1. GCM 12/12/06: Retrieval of biophysical (vegetation) parameters from EO sensors Dr. Mat Disney mdisney@geog.ucl.ac.uk Pearson Building room 216 020 7679 0592 www.geog.ucl.ac.uk/~mdisney

2. 2 More specific parameters of interest –vegetation type (classification) (various) –vegetation amount (various) –primary production (C-fixation, food) –SW absorption (various) –temperature (growth limitation, water) –structure/height (radiation interception, roughness - momentum transfer)

3. 3 Vegetation properties of interest in global change monitoring/modelling components of greenhouse gases –CO 2 - carbon cycling photosynthesis, biomass burning –CH 4 lower conc. but more effective - cows and termites! –H 2 0 - evapo-transpiration (erosion of soil resources, wind/water)

4. 4 Vegetation properties of interest in global change monitoring/modelling also, influences on mankind –crops, fuel –ecosystems (biodiversity, natural habitats) soil erosion and hydrology, micro and meso-scale climate

5. 5 Explicitly deal here with LAI/fAPAR –Leaf Area Index/fraction Absorbed Photsynthetically active radiation (vis.) Productivity (& biomass) –PSN - daily net photosynthesis –NPP - Net primary productivity - ratio of carbon uptake to that produced via transpiration. NPP = annual sum of daily PSN. BUT, other important/related parameters –BRDF (bidirectional reflectance distribution function) –albedo i.e. ratio of outgoing/incoming solar flux –Disturbance (fires, logging, disease etc.) –Phenology (timing)

6. 6 definitions: LAI - one-sided leaf area per unit area of ground - dimensionless fAPAR - fraction of PAR (SW radiation waveband used by vegetation) absorbed - proportion

7. 7 Appropriate scales for monitoring spatial: –global land surface: ~143 x 10 6 km –1km data sets = ~143 x 10 6 pixels –GCM can currently deal with 0.25 o - 0.1 o grids (25-30km - 10km grid) temporal: –depends on dynamics 1 month sampling required e.g. for crops Maybe less frequent for seasonal variations? Instruments??

8. 8 optical data @ 1 km –EOS MODIS (Terra/Aqua) 250m-1km fuller coverage of spectrum repeat multi-angular

9. 9 optical data @ 1 km –EOS MISR, on board Terra platform multi-view angle (9) 275m-1 km VIS/NIR only

10. 10 optical data @ 1 km –ENVISAT MERIS 1 km good spectral sampling VIS/NIR - 15 programmable bands between 390nm an 1040nm. little multi-angular –AVHRR > 1 km Only 2 broad channels in vis/NIR & little multi- angular BUT heritage of data since 1981

11. 11 Future? –production of datasets (e.g. EOSDIS) e.g. MODIS products NPOESS follow on missions P-band RADAR?? –cost of large projects (`big science') high B$7 EOS little direct `commercial' value at moderate resolution data aimed at scientists, policy....

12. 12 LAI/fAPAR  direct quantification of amount of (green) vegetation  structural quantity  uses:  radiation interception (fAPAR)  evapo-transpiration (H 2 0)  photosynthesis (CO 2 ) i.e. carbon  respiration (CO2 hence carbon)  leaf litter-fall (carbon again!)  Look at MODIS algorithm  Good example of algorithm development  see ATBD: http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html

13. 13 LAI  1-sided leaf area (m 2 ) per m 2 ground area  full canopy structural definition (e.g. for RS) requires  leaf angle distribution (LAD)  clumping  canopy height  macrostructure shape

14. 14 LAI  preferable to fAPAR/NPP (fixed CO 2 ) as LAI relates to standing biomass  includes standing biomass (e.g. evergreen forest)  can relate to NPP  can relate to site H 2 0 availability (link to ET)

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16. 16 fAPAR  Fraction of absorbed photosynthetically active radiation (PAR: 400-700nm).  radiometric quantity  more directly related to remote sensing  e.g. relationship to RVI, NDVI  uses:  estimation of primary production / photosynthetic activity  e.g. radiation interception in crop models  monitoring, yield  e.g. carbon studies  close relationship with LAI  LAI more physically-meaningful measure

