1. Comparison of GPP from Terra-MODIS and AmeriFlux Network Towers
Biome types used in comparison: forests (evergreen needleleaf, deciduous broadleaf, and mixed species), oak savanna, grassland, tundra, and chaparral.

8. Climate
Meteorology tends to explain about 27% of the difference between DAO & Tower results.
Meteorology is not an issue at this site.
The DAO tends to capture the trends at the site.
The most likely sources of error at this site include the
over-estimation of LAI. MODIS estimates LAI at
~5, but site LAI is closer to 3. Also, consistent
cloudiness in the region leads to potential
contamination of the input LAI/fPAR, which
is then filled by linear interpolation.

9. Global Daily Surface Meteorology vs Fluxtowers across 9 biomes
From D.P.Turner et al. Remote Sensing of Env. 102:

10. MODIS ASCII Subsets 277 sites worldwide (field sites or flux towers)
Two annual land products, six 8-day land products, one 16-day product, and calculated albedos from Collection 4
Select sites from pick list or Web Map Server
Developing Web-based data visualization tools
Product
Name
Surface Reflectance
09A1
Surface Temperature
11A2
Land Cover
12Q1
NDVI / EVI
13A2
LAI / fPAR
15A2
GPP, PSN
17A2
Annual NPP
17A3
BRDF Albedo
43B1
NBAR
43B3

11. Uncertainties from Land Cover (MOD12Q1)
Duke Forest, NC – Main Tower
V003
V004
2 = Evergreen Broadleaf Forest (EBF)
9 = Savannas
4 = Deciduous Broadleaf Forest (DBF)
10 = Grasslands
5 = Mixed Forest
13 = Urban & Built-Up*
8 = Woody Savannas
MODIS products are not
calculated for this pixel

12. MODIS GPP vs Tower GPP
For 15 Ameriflux Sites
MODIS vs
Field LAI
Heinsch et al. IEEE 44: , 2006

13. Park Falls is a transitional site.
Also, as a tall tower, the footprint is much more similar to that
of MODIS.
Again, MODIS GPP overestimates tower GPP throughout the year.
LC is less of an issue here as it is largely mixed forest.
However, the area consists of approximately 30% wetlands,
which cannot be captured by MODIS (no
soil component).
Still captures general seasonal trend as well as daily fluctuations
in productivity.

15. MODIS GPP/NPP vs Fluxtowers across 9 Biomes
From D.P.Turner et al.
Remote Sensing of Env
102:

16. Penman-Monteith (P-M) equation

17. Comparison of annual latent heat flux (LE) observations from 19 AmeriFlux sites and the ET estimates averaged over the MODIS 3  3 km cutout. These data were created using (a) tower-specific meteorology and (b) the global GMAO meteorology. a
R=0.87 b
R=0.85

18. Comparison of daily latent heat flux (LE) measured (black dots) at a mid-latitude pine forest in North Carolina, and a high-latitude open shrub in Alaska, with ET derived from MODIS satellite data with both site-specific (blue lines) and GMAO (red lines) meteorology.

19. (a) Mean annual total precipitation and (b) mean annual total MODIS GPP versus annual total ET (driven by GMAO meteorological data) during The solid line in (a) represents the1:1 ratio.

22. Duke Forest

24. Harvard Forest

25. Howland, ME 2000 1996 (kgC/m2/5day) (kgC/m2/5day) (kgH20/m2/5day)
NEE GPP ET
Yearday

27. Lethbridge

30. BOREAS NOBS

31. Howland, MAINE Spruce forest
2000
1996
(kgC/m2/day) (kgC/m2/day) (kgH20/m2/day)
NEE GPP ET
Yearday

32. VALIDATION OF SATELLITE DERIVED GPP WITH FLUX TOWER OBSERVATIONS
For example, this is a graph of the same area we just saw depicting the temporal dynamics of grass productivity across the region portrayed in green and 93 portrayed in yellow compared to the 5 year average productivity for the region. On the Y axis is G carbon per m2 per period. On the X we have days of the year. Note the abrupt change in biomass accumulation around,>>>>>>>>>>>> while there is a clear distinct decline after approximately July 8
“Click”

33. Testing the accuracy of incident PAR input to
MOD17 algorithm using GOES derived PAR
over the continental U.S

34. Metolius-Young

35. Metolius-Old

36. Willow Creek

37. Niwot Ridge

38. Lost Creek

39. Barrow, AK