Map of SSM/I derived mean timing of spring thaw for Alaska, excluding non-vegetated areas (in grey) . The timing of thaw corresponds closely with regional anomalies in annual NPP derived from the AVHRR Pathfinder record over Alaska and NW Canada. Negative anomalies relative to the long-term (1988-2000) record denote both earlier thaws and greater productivity while positive values denote the opposite response. Mean annual variability in springtime thaw is on the order of ±7 days, with corresponding impacts to annual productivity of approximately 1% per day.
Relationship between the mean annual timing of the primary spring thaw event over the pan-Arctic basin and Alaska defined by the SSM/I, and corresponding mean annual minimum atmospheric CO2 concentrations from NOAA CMDL northern (>/=50N) monitoring sites. Each year northern high latitude (>/=50N) atmospheric CO2 concentrations decline with the onset of the growing season and vegetation net primary production (NPP). The boreal growing season onset corresponds closely with the timing of the primary seasonal thaw event defined from the SSM/I. Years with relatively early thaw events and growing season onset yield generally greater annual NPP and greater CO2 drawdown; conversely, years with relatively late thaw and growing season onset yield the opposite response. Both the SSM/I thaw and CO2 time series show significant temporal trends, so the thaw and CO2 anomalies are expressed as annual differences from the linear trend line. An advancing trend (~4-6 days per decade) was detected in the SSM/I derived timing of spring thaw and growing season onset between 1988 and 2001; this temporal advance is sufficient to explain recent trends in vegetation greening and enhanced productivity over the northern land areas (Nemani et al. Science 300).
TRL = Technology readiness level; 3=Proof of concept validation & under active research and development; 7=System at or near operational; 8=End of system development; 9=thoroughly demonstrated and tested.
Accuracy of MODIS – AMSRE data is within the icertainy ragnge of the tower measuremnts SMAP will double the accuracy of the tower-based measurements.
The graphs below show the relationships between MODIS-AMSR-E carbon model based annual GPP and total (autotrophic + heterotrophic) respiration, Rtot, and corresponding simulations of annual fluxes using the BIOME-BGC ecosystem process model with local meteorology and ecophysiological parameters. Both models produce similar spatial patterns of annual fluxes ranging from relatively unproductive tundra to higher productivity boreal evergreen needle-leaf and broadleaf deciduous forest. Differences between satellite remote sensing and stand level simulations were primarily attributed to comparatively large MODIS LAI inputs relative to corresponding site-level simulations, differences between MODIS regional land cover and local vegetation (e.g. Table 1) and differences in daily meteorological conditions between the NCEP reanalysis and local conditions (1, 6).
Lower r over the tropics due to less seasonality of the T series. For mv we screen amsre retrievals under higher vod biomass levels.
Characterization of Land Surface Freeze/Thaw State, Temperature and Moisture Controls on Ecosystem Productivity: Carbon Cycle Science Addressed with NASA’s Proposed Soil Moisture Active/Passive (SMAP) Mission Kyle C. McDonald Department of Earth and Atmospheric Sciences The City College of New York, New York, NY, USA and Jet Propulsion Lab, California Institute of Technology Pasadena, California, USA John S. Kimball University of Montana Missoula, Montana, USA International Geoscience and Remote Sensing Symposium July 25-29, 2011, Vancouver, BC, Canada Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology under contract to the National Aeronautics and Space Administration. This work has been undertaken in part within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data were provided by JAXA EORC.
