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Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS

Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS. John Kimball 1,2. with : Lucas Jones 1,2 , Ke Zhang 1,2 and Qiaozhen Mu 1. 1 Numerical Terradynamic Simulation Group, University of Montana, USA. 2 Flathead Lake Biological Station, Division of Biological Sciences, Univ. MT.

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Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS

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  1. Mapping Terrestrial Water and Carbon fluxes using AMSR-E and MODIS John Kimball1,2 with: Lucas Jones1,2, Ke Zhang1,2 and Qiaozhen Mu1 1Numerical Terradynamic Simulation Group, University of Montana, USA. 2Flathead Lake Biological Station, Division of Biological Sciences, Univ. MT. Joint AMSR Science Team Meeting; July 14-16 2008

  2. Goal • Improved measures of land-atmosphere water, energy and carbon exchanges and interactions for monitoring northern biosphere response to recent climate change Working Hypothesis • Daily Tb measurements from AMSR-E are sensitive to near-surface temperature and moisture status of northern ecosystems and can be used for mapping the primary environmental constraints to land-atmosphere carbon and water exchange. Approach • Apply AMSR-E multi-frequency H/V Pol. Tb time series to quantify daily surface soil temperature and soil moisture over northern (>50°N) study sites; • Utilize similar approach with AMSR-E AM/PM H/V Pol. Tb series to estimate daily air temperature and VPD. • Utilize synergistic information from AMSR-E and MODIS to quantify land-atmosphere carbon fluxes and ET. • Algorithm development and verification using biophysical measurements and ecosystem process model simulations from regional station networks.

  3. 1Pan-Arctic Drying Trend (P-PET) (Surface Station Network) 1Drought Impacts to Vegetation Productivity (AVHRR PEM record) 2Regional Drying Patterns Recent Changes to Pan-Arctic Water/Carbon Budgets 1Kang et al., 2008. J. Geophys. Res.; 2007. 2Geophys Res. Lett. 34, L21403

  4. Remote Sensing of Land-Atmosphere C Exchange Inputs: (AMSR-E) (MODIS) 1*Soil Moisture (% Sat.) Land cover (BPLUT) GPP Tsoil (deg C) Cfract CUE NPP = GPP * (1-CUE) Ra = GPP - NPP C Substrate Pools (kg C m-2) Cmet = Cfract * NPP Cstr = (1-Cfract) * NPP Crec = 0.7 * Cstr Scalar Multipliers (DIM) Wmult Tmult Decomp. Rates (d-1) Kmet = (Kmx * Tmult * Wmult) Kstr = 0.4 * Kmet Krec = 0.01 * Kmet Flux Calc. (kg C m-2): Rh = (Kmet * Cmet + Kstr + Cstr + Krec * Crec) SOC = (Cmet + Cstr + Crec) - Rh Outputs: 1Njoku, E.G. (2004). AMSR-E/Aqua Daily L3 Surface Soil Moisture, V001, NSIDC, Boulder, CO, USA. Digital Media * Scaled between max-min observations Rh – NPP = NEE

  5. Daily Surface Soil Temperature Retrieval from AMSR-E Daily surface (<10cm depth) soil temperature retrievals (in degrees Celsius) using AMSR-E multi-frequency brightness temperatures; Remote sensing results are plotted against MODIS LST and site level measurements of soil temperature (Tsoil) and minimum daily air temperature (Tmin) from boreal forest and tundra monitoring sites. Source: Jones et al., 2007.Trans. Geosci. Rem. Sens. 45(7).

  6. Tundra (BRO) Boreal Forest (OBS) MODIS-AMSR-E Carbon Model Results RMSE [g C m-2 d-1] accuracy relative to Tower Obs: 0.8-1.8 (GPP); 0.4-0.9 (Rtot); 0.6-1.7 (NEE) Source: Kimball et al., 2008. TGRS (In press)

  7. Carbon Model Error Sensitivity SM = 30 % Sat Tsoil = 10 °C Estimated carbon model RMSE uncertainty from MODIS (1GPP) and AMSR-E (Ts and SM) inputs indicates MODIS/AMSR-E accuracies (GPP~1.2 g C m-2 d-1; Ts< 3.5 K; SM < 40 % [~20 % vol]) sufficient to resolve NEE to within ~7-31 g C m-2 yr-1. This is within the 1reported (30-100 g C m-2 yr-1) range of accuracy for tower measurements. Source: Kimball et al. 2008. Trans. Geosci. Rem. Sens. (in press) 2Baldocchi, D., 2008. Australian Journal of Botany 56. 1Assumed constant GPP error of 1.2 g C m-2 d-1; average GPP = 500 g C m-2 y-1

  8. Estimating ET from MODIS-AMSR-E Inputs

  9. AMSR-E Satellite Based Daily ET Algorithm Flow Chart Model Inputs MODIS GMAO Source:Mu, Q. et al., 2007. Rem. Sens. Environ. 111.

  10. 1.4 GHz1 6.9 GHz 10.7 GHz 18.7 GHz Vegetation Biomass Constraints on Microwave RS Observations of Soil Processes 1Veg. Water Content/Roughness [kg m-2] 1 Frequency dependence of canopy loss from Njoku & Chan Rem. Sens. Environ. (2006)

  11. Linear correlation between AMSR-E uncorrected Tbv values for various frequencies and in situ temperature measurements for selected tundra (HPV), grassland (LTH) and boreal forest (NOBS, OAS) sites. Source: Jones et al., 2007.Trans. Geosci. Rem. Sens. 45(7).

