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Developing a Global Land Parameter Database for Terrestrial Ecosystem Studies using AMSR-E

Developing a Global Land Parameter Database for Terrestrial Ecosystem Studies using AMSR-E. John S. Kimball Flathead Lake Biological Station, Division of Biological Sciences, The University of Montana

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Developing a Global Land Parameter Database for Terrestrial Ecosystem Studies using AMSR-E

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  1. Developing a Global Land Parameter Database for Terrestrial Ecosystem Studies using AMSR-E John S. Kimball Flathead Lake Biological Station, Division of Biological Sciences, The University of Montana Collaborators: Lucas Jones, Yonghong Yi, Youngwook Kim & Matt Jones (UMT); Kyle McDonald, Eni Njoku & Steven Chan (JPL); Rolf Reichle (GSFC); Rama Nemani (Ames); Craig Ferguson & Eric Wood (Princeton).

  2. Recent Activities: • Release of a new AMSR-E global daily land parameter database (2002-08) including surface air temperature (T), freeze-thaw status (FT) , fractional open water cover (Fw), precipitable water vapor (V), soil moisture (SM) & vegetation optical depth (VOD); available at the NSIDC DAAC (http://nsidc.org/data/nsidc-0451.html ); • Global Implementation & testing of a terrestrial carbon flux model (TCF) using AMSR-E, MERRA and MODIS inputs; • Verification of RS based land parameters using global biophysical measurement networks, synergistic data from other satellite products & model reanalysis; • Application of the AMSR-E VOD parameter for global phenology analysis; • Development of a global Data Record for the FT state parameter using ensemble satellite microwave remote sensing time series; • Algorithm development & refinement for the NASA Soil Moisture Active Passive mission (SMAP, 2014 launch).

  3. Global Implementation of a Terrestrial Carbon Flux (TCF) Model1 using AMSR-E and MODIS Inputs Northern Old Black Spruce, Manitoba AMSR-E / MERRA MODIS Soil T Soil Moisture Scalar Multipliers [0,1] (1) 2 [g C m-2] (2) Soil and Litter Carbon (3-pools) from spin-up and disturbance/landcover maps. 1Kimball, J.S., L.A. Jones, et al. 2009. IEEE TGARS, 47(2), 569-587; 2Mildrexler et al. 2009. RSE 112: 2103-2117.

  4. AMSR-E Derived Environmental Constraints to TCF Soil Carbon Estimates Mean (2003 - 2006) annual surface (<10cm depth) soil temperature and moisture constraints (top) to heterotrophic respiration derived using TCF model with AMSR-E and MODIS (GPP) inputs. (a) The dimensionless temperature multiplier [Tmult; 0-1] is an exponential function of mean daily soil temperature from AMSR-E. (b) The soil moisture multiplier (Wmult) is a convex-parabolic function of soil wetness from AMSR-E. Wmult is assigned a value of 1 where AMSR-E (10.7GHz) vegetation optical depth >1.5. (c) The frozen season constraint is derived from AMSR-E 36V GHz Tb series. (d) The TCF model steady state surface SOC content is derived for conditions described by (a) – (d).

  5. Calibration & Verification of TCF Soil Carbon Stocks from Global Inventory Data AMSR-E Steady State SOC1 [kg C m-2] SOC Probability Density Function (PDF) Soil Pit Data2 SOC1 [ kg C m-2] 1Adjusted to surface (<10 cm) layer and shown as medians for 2 ° × 2° grid cells; 2Source: Zinke et al. (1998), http://www.daac.ornl.gov.

  6. TCF Model Calibration: NEE Response to Soil Moisture TT : Tower Met. + Tower GPP AT : AMSR-E Met. + Tower GPP Woody Savannah (Tonzi Ranch, CA1) TM: Tower Met. + MODIS GPP AM TT AT TM AM: AMSR-E Met. + MODIS GPP TT Flux tower obs. Prediction Median σ [g C m-2 d-1] AT NEE [g C m-2 8d-1] Original AM Vaira Santa Rita2 Tonzi 1D. Baldocchi is PI of the Tonzi and Vairi Ameriflux sites; 2R. Scott is PI of the Santa Rita Site

  7. Correspondence Between AMSR-E and MERRA T and SM Time Series WMO weather stations USA Biophysical stations (SCAN, Ameriflux, …)

  8. Combining AMSR-E and MERRA for Improved Soil Moisture Accuracy Walnut Gulch Grasslands Walnut Gulch Grasslands, AZ (2003-2004)2 AMSR-E improves short-term wetting/drying dynamics Soil Moisture [cm3 cm-3] Lethbridge, AB Grasslands (Plot not Shown) • Kalman Smoother (KS) method: • AMSR-E and MERRA anomalies are combined with an empirical random walk (AR(1)) model using the KS. Respective climatologies are averaged. 1Vienna University X-band soil moisture data (L2A Res 1 swath, 25 km threshold distance);2In situ soil moisture data provided by Ameriflux

  9. Global TCF Simulations using MODIS-MERRA Inputs Model validation using global tower eddy covariance CO2 flux network (FLUXNET) & and atmospheric transport model inversions (CarbonTracker).