SMAP Science Objectives <ul><li>Primary Science Objectives: </li></ul><ul><li>Global, high-resolution mapping of soil moisture and its freeze/thaw state to: </li></ul><ul><ul><li>Link terrestrial water, energy and carbon cycle processes </li></ul></ul><ul><ul><li>Estimate global water and energy fluxes at the land surface </li></ul></ul><ul><ul><li>Quantify net carbon flux in boreal landscapes </li></ul></ul><ul><ul><li>Extend weather and climate forecast skill </li></ul></ul><ul><ul><li>Develop improved flood and drought prediction capability </li></ul></ul>Soil moisture and freeze/thaw state are primary surface controls on Evaporation and Net Primary Productivity
Conceptual relationship between landscape water content and associated environmental constraints to ecosystem processes including land-atmosphere carbon, water and energy exchange and vegetation productivity. The SMAP mission will provide a direct measure of changes in landscape water content and freeze/thaw status for monitoring terrestrial water mobility controls on ecosystem processes . Terrestrial Water Mobility Constraints to Ecosystem Processes
“ Link Terrestrial Water, Energy and Carbon Cycle Processes” Do Climate Models Correctly Represent the Landsurface Control on Water and Energy Fluxes? What Are the Regional Water Cycle Impacts of Climate Variability? Landscape Freeze/Thaw Dynamics Constrain Boreal Carbon Balance [The Missing Carbon Sink Problem]. Water and Energy Cycle Soil Moisture Controls the Rate of Continental Water and 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 Soil Evaporation Normalized by Potential Evaporation
<ul><li>L-band radiometer provides coarse-resolution (40 km) high absolute accuracy soil moisture measurements for climate modeling and prediction </li></ul>SMAP Mission Uniqueness SMAP is the first L-band combined active/passive mission providing both high-resolution and frequent revisit observations <ul><li>L-band radar provides high resolution (1-3 km) observations at spatial scales necessary to accurately measure freeze/thaw transitions in boreal landscapes </li></ul><ul><li>Combined radar-radiometer soil moisture product at intermediate (10 km) resolution provides high resolution and high absolute accuracy for hydrometeorology and weather prediction </li></ul><ul><li>Frequent global revisit (~3 days, 1-2 days for boreal regions) at high spatial resolution (1-10 km) enables several critical applications in water balance monitoring, basin-scale hydrologic prediction, flood monitoring and prediction, and human health </li></ul>Comparison of SMAP coverage with other L-band missions SMAP is the only microwave mission providing consistently high resolution and frequent revisits for the global land area Range bars show the maximum and minimum parameters for the corresponding mission. SAR missions do not allow for complete global coverage.
Ecological Significance of the F/T Signal <ul><li>Seasonal frozen temperatures constrain vegetation growth and land-atmosphere CO 2 exchange for ~52% (66 million km 2 ) of the global land area. </li></ul><ul><li>Spring thaw signal coincides with growing season initiation and influences land boreal source/sink strength for atmospheric CO 2 . </li></ul>Normal to late thaw & Carbon Source [1995, 1996, 1997] Source : Goulden et al. Science , 279. Early thaw & Carbon Sink  Spring thaw dates 5/7 5/27 5/26 4/22 Primary thaw dates
Mean annual variability in springtime thaw is on the order of ±7 days, with corresponding impacts to annual net primary productivity (NPP) of approximately ± 1% per day. Spring Thaw vs Northern Vegetation Productivity Anomalies Mean Primary Thaw Date (SSM/I, 1988-2000) Mean Annual NPP (AVHRR, 1988-2000) Early thaw (- sign) promotes larger (+) NPP Later thaw (+ sign) promotes lower (-) NPP Source : Kimball et al., Earth Interactions 10 (21) AK Regional Correspondence Between SSM/I Thaw Date and Annual NPP
