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SMAP Science Objectives

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

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SMAP Science Objectives

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  1. 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.

  2. SMAP Science Objectives Primary Science Objectives: • Global, high-resolution mapping ofsoil moisture and its freeze/thaw state to: • Link terrestrial water, energy and carboncycle 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 Soil moisture and freeze/thaw state are primary surface controls on Evaporation and Net Primary Productivity

  3. Terrestrial Water Mobility Constraints to Ecosystem Processes 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.

  4. Campbell Yolo Clay Field Experiment Site, California Soil Evaporation Normalized by Potential Evaporation Surface Soil Moisture [% Volume] Measured by L-Band Radiometer “Link Terrestrial Water, Energy and Carbon Cycle Processes” Water and Energy Cycle Carbon Cycle Soil Moisture Controls the Rate of Continental Water and Cycles Landscape Freeze/Thaw Dynamics Constrain Boreal Carbon Balance [The Missing Carbon Sink Problem]. Do Climate Models Correctly Represent the Landsurface 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?

  5. SMAP Measurement Approach

  6. SMAP Mission Uniqueness SMAP is the first L-band combined active/passive mission providing both high-resolution and frequent revisit observations • L-band radiometer provides coarse-resolution (40 km) high absolute accuracy soil moisture measurements for climate modeling and prediction Comparison of SMAP coverage with other L-band missions • L-band radar provides high resolution (1-3 km) observations at spatial scales necessary to accurately measure freeze/thaw transitions in boreal landscapes • Combined radar-radiometer soil moisture product at intermediate (10 km) resolution provides high resolution and high absolute accuracy for hydrometeorology and weather prediction • 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 Range bars show the maximum and minimum parameters for the corresponding mission. SAR missions do not allow for complete global coverage. SMAP is the only microwave mission providing consistently high resolution and frequent revisits for the global land area

  7. Normal to late thaw & Carbon Source [1995, 1996, 1997] Primary thaw dates Early thaw & Carbon Sink [1998] Source: Goulden et al. Science, 279. Spring thaw dates 5/7 5/27 5/26 4/22 Ecological Significance of the F/T Signal • Seasonal frozen temperatures constrain vegetation growth and land-atmosphere CO2 exchange for ~52% (66 million km2) of the global land area. • Spring thaw signal coincides with growing season initiation and influences land boreal source/sink strength for atmospheric CO2.

  8. Spring Thaw vs Northern Vegetation Productivity Anomalies AK Regional Correspondence Between SSM/I Thaw Date and Annual NPP Mean Primary Thaw Date (SSM/I, 1988-2000) Early thaw (- sign) promotes larger (+) NPP Later thaw (+ sign) promotes lower (-) NPP Mean Annual NPP (AVHRR, 1988-2000) Source: Kimball et al., Earth Interactions 10 (21) 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.

  9. Spring Thaw Regulates Boreal-Arctic Sequestration of Atmospheric CO2 NOAA CMDL Observatory at Barrow Mean Thaw Date (SSM/I, 1988-2001) Julian Day Earlier thaw & larger CO2 drawdown (- sign) Later thaw & smaller CO2 drawdown (+ sign) R = 0.63, p = 0.015 Source: McDonald et al., Earth Interactions 8(20) Freeze/thaw link to carbon source-sink activity: Early thaw years enhance growing season uptake (drawdown) of atmospheric CO2 by NPP; Later thaw years reduce NPP and CO2 drawdown.

  10. 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

  11. Microwave Remote Sensing for F/T Detection

  12. SMAP L3_FT_HiRes Algorithm Algorithm Parameterizations: • Seasonal frozen and thawed reference states • Varies with topography and landcover • Threshold reference (T) • Selected based on difference in seasonal frozen and thawed states Approach for Assignment of Parameters: - Seasonal frozen and thawed reference states may be initially assigned using prototype SAR datasets and radar backscatter modeling over representative test sites. - Ancillary landcover and topography information may be used to interpolate reference states across the product domain. - The threshold reference (T) depends on landcover and topography. Setting initial algorithm parameters is a key application of the algorithm testbed. - Final parameterization will be performed using the SMAP L2 radar data as part of reprocessing. Baseline Algorithm: D(t) = [s0(t) -s0fr] / [s0th -s0fr] s0fr =frozen reference s0th= thawed reference T = threshold D(t) > T (Thawed) D(t)  T (Frozen)

  13. - - 1 L 1 L - - band SAR landscape freeze - - thaw classification JERS < < - - 2 2 - - 4 4 - - 6 6 - - 8 8 - - 10 10 - - 13 13 < < - - 18 18 Backscatter (dB) Backscatter (dB) 17 Feb. (Day 48) 1 April (Day 91) 3 April (Day 93) Frozen Frozen Thawed Water Water Classified Classified State State SMAP Freeze/Thaw Algorithm Seasonal Threshold D(t) = [s0(t) -s0fr] / [s0th -s0fr] s0fr=frozen reference s0th= thawed reference T = threshold D(t) > T (Thawed) D(t)  T (Frozen)

  14. ΔTb F/T Classification Algorithm Pixel-wise Calibration using Tmx/Tmn from Global Reanalysis Seasonal Threshold Approach: Annual Definition of SSM/I (37V GHz) Tb F/T Reference States Frozen Non-Frozen Source: Kim et al. 2010. Developing a global record of daily landscape freeze/thaw status using satellite passive microwave remote sensing. IEEETGARS, DOI: 10.1109/TGRS.2010.2070515.

