stribution.
Fiel d validation program .
Integration of data products
into m ultis ource land data
assim ilation.
Orbit: 670 km , circular,
polar, sun-synchronous,
~6am /pm equator crossi ng
Three year baseline
m ission***
* Includes precision and calibration stability, and antenna ef f ects
** Def ined without regard to local topographic v ariation
*** Includes allowance for up to 30 days post-launch observ atorycheck-out
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Baseline 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 CO2 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 Example
Mean Daily net CO2 Exchange (NEE)
NEE for NSA-OBS Ameriflux Site
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C +Source
source (+)
2
g C m -2 d -1
1
0
-1
-Sink (-)
C sink
-2
-3
-4
1/1
2/10 3/21 4/30
6/9
7/19 8/28 10/7 11/16 12/26
Date
L4_C algorithm using MODIS
- AMSR-E inputs
BIOME-BGC
simulations
using
local meteorology
Carbon Model
with AMSR and
MODIS
BGC
Tower
Tower CO2 eddy flux measurement results
>7
<-7
4
2
NEE (g C
0
m-2)
-2
DOY 177, 2004
-4
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.
Source: Kimball et al. 2009 TGARS 47.
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SMAP Calibration and Validation activities
Pre-launch (2009-2013):
Pre-launch L4_C Test using MODIS & AMSR-E Inputs
- 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;
Kimball et al. TGARS 2009
Global Biophysical Station Networks
- 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);
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Opportunities for Community Involvement
• Community workshops (Events)
• SMAP SDT Working Groups (Team):
- Algorithms
- Calibration & Validation
- Applications
http://smap.jpl.nasa.gov/
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