Freeze/thaw link to carbon source-sink activity: Early thaw years enhance growing season uptake (drawdown) of atmospheric CO 2 by NPP; Later thaw years reduce NPP and CO 2 drawdown. R = 0.63, p = 0.015 Spring Thaw Regulates Boreal-Arctic Sequestration of Atmospheric CO 2 Earlier thaw & larger CO 2 drawdown (- sign) Later thaw & smaller CO 2 drawdown (+ sign) Source : McDonald et al., Earth Interactions 8(20) NOAA CMDL Observatory at Barrow Julian Day Mean Thaw Date (SSM/I, 1988-2001)
Define F/T Affected Regions FT Affected Regions Defined by Cold Temperature Constraints Index & long-term reanalysis (GMAO) data FT domain: Vegetated areas where CCI ≥ 5 d yr -1
<ul><li>Algorithm Parameterizations: </li></ul><ul><ul><li>Seasonal frozen and thawed reference states </li></ul></ul><ul><ul><ul><li>Varies with topography and landcover </li></ul></ul></ul><ul><ul><li>Threshold reference (T) </li></ul></ul><ul><ul><ul><li>Selected based on difference in seasonal </li></ul></ul></ul><ul><ul><ul><li>frozen and thawed states </li></ul></ul></ul><ul><li>Approach for Assignment of Parameters: </li></ul><ul><li>- Seasonal frozen and thawed reference states may be initially assigned using prototype SAR datasets and radar backscatter modeling over representative test sites. </li></ul><ul><li>- Ancillary landcover and topography information may be used to interpolate reference states across the product domain. </li></ul><ul><li>- The threshold reference (T) depends on landcover and topography. </li></ul><ul><li>Setting initial algorithm parameters is a key application of the algorithm testbed. </li></ul><ul><li>- Final parameterization will be performed using the SMAP L2 radar data as part of reprocessing. </li></ul>SMAP L3_FT_HiRes Algorithm Baseline Algorithm (t) = 0 (t) - 0 fr ] / [ 0 th - 0 fr ] 0 fr = frozen reference 0 th = thawed reference T = threshold (t) > T (Thawed) (t) T (Frozen)
Seasonal Threshold (t) = 0 (t) - 0 fr ] / [ 0 th - 0 fr ] 0 fr = frozen reference 0 th = thawed reference T = threshold (t) > T (Thawed) (t) T (Frozen) SMAP Freeze/Thaw Algorithm - 1 L - band SAR landscape freeze - thaw classification Backscatter (dB) < - 2 - 4 - 6 - 8 - 10 - 13 < - 18 Frozen Water Classified State 17 Feb. (Day 48) 1 April (Day 91) 3 April (Day 93) JERS - 1 L - - Backscatter (dB) < - 2 - 4 - 6 - 8 - 10 - 13 < - 18 Frozen Water Classified State Thawed
Source : Kim et al. 2010. Developing a global record of daily landscape freeze/thaw status using satellite passive microwave remote sensing. IEEE TGARS, DOI: 10.1109/TGRS.2010.2070515. Seasonal Threshold Approach: Annual Definition of SSM/I (37V GHz) T b F/T Reference States Frozen Non-Frozen Pixel-wise Calibration using T mx /T mn from Global Reanalysis F/T Classification Algorithm Δ Tb
McDonald et al. Freeze/Thaw Algorithm: Other Considerations
Apr 10 Jul 19 Dec 26 Daily Freeze-Thaw Status SSM/I (37GHz, 25km Res.) 2004 Source : http://freezethaw.ntsg.umt.edu <ul><li>Daily F/T state maps: </li></ul><ul><ul><li>Frozen (AM & PM), </li></ul></ul><ul><ul><li>Thawed (AM & PM), </li></ul></ul><ul><ul><li>Transitional (AM frozen, PM thaw), </li></ul></ul><ul><ul><li>Inverse-Transitional (AM thaw, PM frozen) </li></ul></ul><ul><li>Global domain - F/T affected areas: </li></ul><ul><ul><li>- 66 million km 2 or 52% of global vegetated area); </li></ul></ul>L3_FT_A AM-PM Combined Product Prototype Mean Seasonal F-T Progression SSM/I 1988-2007 Frozen
Algorithm requirements L3_F/T_A: Obtain measurements of binary F/T transitions in boreal ( ≥ 45N) zones with ≥80% spatial classification accuracy (baseline); capture F/T constraints on boreal C fluxes consistent with tower flux measurements. L4_Carbon: Obtain estimates of land-atmosphere CO 2 exchange (NEE) at accuracy level commensurate with tower based CO 2 Obs. (RMSE ≤ 30 g C m -2 yr -1 ).
