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

2. Apply AMSR-E multi-frequency H/V Pol. T b 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. T b 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. Approach Working Hypothesis Daily T b 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. Goal Improved measures of land-atmosphere water, energy and carbon exchanges and interactions for monitoring northern biosphere response to recent climate change

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

4. GPP T mult W mult Scalar Multipliers (DIM) Decomp. Rates (d -1 ) Flux Calc. (kg C m -2 ): C Substrate Pools (kg C m -2 ) R h = (K met * C met + K str + C str + K rec * C rec ) C met = C fract * NPP C str = (1-C fract ) * NPP C rec = 0.7 * C str K met = (K mx * T mult * W mult ) K str = 0.4 * K met K rec = 0.01 * K met T soil (deg C) 1* Soil Moisture (% Sat.) Land cover (BPLUT) (AMSR-E) (MODIS) NPP = GPP * (1-CUE) R a = GPP - NPP C fract CUE R h – NPP = NEE SOC = (C met + C str + C rec ) - R h Outputs: Inputs: Remote Sensing of Land-Atmosphere C Exchange 1 Njoku, E.G. (2004). AMSR-E/Aqua Daily L3 Surface Soil Moisture, V001, NSIDC, Boulder, CO, USA. Digital Media * Scaled between max-min observations

5. 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. Daily Surface Soil Temperature Retrieval from AMSR-E Source: Jones et al., 2007.Trans. Geosci. Rem. Sens. 45(7).

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

7. Carbon Model Error Sensitivity Source: Kimball et al. 2008. Trans. Geosci. Rem. Sens. (in press) 2 Baldocchi, D., 2008. Australian Journal of Botany 56. Estimated carbon model RMSE uncertainty from MODIS ( 1 GPP) 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 1 reported (30-100 g C m -2 yr -1 ) range of accuracy for tower measurements. 1 Assumed constant GPP error of 1.2 g C m -2 d -1 ; average GPP = 500 g C m -2 y -1 SM = 30 % Sat Tsoil = 10 °C

8. Estimating ET from MODIS-AMSR-E Inputs

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

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

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 (T mn, T mx, T av ) Estimation from AMSR-E day/night T b s 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 Vertical (Profile) Horizontal (footprint) 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 T b s 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 temperature 1 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 applied 1 1 Source: Kimball et. al. Ag. For. Meteor. (1997) 85. Tmn Tmx VPD kPa °C July 9, 2003

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

16. 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/m 2 (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. RS-ET Error 1 Sensitivity 1 LAI, dew point temperature, net incoming solar radiation, and error in net incoming solar radiation are held at constant, moderate values of 3 m 2 m -2, 0 °C, 300 W/m 2, and 70 W/m 2 (~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 T b 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.

19. Back-up Slides

20. Model Development and Validation Sites

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

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

23. AMSR-E 6.9 GHz

24. AMSR-E Daily 1 Soil Moisture Retrievals NSA-OBS, CN (ENLF) Barrow, AK (Coastal Tundra) Lethbridge, CN (Grassland) Site vs. AMSR-E SM for Tower Windows Scaled L3 product June 15, 2003 AMSR-E soil moisture RMSE values range from 22 to 48 %; R 2 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 %; R 2 range 0.53 to <0.01). Retrieval error increases primarily under increasing biomass and water fraction 1 Source: Njoku, E.G. (2004). AMSR-E/Aqua Daily L3 Surface Soil Moisture, V001, NSIDC, Boulder, CO, USA. Digital Media * Scaled between max-min observations Surface Wetness % Sat Site observed <10 cm SM BGC SM AMSR-E L3 SM AMSR-E LSW

25. AMSR-E Temperature Algorithm Multiple regression method: Emission Process 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. 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.

26. 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. Seasonality in MODIS Based ET Source: Mu, Q. et al., 2008.Water Resources. Research (In-review).

27. Source: Kimball et al., 2008. TGRS (In press) 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