  10. Developing a Global Data Record for Landscape Freeze/Thaw Status Daily FT Dynamics (AMSR-E AM/PM 36V GHz, 2004) Goal: 1) Build a global, long-term (30+ yr) record of daily landscape freeze-thaw state dynamics with well quantified accuracy for climate change studies; 2) Inform development of similar algorithms & products under NASA SMAP mission. Methods: Temporal change classification of ensemble satellite active & passive microwave remote sensing series; accuracy assessment and uncertainty analysis using global in situ station networks and ancillary geospatial data. Apr 10 Oct 27 Dec 26 Global Mean Annual Non-Frozen Period (1979-2009) 2004 Non-Frozen Period (AMSR-E 36V GHz)

  11. Mean Annual Accuracy (AMSR-E, 2004) Developing a Global Earth System Data Record for Landscape Freeze/Thaw Status Mean Daily FT Classification Accuracy Mean Annual FT Classification Accuracy FT_ESDR QA/QC Map Source: Y. Kim et al. IEEE TGARS (In-review)

  12. The Soil Moisture Active Passive Mission (SMAP) • NASA Tier 1 Decadal Survey mission (2014 launch); • L-band radar & radiometer suite with global 1-3 day repeat coverage & 3-40km resolution; • Prelaunch L4_Carbon & L3_F/T algorithm development, testing using AMSR-E, MODIS and MERRA land products and model drivers; http://smap.jpl.nasa.gov/

  13. AMSR-E VOD A B C Global Phenology Monitoring using Vegetation Optical Depth (VOD) from AMSR-E • AMSR-E VOD (10.7GHz) is well correlated with MODIS LAI, EVI and NDVI • Microwave provides enhanced data availability, especially over cloud dominated regions, resulting in complete vegetation phenologies when optical-IR VIs are unavailable or saturated • AMSR-E VOD provides a unique and complimentary phenology dataset. R-value MODIS LAI & AMSR-E VOD Correlation 8-Day Data 2003-2008 R-value Source: M. Jones et al. RSE (In-review) Highest QC Data Availability MODIS EVI IGBP Barren Land Cover Class B A C -1.0 -.75 -.5 -.25 .25 .5 .75 1.0 Percent of Total (2003-2008) 0 – 20% 21-40% 41-60% 61-80% 81-100%

  14. Mapping open water fraction (Fw) from AMSR-E Fw (25 km) (a) AMSR-E (25 km) JERS-1 (100 m) MODIS (1 km) B A AMSR-E seasonal Fw changes: (c) Yukon River, Stevens Village (B) (b) Yukon River, Delta (A) Comparison of Alaska fw maps derived from AMSR-E and relatively fine scale JERS-1 and MODIS land cover classifications (a). The AMSR-E fw product produces similar results (b), but with enhanced capabilities for near-daily monitoring of this dynamic variable (c); fw is a bi-product of the AMSR-E surface air temperature retrievals and is useful for global water, energy & carbon cycle studies. Jones, L.A., J.S. Kimball, E. Podest, K.C. McDonald, S.K. Chan, and E.G. Njoku, 2009. A method for deriving land surface moisture, vegetation optical depth and open water fraction from AMSR-E. Proceedings of the IEEE Int. Geosci. Rem. Sens. Symp. (IGARSS ‘09), 916-919.

  15. Summary • Verification and analysis of the AMSR-E land parameter database is ongoing; periodic updates (post-2008) will occur as additional data become available & posted to online archive (http://nsidc.org/data/nsidc-0451.html); • Accuracy documented for T, VOD, FT, Fw & SM; less so for V; • Ecological investigations of these data are ongoing, including FT & VOD phenology dynamics; T & SM driven assessment of NEE & global carbon source/sink activity, & Fw based wetland inundation dynamics. • AMSR-E T, SM series provide additional value over similar variables from MERRA reanalysis; AMSR-E SM accuracy & value decreases with increasing vegetation biomass; • TCF model calibration, testing and validation activities on-going in conjunction with L4 Carbon product development for SMAP.