Level 4 Carbon Algorithm Development for SMAP MODIS AMSR-E / MERRA [g C m -2 ] (1) (2) Soil T Soil Moisture Scalar Multipliers [0,1] Tundra ( 2 Samoylov Island, Siberia) <ul><li>A level 4 carbon product (L4_C) is being developed as part of the Soil Moisture Active Passive Mission (SMAP); </li></ul><ul><li>Algorithm employs a 3-pool soil decomposition model ( 1 TCF) with ancillary GPP, T & SM inputs; </li></ul><ul><li>Initial L4_C global runs are driven by MODIS, AMSR-E & reanalysis (MERRA) inputs; </li></ul>SMAP Mission: http://smap.jpl.nasa.gov
The UMT AMSR-E Global Land Parameter Database <ul><li>Data Characteristics : </li></ul><ul><li>Variables: T mx,mn ; mv (10.7, 6.9 GHz); Fw ; VOD (10.7, </li></ul><ul><li>6.9, 18.7 GHz); V (total col.); </li></ul><ul><li>Global, daily coverage; </li></ul><ul><li>Period of Record: 2002 – 2008. </li></ul><ul><li>Product maturity: 3-7 (TRL) </li></ul><ul><li>Available online (NSIDC & UMT) </li></ul><ul><li>Reprocessing planned </li></ul>Surface Air Temperature [T mx , mn ; °C] 0 0.5 1.0 1.5 Vegetation Optical Depth (VOD) Open Water Fraction [Fw] Atm. Water Vapor [ V , mm] Soil Moisture [mv, vol.]
Source : Kimball, J.S., L.A. Jones, et al., 2008. IEEE TGARS (in-press); 1 Baldocchi, D., 2008. Aust. J. Botany 56, 1-26. Satellite Mapping of Land-Atmosphere CO 2 Exchange using MODIS and AMSR-E: L4 Carbon Product Development for SMAP <ul><li>Application of MODIS - AMSR-E carbon model over boreal-Arctic tower sites indicates RMSE accuracies sufficient to determine NEE (net ecosystem exchange) to within ~31 g C m -2 yr -1 , </li></ul><ul><li>which is within 1 estimated (30-100 gC m -2 yr -1 ) tower measurement accuracy. </li></ul><ul><li>Sensitivity studies show SMAP will provide improved Ts and SM inputs, and resolve NEE to within ~13 g C m -2 over a ~100-day growing season. </li></ul>Boreal Forest (OBS) Tundra (BRO) NEE GPP R tot NEE GPP R tot Boreal-Arctic Tower Test Sites 56 km 56 km
Estimated Annual C Fluxes vs Site Ecosystem Model Results 1:1 RMSE = 25.3% MR = 7.1% <ul><li>C-Model derived annual GPP and Rtot similar (RMSE<30%) to stand ecosystem process model results across latitudinal gradient of boreal-arctic tower sites. </li></ul><ul><li>Uncertainty in residual NEE larger than component GPP/Rtot fluxes, especially for low productivity tundra sites. </li></ul>1:1 RMSE = 28.8% MR = 21.5%
Daily T and SM Time Series from AMSR-E and MERRA WMO weather stations USA Biophysical stations (SCAN, Ameriflux, …) Source : Yi, Kimball, Jones, Reichle, McDonald, 2011. Journal of Climate
Prototype L4_C using MODIS-MERRA inputs Algorithm calibration and validation using FLUXNET tower CO 2 (GPP, R eco , NEE) flux measurements across global range of land cover types. L4_C and Tower R eco Comparison FLUXNET Tower Eddy Covariance Measurement Network
Quantifying Land Source-Sink activity for CO 2 <ul><li>The L4_C NEE (g C m -2 d -1 ) outputs provide initial conditions for 1 CarbonTracker inversions of terrestrial CO 2 source/sink activity; </li></ul><ul><li>Differences in final optimized monthly C-fluxes relative to 1 ESRL baseline are strongly dependent on these initial “first guess” C-fluxes (right); </li></ul><ul><li>Atm. inversions provide additional verification of L4_C NEE against global flask network Obs. & other land models; </li></ul><ul><li>Results link C source-sink activity to underlying vegetation productivity & moisture/temperature controls. </li></ul>Initial conditions ( 1 ESRL) Final optimized C-flux ( 1 ESRL) Initial conditions (L4_C) Final optimized C-flux (L4_C) 1 http://www.esrl.noaa.gov/gmd/ccgg/carbontracker July 2003
Soil Moisture Active and Passive (SMAP) Mission
Prototype L4_C Implementation using MODIS-MERRA inputs Annual NEE was estimated at a 0.5 degree spatial resolution globally over a 7-year record using daily time series MERRA (SM, T) & MODIS (GPP) inputs. Estimated global carbon (NEE) source (+) & sink (-) variability is strongly affected by tropical (EBF) areas (above); large source activity in the tropics is driven by regional drought-induced GPP decline.
