1. 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. 2 SMAP 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 Soil moisture and freeze/thaw state are primary surface controls on Evaporation and Net Primary Productivity

3. 3 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

4. 4 “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

5. 5 SMAP Measurement Approach

6. 6 L-band radiometer provides coarse-resolution (40 km) high absolute accuracy soil moisture measurements for climate modeling and prediction SMAP Mission Uniqueness SMAP is the first L-band combined active/passive mission providing both high-resolution and frequent revisit observations 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 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.

7. 7 Normal to late thaw & Carbon Source [1995, 1996, 1997] Source: Goulden et al. Science, 279. Early thaw & Carbon Sink [1998] Spring thaw dates 5/75/275/264/22 Primary thaw dates Ecological Significance of the F/T Signal  Seasonal frozen temperatures constrain vegetation growth and land-atmosphere CO 2 exchange for ~52% (66 million km 2 ) of the global land area.  Spring thaw signal coincides with growing season initiation and influences land boreal source/sink strength for atmospheric CO 2.

8. 8 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

9. 9 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. NOAA CMDL Observatory at Barrow Julian Day Mean Thaw Date (SSM/I, 1988-2001) 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)

10. 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. 11 Microwave Remote Sensing for F/T Detection

12. 12 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. SMAP L3_FT_HiRes Algorithm 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)

13. 13 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) -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 SMAP Freeze/Thaw Algorithm

14. 14 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 ΔTb F/T Classification Algorithm

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

16. 16 Apr 10 Jul 19 Dec 26 Daily Freeze-Thaw Status SSM/I (37GHz, 25km Res.) 2004 Source: http://freezethaw.ntsg.umt.edu 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 km 2 or 52% of global vegetated area); L3_FT_A AM-PM Combined Product Prototype Mean Seasonal F-T Progression SSM/I 1988-2007 Frozen

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

18. 18 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) 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 ( 1 TCF) with ancillary GPP, T & SM inputs; Initial L4_C global runs are driven by MODIS, AMSR-E & reanalysis (MERRA) inputs; SMAP Mission: http://smap.jpl.nasa.gov

19. 19 The UMT AMSR-E Global Land Parameter Database Surface Air Temperature [T mx, mn ; °C] 00.5 1.0 1.5 Vegetation Optical Depth (VOD) Open Water Fraction [Fw] Atm. Water Vapor [V, mm] Soil Moisture [mv, vol.] Data Characteristics:  Variables: T mx,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

20. 20 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 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 1 estimated (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. Boreal Forest (OBS) Tundra (BRO) NEE GPP R tot NEE GPP R tot Boreal-Arctic Tower Test Sites 56 km

21. 21 Estimated Annual C Fluxes vs Site Ecosystem Model Results 1:1 RMSE = 25.3% MR = 7.1% 1:1 RMSE = 28.8% MR = 21.5% 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. 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. 23 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

24. 24 Quantifying Land Source-Sink activity for CO 2 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 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; Differences in final optimized monthly C-fluxes relative to 1 ESRL 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. 26 Extra slides

27. 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. 28

29. 29

30. 30 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 ) Reanalysis (e.g. GMAO) R (W m -2 ) Ta (°C) VPD (Pa) MODIS/AVHRR/VIIRS: EVI-NDVI LAI-FPAR SMAP: L1C_S0_HiRes (HH VV HV) L1B/C_Tb (AM, K) L3_FT_HiRes (DIM) L3_SM_A/P (g m -2 ) 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)

31. 31 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

32. 32 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

33. 33 Backup

34. 34 Baseline Science Data Products Data ProductDescription L1B_S0_LoResLow Resolution Radar σ o in Time Order L1C_S0_HiResHigh Resolution Radar σ o on Earth Grid L1B_TBRadiometer T B in Time Order L1C_TBRadiometer T B on Earth Grid L2/3_F/T_HiResFreeze/Thaw State on Earth Grid L2/3_SM_HiResRadar Soil Moisture on Earth Grid L2/3_SM_40kmRadiometer Soil Moisture on Earth Grid L2/3_SM_A/PRadar/Radiometer Soil Moisture on Earth Grid L4_CarbonModel Assimilation on Earth Grid L4_SM_profileModel Assimilation on Earth Grid Global Mapping L-Band Radar and Radiometer High-Resolution and Frequent-Revisit Science Data Observations + Models = Value-Added Science Data