17. 17 Issues  empirical relationship to VIs can be formed  but depends on LAD, leaf properties (chlorophyll concentration, structure)  need to make relationship depend on land cover  relationship with VIs can vary with external factors, tho’ effects of many can be minimised

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19. 19 Estimation of LAI/fAPAR  initial field experiments on crops/grass  correlation of VIs - LAI  developed to airborne and satellite  global scale - complexity of natural structures

20. 20 Estimation of LAI/fAPAR  canopies with different LAI can have same VI  effects of clumping/structure  can attempt different relationships dept. on cover class  can use fuller range of spectral/directional information in BRDF model  fAPAR related to LAI  varies with structure  can define through  clumped leaf area  ground cover

21. 21 Estimation of LAI/fAPAR  fAPAR relationship to VIs typically simpler  linear with asymptote at LAI ~6  BIG issue of saturation of VI signal at high LAI (>5 say) need to define different relationships for different cover types

22. 22 MODIS LAI/fAPAR algorithm  RT (radiative transfer) model-based  define 6 cover types (biomes) based on RT (structure) considerations  grasses & cereals  shrubs  broadleaf crops  savanna  broadleaf forest  needle forest

23. 23 MODIS LAI/fAPAR algorithm  have different VI-parameter relationships  can make assumptions within cover types  e.g., erectophile LAD for grasses/cereals  e.g., layered canopy for savanna  use 1-D and 3D numerical RT (radiative transfer) models (Myneni) to forward-model for range of LAI  result in look-up-table (LUT) of reflectance as fn. of view/illumination angles and wavelength  LUT ~ 64MB for 6 biomes

24. 24 Method  preselect cover types (algorithm)  minimise RMSE as fn. of LAI between observations and appropriate models (stored in look-up-table – LUT)  if RMSE small enough, fAPAR / LAI output  backup algorithm if RMSE high - VI-based

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29. 29 Productivity: PSN and NPP  (daily) net photosynthesis (PSN)  (annual) net primary production (NPP)  relate to net carbon uptake  important for understanding global carbon budget -  how much is there, where is it and how is it changing  Hence climate change, policy etc. etc.

30. 30 PSN and NPP  C0 2 removed from atmosphere –photosynthesis  C0 2 released by plant (and animal) –respiration (auto- and heterotrophic) –major part is microbes in soil....  Net Photosynthesis (PSN)  net carbon exchange over 1 day: (photosynthesis - respiration)

31. 31 PSN and NPP  Net Primary Productivity (NPP)  annual net carbon exchange  quantifies actual plant growth  Conversion to biomass (woody, foliar, root) –(not just C0 2 fixation)

32. 32 Algorithms - require to be model-based  simple production efficiency model (PEM) –(Monteith, 1972; 1977)  relate PSN, NPP to APAR  APAR from PAR and fAPAR

33. 33  PSN = daily total photosynthesis  NPP, PSN typically accum. of dry matter (convert to C by assuming DM 48% C)   = efficiency of conversion of PAR to DM (g/MJ)  equations hold for non-stressed conditions

34. 34 to characterise vegetation need to know efficiency  and fAPAR: Efficiency fAPAR so for fixed 

35. 35 Determining   herbaceous vegetation (grasses):  av. 1.0-1.8 gC/MJ for C 3 plants  higher for C 4  woody vegetation:  0.2 - 1.5 gC/MJ simple model for  :

36. 36  gross - conversion efficiency of gross photosyn. (= 2.7 gC/MJ)  f - fraction of daytime when photosyn. not limited (base tempt. etc)  Y g - fraction of photosyn. NOT used by growth respiration (65-75%)  Y m - fraction of photosyn. NOT used by maintainance respiration (60-75%)

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39. 39 NPP 1km over W. Europe, 2001.

