1. Radiometric and biophysical measures of global vegetation from multi-dimensional MODIS data Ramakrishna Nemani NTSG

2. Acknowledgements: University of Arizona Boston University NTSG Alfredo Huete Ranga Myneni Joe Glassy Kamel Didan Y. Knyazikhhin Petr Votava Tomoaki Miura Y. Zhgang Hiroki Yoshioka Y. Tian Laerte Ferreira Xiang Gao Karim Batchily

3. Radiometric Measures Vegetation Indices SR (Simple Ratio), MSR (Modified SR) SAVI (Soil Adjusted VI), MSAVI, ARVI, GEMI NDVI (Normalized Difference Vegetation Index) EVI (Enhanced Vegetation Index) Biophysical Measures Leaf Area Index (Area of leaves per unit ground area, m 2 /m 2 ) FPAR (Fraction of incident PAR that is absorbed)

4. Vegetation Indices are ‘robust’ spectral transformations of two or more bands designed to enhance the ‘vegetation signal’ and allow for reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations. VEGETATION INDICES

5. APPLICATIONS  Indicators of seasonal and inter-annual variations in vegetation (phenology)  Change detection studies (human/ climate)  Tool for monitoring and mapping vegetation  Serve as intermediaries is the assessment of various biophysical parameters: leaf area index (LAI), % green cover, biomass, FPAR, land cover classification

6. Spatio-temporal vegetation dynamics

7. 1999 Onset of Greenness

9. Departure from Average Maps from the Wildland Fire Assessment System Departure from Average maps relate current year vegetative greenness to average vegetative greenness for the same time of year.

10. Leaf Area Index (LAI) Fraction of intercepted photosynthetically active radiation (FPAR)

11. Global Leaf Area Index derived from Pathfinder NDVI and NDVI-LAI relationships

12. Global FPAR derived from Pathfinder NDVI and NDVI-FPAR relationships

13. Relating transpiration and photosynthesis to NDVI, 1988

14. Spectral reflectance of leaves Theoretical basis for spectral vegetation indices :

15. SVI Formulations Simple Ratio = NIR/Red Normalized Difference = (NIR-Red)/(NIR+Red) Vegetation Index Advantages: simple Disadvantages: residual influences of atmosphere, background and viewing geometry

16. Atmospheric Influences on Spectral Response Functions Path Radiance Sunlight Skylight Reflected Energy Total Radiance Atmosphere influences are not the same for Red and NIR Water vapor absorption Scattering by aerosols

17. Wavelength in Micrometers TM 4 Band 6 : 10.4 - 12.5 ReflectanceReflectance 1.01.52.0 1 234 5 7 2.5 0 0.5 Background Influences Vegetation Dry Soil Wet Soil

18. Angular dependence

19. VI Equations Enhanced Vegetation Index: -where  is atmospherically-corrected, surface reflectances, L is the canopy background adjustment, G is a gain factor, and C 1, C 2 are coefficients for atmospheric resistance.

20. MODIS Standard Vegetation Index Products Products KThe MODIS Products include 2 Vegetation Indices (NDVI, EVI) and QA produced at 16-day and monthly intervals at 250m/ 500m, 1km, and 25km resolutions KThe narrower ‘red’ MODIS band provides increased chlorophyll sensitivity (band 1), KThe narrower ‘NIR’ MODIS band avoids water vapor absorption (band 2) KUse of the blue channel in the EVI provides aerosol resistance

21. Dotted lines indicate AVHRR bands 1 RED 2 NIR

22. Normalizing the VIs to nadir values

23. Compositing Algorithm  Provide cloud-free VI product over set temporal intervals,  Reduce atmosphere variability & contamination  Minimize BRDF effects due to view and sun angle geometry variations  Depict and reconstruct phenological variations  Accurately discriminate inter-annual variations in vegetation. Physical and semi-empirical BRDF models Maximum VI (MVC) or constrained VI (CMVC)

25. MODIS-VI Compositing Scheme Flow Diagram

26. Global NDVI at 500 m DOY 113-128

27. 500m NDVI subset DOY 113-128 Tapajós

28. MOD13A1 QA 500m

29. 1km EVI Time Series 1km NDVI Time Series South America

30. 1 km VI’s Tapajós 113 - 128 ‘Forest’ NDVIEVI NDVI EVI

32. MODIS & AVHRR NDVI Comparisons

33. Dotted lines indicate AVHRR bands 1 RED 2 NIR AVHRR & MODIS Red and NIR bands

34. White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water

35. White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water

36. SUMMARY Both indices were robust and performed well in global vegetation monitoring and analysis The improved spectral and spatial resolutions of MODIS offer the potential for improved change detection / land use and conversion studies,

