Shaun Quegan and friends

1. Making C flux calculations interact with satellite observations of land surface properties Shaun Quegan and friends

2. Global Carbon Data Assimilation System Ciais et al. 2003 IGOS-P Integrated Global Carbon Observing Strategy

3. Terrestrial Component + Water components: SWE soil moisture

4. The SDGVM carbon cycle ATMOSPHERIC CO2 Photosynthesis GPP BIOPHYSICS NPP Soil Litter Fire Mortality GROWTH Thinning Disturbance NBP Biomass LEACHED

5. The Structure of a Dynamic Vegetation Model Parameters Climate Sn Sn+1 • DVM Soil texture Testing Processes

6. EO interactions with the DVM Phenology Snow water Burnt area Processes Climate • DVM Land cover Forest age Sn+1 Sn Testing: Radiance fAPAR Soils Observable Possible feedback Parameters

7. Matching of concepts Real world S Primary observation Model Model Derived parameter

8. MODIS LAI/fAPAR biome Landcover 2000 MODIS/IGBP Landcover 2000 MODIS/UMD Landcover 2000

9. GLC2000 (SPOT-VGT) CEH LCM2000

10. Scale effects on flux estimates (GLC-LCM) GPP NPP NEP Difference in annual predicted fluxes for GB, 1999. GLC – LCM.

11. Lessons 1 • Land cover matters. • ‘Subjective’ land cover may be more useful than ‘objective’ land cover. • Scale matters. • Can we do this better?

12. The SDGVM budburst algorithm min(0, T – T0) > Threshold, budburst occurs. When The sum is the red area. Optimise over the 2 parameters, Threshold and T0 (minimum effective temperature). T0 Start of budburst

13. Data • SPOT-VEG budburst 1998, 2000-02: 0.1o • Ground data; Komarov RAS, dates of bud-burst at 9 sites in the region. • Temperature data: ERA-40, 1.125o • GTOPO-30 DEM • Land cover: GLC2000

14. The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO Day of year

15. Variability in optimising coefficients

16. Application of model to entire boreal regions Model 1985 Model 2002 EO 2002 EO 1985

17. Comparison of ground data with calibrated model

18. Impact on Carbon Calculations 1 day advance: NPP increases by 10.1 gCm-2yr-1 15 days advance: 38% bias in annual NPP Observations Carbon Calculation Dynamic Vegetation Model Phenology model Picard et al.,GCB, 2005

19. Comparison Model-EO: RMSE Model needs to be region specific, here include chilling requirement ?

20. Lessons 2 • A simple 2-parameter spring warming model gives a good fit between model and EO data • RMS differences between model, VGT data and ground data are ~6.5 days. • Ground data are crucial in investigating bias. • Model failures are identifiable. • Noise errors in NPP estimates are ~8%. Bias effects are ~2.2% per day. • Biophysical content of the parameters is low.

21. SDGVM module driven by climate data Precipitation Cloud cover Evaporation Humidity Atmosphere Temperature Snowpack Ground Snow melt Snow water equivalent (SWE)

23. SWE estimated from SSM/I data over Siberia

25. CTCD: Comparison model and EO (& IIASA snow map) Snow Water Equivalent (mm) 01/97 SSM/I SDGVM using ECMWF IIASA maximum snow storage

26. Lessons 3 • The physical quantity inferred from the EO data is almost certainly not what it is called. • The problem here is making the model and the EO data communicate. Until communication is established, the data cannot be used to test or calibrate the model.

27. Severity of disagreement – AVHRR/SDGVM 1998 r > 0.497 OR r.m.s.e < 0.2 r < 0.497 AND r.m.s.e > 0.2 r < 0.497 AND r.m.s.e > 0.3

28. Severity of disagreement – example Mid Europe

29. Severity of disagreement – example SW China

30. Lessons 5 • The DVM as currently formulated only supports a simple observation operator. This allows meaningful estimates of time series of observables; absolute values of the observables are of dubious value. • These time series permit the model to be interrogated with satellite data, and model failures to be identified.

31. Detecting incorrect land cover Crop class incorrectly set Crop class correctly set 0.9 0.0 Pearson’s product moment Temporal correlation

32. Final remarks • The link between satellite measurements and most surface parameters used by the C models (and how they are represented) is indirect. • In many cases, the only viable source of information on surface properties is from satellites. • The art is to find the right means of communication between the data and the models.

33. Environmental effects on coherence Coherence of Kielder Forest, July 1995 • Measurements by radar satellites are sensitive to biomass, but: • only for younger ages • weather dependent through soil and canopy moisture

34. Age Estimation Accuracy Raw Coherence • Small Spatial Scale • Inter-stand variance • Inter stand bias Time Kielder Forest Kielder Forest North South • Large Scale • Meteorology dominant

35. Estimating NEE with SAR NEE = X N(A(x)) dx X N(age) coherence NEE tc ha-1 y-1 -8 -4 0 4 8 0 10 20 30 40 50 60 70 Age (y) 0 5 10 15 20 25 30 35 40 Age (y) age Sensitivity range

36. Using SPA to model coherence • Observations + Model with biomass saturation information Model Backscatter SPA was used to predict canopy and soil moisture, and coupled with a radar scattering model to predict coherence. Also needed was the saturation level of biomass, which had to be measured from the data

37. Lessons 3 Here the carbon model is essential to interpret the data and its variation.

38. UK Forest NEE Calculations 1995

39. MODIS Burned Area Russian Federation 500m burned areas 1 month 2002

40. MODIS Active Fires (& FRP) Russian Federation 1km active fires 1 month 2002