  12. Daily Air Temperature (Tmn, Tmx, Tav) Estimation from AMSR-E day/night Tbs • Method 1: Multiple Regression • Uses vertically polarized AM/PM (Asc/Desc)Tb data at 10.7, 18.7, and 89 GHz frequencies, and H/V polarization ratios of the 6.9 GHz and 89 GHz channels • Method 2: Emissivity Triangle RT-model Horizontal (footprint) Vertical (Profile) Each pixel represents a mixture of open water and vegetated soil: Uses 6.9, 10.7, 18.7, 36.5 GHz polarization ratios to iteratively solve for open water fraction and vegetation/roughness parameters and uses 36.5 GHz V-pol. AM/PM Tbs to solve for Tmn/Tmx

  13. Estimating Daily Vapor Pressure Deficit Uses AMSR-E Tmx/Tmn retrievals to calculate mean daily air temperature Assumes Tmn = Dewpoint temperature1 Relatively Robust for northern regions with low night-time temperatures and high surface water storage (low surface evaporative resistance) An arid region correction can be applied1 VPD Tmx Tmn °C kPa July 9, 2003 1Source: Kimball et. al. Ag. For. Meteor. (1997) 85.

  14. Comparison of AMSR-E and GMAO meteorological variables to tower observations at all sites; solid lines represent the linear least-square regression line, while dashed lines represent a 1:1 relationship. Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).

  15. Tower vs Model Based ET Mean Annual ET Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).

  16. RS-ET Error 1Sensitivity Absolute error (solid black lines; W/m2) and relative error (dashed gray lines; %) propagated to model derived latent energy flux (LE) for three error levels of AMSR-E derived air temperatures. Meaningful LE information is derived when LE > 7-26 W/m2 (ET > 0.13 – 1.33 mm/d) given observed MODIS/AMSR-E input and model uncertainty. Meteorological inputs contribute 28-65% of total model LE error and translate to ~3-7% relative error in cumulative ET over a 100-day growing season. 1LAI, dew point temperature, net incoming solar radiation, and error in net incoming solar radiation are held at constant, moderate values of 3 m2 m-2, 0 °C, 300 W/m2, and 70 W/m2 (~20%), respectively. Tmax varies from 0 to 30 °C. Soil evaporation is considered negligible. Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).

  17. Results Summary • AMSR-E Tb data provide reasonable estimates of surface Ta and VPD across wide range of surface/climate conditions; results similar to or better than alternative measures from station corrected reanalysis (GMAO) meteorology; • Use of MODIS GPP and AMSR-E Tsoil, SM within a simple carbon model captures regional patterns and variability SOC stocks and C-fluxes relative to site measurements and ecosystem model simulations. Model results within range of tower measurement error; • MODIS-AMSR-E based ET results similar to tower measurements and alternate results using local and reanalysis (GMAO) based daily meteorology; • Processing of these data continues from 2002-present and spans all Northern Hemisphere vegetated land areas; • Results provide basis for assessing northern carbon-water cycle interactions and ecosystem response to recent warming.

  18. Back-up Slides

  19. Model Development and Validation Sites

  20. Carbon Model Results Comparison over Tower Sites Source: Kimball et al., 2008. TGRS (In press)

  21. Relations Between TCF and BIOME-BGC Based Annual Carbon Fluxes Source: Kimball et al., 2008. TGRS (In press)

  22. 6.9 GHz AMSR-E

  23. Site observed <10 cm SM BGC SM AMSR-E LSW AMSR-E L3 SM AMSR-E Daily 1Soil Moisture Retrievals Scaled L3 product June 15, 2003 Site vs. AMSR-E SM for Tower Windows Lethbridge, CN (Grassland) Surface Wetness % Sat NSA-OBS, CN (ENLF) • AMSR-E soil moisture RMSE values range from 22 to 48 %; R2 range 0.59 to <0.01 for both methods. • AMSR-E results similar to site (BIOME-BGC) modeled soil moisture accuracy (RMSE range from 22 to 44 %; R2 range 0.53 to <0.01). • Retrieval error increases primarily under increasing biomass and water fraction Barrow, AK (Coastal Tundra) 1Source: Njoku, E.G. (2004). AMSR-E/Aqua Daily L3 Surface Soil Moisture, V001, NSIDC, Boulder, CO, USA. Digital Media * Scaled between max-min observations

  24. AMSR-E Temperature Algorithm • Multiple regression method: Uses normalized polarization ratio [ = (Tbv -Tbh)/(Tbv +Tbh)] to correct for surface water Multiple V-pol. bands (6, 10, 23, 89 GHz) contribute additional information; separate coefficients for frozen and non-frozen conditions. • Emission Process method: Assumes each pixel represents a mixture of open water and vegetated soil Slope (a) and intercept (b) dependence on land surface emissivity described by simple RT equation and constant open water emissivity Iterative minimization of Ts for adjacent bands allows simultaneous estimates of land emissivity and Ts. Source: Jones, L.A., et al., 2007. Trans. Geosci. Rem. Sens. 45(7), 2004-2018.

  25. Seasonality in MODIS Based ET the mean (2000-2006) seasonality of regional ET for the pan-Arctic domain as derived from the RS-ET algorithm and GMAO meteorology. Masked areas are shown in white. Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).

  26. MODIS-AMSR-E Estimated Surface Soil Organic Carbon (≤10cm depth, 2002-2004) TCF = MODIS-AMSR-E C model BGC = BIOME-BGC IGBP-DIS = Global SOC Inventory Site = Tower site SOC Inventory Source: Kimball et al., 2008. TGRS (In press)

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