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

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

3. Terrestrial Component + Water components: SWE soil moisture

4. NBP LEACHED Litter Disturbance ATMOSPHERIC CO 2 BIOPHYSICS Soil Photosynthesis GROWTH Biomass GPPNPP Thinning Mortality Fire The SDGVM carbon cycle

5. Soil texture The Structure of a Dynamic Vegetation Model Parameters Climate SnSn S n+1 DVM Processes Testing

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

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

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

9. CEH LCM2000 GLC2000 (SPOT-VGT)

10. Scale effects on flux estimates (GLC-LCM) GPPNPPNEP Difference in annual predicted fluxes for GB, 1999. GLC – LCM. +1.0%+6.4%+16.1%

11. Lessons 1 1.Land cover matters. 2.‘Subjective’ land cover may be more useful than ‘objective’ land cover. 3.Scale matters. 4.Can we do this better?

12. Start of budburst T0T0 min(0, T – T 0 ) > Threshold, budburst occurs. The sum is the red area. Optimise over the 2 parameters, Threshold and T 0 (minimum effective temperature). When The SDGVM budburst algorithm

13. Data  SPOT-VEG budburst 1998, 2000-02: 0.1 o  Ground data; Komarov RAS, dates of bud-burst at 9 sites in the region.  Temperature data: ERA-40, 1.125 o  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 EO 2002 EO 1985 Model 2002

17. Comparison of ground data with calibrated model

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

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

20. Lessons 2 1.A simple 2-parameter spring warming model gives a good fit between model and EO data 2.RMS differences between model, VGT data and ground data are ~6.5 days. 3.Ground data are crucial in investigating bias. 4.Model failures are identifiable. 5.Noise errors in NPP estimates are ~8%. Bias effects are ~2.2% per day. 6.Biophysical content of the parameters is low.

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

23. SWE estimated from SSM/I data over Siberia

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

26. Lessons 3 1.The physical quantity inferred from the EO data is almost certainly not what it is called. 2.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 r > 0.497 OR r.m.s.e < 0.2 r 0.2 r 0.3 1998

28. Severity of disagreement – example Mid Europe

29. Severity of disagreement – example SW China

30. Lessons 5 1.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. 2.These time series permit the model to be interrogated with satellite data, and model failures to be identified.

31. Detecting incorrect land cover Pearson’s product moment 0.0 0.9 Crop class incorrectly set Crop class correctly set 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 Measurements by radar satellites are sensitive to biomass, but: only for younger ages weather dependent through soil and canopy moisture Coherence of Kielder Forest, July 1995

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

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

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 MethodsNEE Total (MtC y -1) [NEE per ha (tC ha y -1 )] Area (k ha) FC GIS (extrap. private forest) -9.37 [-3.2]2,928 SAR Estimate (measured private forest) -10.87 [-3.7]2,928 National Inventory ( land class only) -2.8 [-1.75]1,600

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

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