  16. Jones, L.A., C.R. Ferguson, J.S. Kimball, K. Zhang, S.K. Chan, K.C. McDonald, E.G. Njoku, and E.F. Wood, 2010. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE JSTARS 3(1), 111-123. Jones, L.A., and J.S. Kimball, 2010. Daily Global Land Surface Parameters Derived from AMSR-E. Boulder Colorado USA: National Snow and Ice Data Center. Digital media (http://nsidc.org/data/nsidc-0451.html). Jones, L.A., J.S. Kimball, E. Podest, K.C. McDonald, S.K. Chan, and E.G. Njoku, 2009. A method for deriving land surface moisture, vegetation optical depth and open water fraction from AMSR-E. Proceedings of the IEEE Int. Geosci. Rem. Sens. Symp. (IGARSS ‘09), Cape Town, South Africa, 916-919. Jones, M., J.S. Kimball, K.C. McDonald, and L.A. Jones, 2010. Utilizing satellite passive microwave remote sensing for monitoring global land surface phenology. Rem. Sens. Environ. (In-review). Kim, Y. J.S. Kimball, K.C. McDonald and J. Glassy, 2010. Developing a global data record of daily landscape freeze/thaw status using satellite microwave remote sensing. IEEE TGARS (In-review). Kimball, J.S., L.A. Jones, K. Zhang, F.A. Heinsch, K.C. McDonald, and W.C. Oechel, 2009. A satellite approach to estimate land-atmosphere CO2 exchange for Boreal and Arctic biomes using MODIS and AMSR-E. IEEE TGARS, 47(2), 569-587. Mu, Q., L.A. Jones, J.S. Kimball, K.C. McDonald, and S.W. Running, 2009. Satellite assessment of land surface evapotranspiration for the pan-Arctic domain. Water Resources Research 45, W09420, doi:10.1029/2008WR007189. Recent Publications:

  17. AMSR-E Algorithm Logic: Veg. Optical Depth and Soil Moisture Each pixel is modeled as a mix of uniformly vegetated land and open water: Horizontal (footprint) Vertical (Profile) Given temperature data the H-V slope can be calculated: Optical depth (VOD) is determined from a. Land fraction emissivity error increases with open water: Soil moisture is obtained by inverting the τ-ω equation with smooth optical depth and open water corrections. Jones, L.A., et al. IGARSS ’09 Proceedings Cape Town, South Africa, 916-919.

  18. AMSR-E Algorithm Logic: Temperature and Open Water Global 2003 Averages (AMSR-E, AM) Separation of atmospheric water vapor and surface emissivity V = 5 mm Surface Air Temperature [°C] Tbv23 - Tbh23 [K] V = 50 mm Tbv18 – Tbh18 [K] V = 50 mm Atmos. Water Vapor [mm] Tbh23/Tbh18 V = 5 mm 1-Tbh18/Tbv18 Jones, L.A., et al. 2010. IEEE JSTARS 3(1), 111-123.

  19. Combining AMSR-E and MERRA Soil Moisture Time-series: A State-Space (Kalman Smoother) Approach (METHODS) Observation Equation: Method Steps: Break soil moisture time series into anomaly and climatology components (30-day moving average). Scale AMSR-E anomalies to have the same variance as MERRA. Optimize KS observation (Q) and model (R) covariances using maximum likelihood. Smooth anomaly components using the optimized KS. Compute simple average of climatology components Add smoothed anomaly and averaged climatology back together to form the new soil moisture time series. Random Walk Model Equation (AR(1)): Maximum Likelihood Filter optimization: Minimize filter innovation log likelihood with respect to R, and Q to initialize: In principle this provides normalized innovations that are independent with variance of unity. Additionally, the innovations should have no serial correlation.

  20. Tmax Daily Maximum and Minimum Land Surface Air Temperature from AMSR-E: Comparison with AIRS/AMSU temperature product (a) (b) Annual Means T [°C] Tmin (a) Meanlatitudinal distribution of AMSR-E and AIRS/AMSU surface air temperatures. AMSR-E algorithm uses 18.7 and 23.8 H /V, asc./desc. Tb to retrieve temperatures by first estimating and removing atmospheric water vapor, vegetation optical depth, and open water fraction effects. (b) Corresponding maps of mean annual air temperatures from AMSR-E. Objective: Derive global daily surface air temperatures with sufficient accuracy to drive global hydrological and ecological process models. Jones, L.A., C.R. Ferguson, J.S. Kimball, K. Zhang, S.K. Chan, K.C. McDonald, E.G. Njoku, and E.F. Wood, 2010. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE JSTARS 3(1), 111-123.

  21. Vertically integrated atmospheric water vapor from AMSR-E: Comparison with AIRS (880 mb) standard mixing ratio (b) (a) Annual Means PM V [mm] AM (a) latitudinal distribution of mean annual integrated atmospheric water vapor (V) from AMSR-E and the AIRS lowest layer (880 mb) standard mixing ratio. (b) Corresponding maps of mean annual V from AMSR-E are also presented, where grey regions indicate dense vegetation where retrievals are not possible because of low polarization ratio. Daily atmospheric water vapor is derived as a bi-product of the AMSR-E surface air temperature retrievals and is useful for global water and energy cycle studies. Future research will include more detailed assessment of retrieval accuracy using radiosondes and the AIRS integrated water vapor product.

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