  15. Freeze/Thaw Algorithm: Other Considerations McDonald et al.

  16. Frozen L3_FT_A AM-PM Combined Product Prototype • Daily F/T state maps: • Frozen (AM & PM), • Thawed (AM & PM), • Transitional (AM frozen, PM thaw), • Inverse-Transitional (AM thaw, PM frozen) • Global domain - F/T affected areas: • - 66 million km2 or 52% of global vegetated area); Daily Freeze-Thaw Status SSM/I (37GHz, 25km Res.) 2004 Apr 10 Mean Seasonal F-T Progression SSM/I 1988-2007 Jul 19 Dec 26 Source: http://freezethaw.ntsg.umt.edu

  17. 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 CO2 exchange (NEE) at accuracy level commensurate with tower based CO2 Obs. (RMSE ≤ 30 g C m-2 yr-1).

  18. Level 4 Carbon Algorithm Development for SMAP Tundra (2Samoylov Island, Siberia) AMSR-E / MERRA MODIS Soil T Soil Moisture Scalar Multipliers [0,1] (1) • A level 4 carbon product (L4_C) is being developed as part of the Soil Moisture Active Passive Mission (SMAP); • Algorithm employs a 3-pool soil decomposition model (1TCF) with ancillary GPP, T & SM inputs; • Initial L4_C global runs are driven by MODIS, AMSR-E & reanalysis (MERRA) inputs; [g C m-2] (2) SMAP Mission: http://smap.jpl.nasa.gov

  19. Atm. Water Vapor [V, mm] Surface Air Temperature [Tmx,mn; °C] Open Water Fraction [Fw] Soil Moisture [mv, vol.] Vegetation Optical Depth (VOD) The UMT AMSR-E Global Land Parameter Database Data Characteristics: • Variables: Tmx,mn; mv (10.7, 6.9 GHz); Fw; VOD (10.7, 6.9, 18.7 GHz); V (total col.); • Global, daily coverage; • Period of Record: 2002 – 2008. • Product maturity: 3-7 (TRL) • Available online (NSIDC & UMT) • Reprocessing planned 1.5 1.0 0 0.5

  20. Boreal Forest (OBS) Tundra (BRO) Boreal-Arctic Tower Test Sites NEE NEE GPP GPP Rtot Rtot 56 km 56 km Satellite Mapping of Land-Atmosphere CO2 Exchange using MODIS and AMSR-E: L4 Carbon Product Development for SMAP Source: Kimball, J.S., L.A. Jones, et al., 2008. IEEE TGARS (in-press); 1Baldocchi, D., 2008. Aust. J. Botany 56, 1-26. • 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, • which is within 1estimated (30-100 gC m-2 yr-1) tower measurement accuracy. • 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.

  21. 1:1 RMSE = 28.8% MR = 21.5% Estimated Annual C Fluxes vs Site Ecosystem Model Results 1:1 RMSE = 25.3% MR = 7.1% • C-Model derived annual GPP and Rtot similar (RMSE<30%) to stand ecosystem process model results across latitudinal gradient of boreal-arctic tower sites. • Uncertainty in residual NEE larger than component GPP/Rtot fluxes, especially for low productivity tundra sites.

  22. 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

  23. Prototype L4_C using MODIS-MERRA inputs L4_C and Tower Reco Comparison Algorithm calibration and validation using FLUXNET tower CO2 (GPP, Reco, NEE) flux measurements across global range of land cover types. FLUXNET Tower Eddy Covariance Measurement Network

  24. Initial conditions (1ESRL) Initial conditions (L4_C) July 2003 Final optimized C-flux (L4_C) Final optimized C-flux (1ESRL) 1http://www.esrl.noaa.gov/gmd/ccgg/carbontracker Quantifying Land Source-Sink activity for CO2 • The L4_C NEE (g C m-2 d-1) outputs provide initial conditions for 1CarbonTracker inversions of terrestrial CO2 source/sink activity; • Differences in final optimized monthly C-fluxes relative to 1ESRL baseline are strongly dependent on these initial “first guess” C-fluxes (right); • Atm. inversions provide additional verification of L4_C NEE against global flask network Obs. & other land models; • Results link C source-sink activity to underlying vegetation productivity & moisture/temperature controls.

  25. Soil Moisture Active and Passive (SMAP) Mission

  26. Extra slides

  27. 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.

  28. MODIS/AVHRR/VIIRS: Reanalysis (e.g. GMAO) • EVI-NDVI • LAI-FPAR • R (W m-2) • Ta (°C) • VPD (Pa) SMAP L4 Carbon Product Development • SMAP: • L1C_S0_HiRes (HH VV HV) • L1B/C_Tb (AM, K) • L3_FT_HiRes (DIM) • L3_SM_A/P (g m-2) Tsoil (°C, <10cm) Rh (g C m-2 d-1) SOC (g C m-2 d-1) GPP (g C m-2 d-1) Raut (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) NEE (g C m-2 d-1)

  29. Nominal SMAP Mission Overview • Science Measurements • Soil moisture and freeze/thaw state • Orbit: • Sun-synchronous, 6 am/6pm nodal 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: 3 years SMAP has significant heritage from the Hydros mission concept and Phase A studies

  30. Potential Applications Climate Change: Monitoring of patterns, variations & anomalies in CO2 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.

  31. Backup

  32. Baseline Science Data Products Global Mapping L-Band Radar and Radiometer High-Resolution and Frequent-Revisit Science Data Observations + Models = Value-Added Science Data

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