37. BIOPHYSICAL MEASURES Leaf Area Index (m2/m2): FPAR (Fraction of absorbed PAR): Incident Radiation Ground Leaf PAR absorption (radiometric) Leaf Area (structural)

38. Applications of FPAR and LAI FPAR and LAI are useful variables which help describe: –canopy structure –radiation absorption –vegetative productivity –seasonal boundaries, phenological state –global carbon cycling

39. MODIS Terrestrial Productivity Remote Sensing Inputs Model Land Cover FPAR LAI NPP = GPP - Respiration Outputs Weekly and Annual Productivity Daily Weather (Tmin, Tmax, Rnet)

41. Leaf Area Index (LAI) Fraction of intercepted photosynthetically active radiation (FPAR) Functional relations

42. 0.70 NDVI Need for a more robust approach

44. FPAR, LAI Algorithmic Approach Two-tier algorithmic approach: LUT based approach using spectral as well as angular observations simple VI based backup

45. Controlling factors: Leaf optical properties (refl,tran,abs) Canopy structure Background reflectance Sun-sensor geometry Leaf area

46. Controlling factors: Leaf optical properties (refl,tran,abs) Canopy structure Background reflectance Sun-sensor geometry Leaf area White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water

49. White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water 0.70 NDVI

50. Controlling factors: Leaf optical properties (refl,tran,abs) Canopy structure Background reflectance Sun-sensor geometry Leaf area

51. The LUT contains entries at one critical wavelength only, and certain other non-wavelength dependent constants; thus, as the algorithm ingests 2 band data or 4 band data or even 7 band data, the size of the LUT is the same! Leaf Spectral reflectance is characterized for 6 biomes at 152 points. RSAC figure

52. Wavelength in Micrometers TM MSS 4 5678 Band 6 : 10.4 - 12.5 ReflectanceReflectance 1.01.52.0 1 234 5 7 2.5 0 0.5 Vegetation Jarosite Kaolinite Dry Soil Wet Soil Background parameterization (25 types)

53. since the main algorithm is physically based, sun and view angle changes are treated as SOURCES of information rather than NOISE and thus aid in LAI/FPAR retrievals

54. LAI is defined as: LAI = g * LAI o LAI o is mean LAI of a plant g is canopy cover, which controls both total LAI as well as background contribution

55. THE LUT Contains: for each biome (6) leaf albedo at one wavelength coefficients to compute albedo any wavelength coefficients to compute BRF coefficients to compute effective background reflectance sun-sensor geometry intervals number of LAI intervals LAI saturation point

56. THE LUT Key features: energy conservation ability to ingest multiple wavelengths allows the use of uncertainities angular data as a source of information

57. FPAR, LAI Algorithm Inputs –Aggregated and atmospherically corrected 1km surface reflectances from channels {1..6}, and their uncertainities; currently only 1,2 {VIS,NIR} are used. –Land cover classification (IGBP translated to 6- class biome scheme; new 6-class coming. –Ancillary data: Radiative Transfer model lookup tables, epsilon

58. White: Needle forest Blue : Broadleaf forest Green: Grass Purple: Crop Yellow: Shrub Red : Water Controlling factors: Background reflectance Sun-sensor geometry Leaf area

59. FPAR, LAI Algorithm Outputs a distribution of LAI and FPAR, and NOT a single value! The mean of the distribution and its standard deviation are reported, thus providing an error/uncertainity estimate of its own. LAI Frequency

60. When does LUT approach fail? Land cover mixtures

61. Effect of changing Epsilon

62. Leaf Area Index (LAI) Fraction of intercepted photosynthetically active radiation (FPAR) SATURATION

67. Deriving LAI/FPAR at 250m resolution! Need land cover at 250m Blue band is at 500m

68. SUMMARY -physically based approach -use of angular data (e.g. MISR synergism) -realizing a distribution of LAIs rather than one LAI -ability to change the LUT for other sensors -VI based backup