Tsoil ( ° C, <10cm) GPP (g C m -2 d -1 ) R aut (g C m -2 d -1 ) R h (g C m -2 d -1 ) SOC (g C m -2 d -1 ) <ul><li>SMAP : </li></ul><ul><li>L1C_S0_HiRes (HH VV HV) </li></ul><ul><li>L1B/C_Tb (AM, K) </li></ul><ul><li>L3_FT_HiRes (DIM) </li></ul><ul><li>L3_SM_A/P (g m -2 ) </li></ul>SMAP L4 Carbon Product Development NEE (g C m -2 d -1 ) MODIS MOD17A2 Algorithm (Running et al. 2004) TCF Model (Kimball et al. 2008) SMAP L1/3 product streams Microwave RS based soil T (e.g. Jones et al. 07, Wigneron et al. 08) Reanalysis (e.g. GMAO) <ul><li>R (W m -2 ) </li></ul><ul><li>Ta ( ° C) </li></ul><ul><li>VPD (Pa) </li></ul>MODIS/AVHRR/VIIRS: <ul><li>EVI-NDVI </li></ul><ul><li>LAI-FPAR </li></ul>
Nominal SMAP Mission Overview <ul><li>Science Measurements </li></ul><ul><ul><li>Soil moisture and freeze/thaw state </li></ul></ul><ul><li>Orbit: </li></ul><ul><ul><li>Sun-synchronous, 6 am/6pm nodal crossing </li></ul></ul><ul><ul><li>670 km altitude </li></ul></ul><ul><li>Instruments: </li></ul><ul><ul><li>L-band (1.26 GHz) radar </li></ul></ul><ul><ul><ul><li>Polarization: HH, VV, HV </li></ul></ul></ul><ul><ul><ul><li>SAR mode: 1-3 km resolution (degrades over center 30% of swath) </li></ul></ul></ul><ul><ul><ul><li>Real-aperture mode: 30 x 6 km resolution </li></ul></ul></ul><ul><ul><li>L-band (1.4 GHz) radiometer </li></ul></ul><ul><ul><ul><li>Polarization: V, H, U </li></ul></ul></ul><ul><ul><ul><li>40 km resolution </li></ul></ul></ul><ul><ul><li>Instrument antenna (shared by radar & radiometer) </li></ul></ul><ul><ul><ul><li>6-m diameter deployable mesh antenna </li></ul></ul></ul><ul><ul><ul><li>Conical scan at 14.6 rpm </li></ul></ul></ul><ul><ul><ul><li>incidence angle: 40 degrees </li></ul></ul></ul><ul><ul><ul><ul><li>Creating Contiguous 1000 km swath </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Swath and orbit enable 2-3 day revisit </li></ul></ul></ul></ul><ul><li>Mission Ops duration: 3 years </li></ul>SMAP has significant heritage from the Hydros mission concept and Phase A studies
Climate Change : Monitoring of patterns, variations & anomalies in CO 2 source/sink activity; vegetation, moisture & temperature effects on carbon uptake and release. Forestry and Agriculture : Carbon sequestration assessment and monitoring; net productivity; drought impacts, disturbance & recovery; Spatial-temporal extrapolation of in situ observations. Environmental Policy : Regional carbon budgets; carbon accounting and vulnerability assessments. Potential Applications
Baseline Science Data Products Global Mapping L-Band Radar and Radiometer High-Resolution and Frequent-Revisit Science Data Observations + Models = Value-Added Science Data Data Product Description L1B_S0_LoRes Low Resolution Radar σ o in Time Order L1C_S0_HiRes High Resolution Radar σ o on Earth Grid L1B_TB Radiometer T B in Time Order L1C_TB Radiometer T B on Earth Grid L2/3_F/T_HiRes Freeze/Thaw State on Earth Grid L2/3_SM_HiRes Radar Soil Moisture on Earth Grid L2/3_SM_40km Radiometer Soil Moisture on Earth Grid L2/3_SM_A/P Radar/Radiometer Soil Moisture on Earth Grid L4_Carbon Model Assimilation on Earth Grid L4_SM_profile Model Assimilation on Earth Grid