40. 40 Issues?  Need to know land cover  Ideally, plant functional type (PFT)  Get this wrong, get LAI, fAPAR and NPP/GPP wrong  ALSO  Need to make assumptions about carbon lost via respiration to go from GPP to NPP

41. 41 0 = water; 1 = grasses/cereal crops; 2 = shrubs; 3 = broadleaf crops; 4 = savannah; 5= broadleaf forest; 6 = needleleaf forest; 7 = unvegetated; 8 = urban; 9 = unclassified MODIS LAI/fAPAR land cover classification UK is mostly 1, some 2 and 4 (savannah???) and 8. Ireland mostly broadleaf forest? How accurate at UK scale? At global scale?

42. 42 Compare/assimilate with models  Dynamic Global Vegetation Models  e.g. LPJ, SDGVM, BiomeBGC... Driven by climate (& veg. Parameters)  Model vegetation productivity –hey-presto - global terrestrial carbon Nitrogen, water budgets.....  BUT - how good are they?  Key is to quantify UNCERTAINTY

43. Why carbon? CO2, CH4 etc. greenhouse gases Importance for understanding (and Kyoto etc...) Lots in oceans of course, but less dynamic AND less prone to anthropgenic distrubance de/afforestation land use change (HUGE impact on dynamics) Impact on us more direct

44. 44 Canadell et al. 2000 Data-Model Fusion [Using multiple streams of datasets with parameter optimization] C stock and flux measurements Inventory analyses Process-based information Climate data Remote sensing information CO 2 column from space Inverse modeling Process-based modeling Retrospective and forward analyses

45. 45 Carbon: how?? Measure fluxes using eddy-covariance towers

46. 46 MODIS Phenology 2001 (Zhang et al., RSE) Dynam. global veg. models driven by phenology This phenol. Based on NDVI trajectory.... greenup maturity senescencedormancy DOY 0 DOY 365

47. 47 - Carbon sinks/sources using AVHRR data to derive NPP - Carbon pool in woody biomass of NH forests (1.5 billion ha) estimated to be 61  20 Gt C during the late 1990s. - Sink estimate for the woody biomass during the 1980s and 1990s is 0.68  0.34 Gt C/yr. -From Myneni et al. PNAS, 98(26),14784- 14789 http://cybele.bu.edu/biomass/biomass.html

48. 48 Dominant Controls water availability 40% temperature 33% solar radiation 27% Total vegetated area: 117 M km2 Limiting factors

49. 49 Bottom line - half the vegetated lands greened by about 11% - 15% of the vegetated lands browned by about 3% - 1/3 rd of the vegetated lands showed no changes. Since the early 1980s about, These changes are due to easing of climatic constraints to plant growth.

50. 50 EO data: current  Global capability of MODIS, MISR, AVHRR...etc.  Estimate vegetation cover (LAI)  Dynamics (phenology, land use change etc.)  Productivity (NPP)  Disturbance (fire, deforestation etc.)  Compare with models  AND/OR use to constrain/drive models (assimilation)

51. 51 EO data: future?  BIG limitation of saturation of reflectance signal at LAI > 5  Spaceborne LIDAR, P-band RADAR to overcome this?  Use structural information, multi-angle etc.?  What does LAI at 1km (and lower) mean?  Heterogeneity/mixed pixels  Large boreal forests? Tropical rainforests?  Combine multi-scale measurements – fine scale in some places, scale up across wider areas….  EOS era (MODIS etc.) coming to an end ????

52. 52 References Cox et al. (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184-187. Dubayah, R. (1992) Estimating net solar radiation using Landsat Thematic Mapper and Digital Elevation data. Water resources Res., 28: 2469-2484. Monteith, J.L., (1972) Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol, 9:747-766. Monteith, J.L., (1977). Climate and efficiency of crop production in Britain. Phil. Trans. Royal Soc. London, B 281:277-294. Myneni et al. (2001) A large carbon sink in the woody biomass of Northern forests, PNAS, Vol. 98(26), pp. 14784-14789 Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm Theoretical Basis Document, NASA. http://cybele.bu.edu http://www.globalcarbonproject.org