Linking In situ Measurements, Remote Sensing, andModels to Validate MODIS Products Related

to the Terrestrial Carbon Cycle

Peter B. Reich, University of Minnesota

Warren B. CohenUSDA Forest Service

Stith T. GowerUniversity of Wisconsin

David P. TurnerOregon State University

Steven W. RunningUniversity of Montana

MODLAND Validation Meeting, January 22 & 23, 2001

Objectives(MODIS Validation & Ecological Science)

• Provide high quality, site-specific data layers at several sites that can be compared to MODIS and other sensor products (land cover, LAI, NPP)

•Develop better understanding of the climatic and ecological controls on totalnet primary production and carbon allocation within and among biomes

• Learn how flux tower-measured NEEand field-measured NPP co-vary in time& how to translate between them using ecological models

• Explore errors and information lossesthat accrue when generalizing detailedecological information through remotelysensed data and models

Sites

BOREAS Northern Old Black Spruce (NOBS)

muskeg (open black spruce),

“closed” black spruce, aspen,

wetlands

Harvard Forest (HARV) LTER

mixed hardwoods, eastern hemlock,

red pine, old-field meadow

Konza Prairie Biological Station (KONZ) LTER

tallgrass, shortgrass, shrub, gallery forest; grazing and burning regimes

Bondville Agricultural Farmland (AGRO) corn, soybeans

Field-BasedSampling Design

100 25m2 plots

80 in a nested spatial series

20 plots broadly distributed

Plot measurements

Vegetation cover

LAI, fAPAR

Aboveground biomass

Aboveground productivity

Belowground productivity

•Ecosystem properties (e.g, LAI)highly variable in space

•Tower footprints notnecessarily representative ofgreater site area

•Specific crop types can havevery different productivity levels

•A given crop can vary inproductivity among years

July August

Overall Accuracy: 95%

2000 AGRO

Corn LAI=4.41+0.63*CCIcj R2=0.61

Soy LAI=1.54+0.49*CCIsj R2=0.58

ETM+ predictions of July LAI

Corn LAI=4.00+0.45*CCIca R2=0.63

Soy LAI=3.44+0.49*CCIsa R2=0.27

ETM+ predictions of Aug. LAI

July LAI

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7

Predicted (n-1 jackknife)

Ob

serv

ed

Corn

Soybeans

RMSE=0.45Slope=0.99Intercept=0.01R=0.95

1:1

August LAI

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7

Predicted (n-1 jackknife)

Ob

serv

ed

Corn

Soybeans

RMSE=0.71Slope=0.92Intercept=0.27R=0.57

1:1

Crop- and measurement date-specific indices derived from canonical correlationanalyses of 4-date (April-September) ETM+ spectral trajectories

NOBS

LAI

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

Predicted (n-1 jackknife)

Obs

erve

d

Classified Urbn/Blt Brn/Sprse Water Wetland Muskeg Black Spr Hardwood

Urbn/Blt 3 0 0 0 0 0 0

Brn/Sprse 0 1 0 0 1 1 0

Water 0 0 6 1 0 0 0

Wetland 0 0 1 7 1 0 0

Muskeg 0 0 0 2 66 13 0

Black Spr 0 0 0 4 11 99 2

Hardwood 0 0 0 1 0 1 11

Reference

Overall Accuracy = 83.19%Non-burned area error matrix

Conifer Cover (%)

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

Predicted (n-1 jackknife)

Ob

serv

ed

RMSE=9.09Slope=0.98Intercept=1.46R=0.84

1:1 1:1

Canonical indicesETM+ March, June

RMSE=1.19Slope=1.00Intercept=0.10R=0.74

Canonical indicesETM+ March, June

NOBSJuly

BigFoot MODLAND

NOBSJuly

BigFoot MODLAND

AGROJuly

BigFoot MODLAND

AGROJuly

August

BigFoot MODLAND

MODIS GPP Product

GPP (gC m-2 d-1) = PAR * fAPAR * g

Where:

PAR = from DAO climate model

fAPAR = from MODIS reflectances

g ( gC MJ-1) = GPP / APAR

MODIS g from lookup table Spatial Resolution is 1 km Temporal Res. is 8-day mean

AGRO 2000 NPP (using observed July LAIs)

y = 0.8803x + 53.24

R2 = 0.8566

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

Observed NPP

Pred

icte

d N

PP

RMSE = 87.143

AGRO 1999 CORN GPP

0

5

10

15

20

25

30

35

120 140 160 180 200 220 240 260 280

Day of Year

Gro

ss P

rim

ary

Pro

duct

ion (

gC m

-2 d

-1)

Tower-based GPP

Simulated GPP

Final Points

Anticipated quantitative comparisons with MODIS products

• Direct map-to-map comparisons (e.g., freq. dist. fine- vs. coarse-grainedlandcover classes; mean/SD of LAI/fAPAR/NPP/GPP//tree cover); cell, site, multi-site

• Evaluate effects of generalization on modeled productivity estimates by

successive coarsening of site-specific detail (land cover IGBP/6-class;

grain size 1 km; (following land cover generalization)

BigFoot data are freely available via Mercury with minimaldelay

• 21 datasets thus far (field data, derived surfaces, climate data)

Tasseled Cap Correlations: DN vs. Reflectance; Landsat 5 vs. Landsat 7

L5 exoatmospheric reflectance to L5 DN

Brightness: 0.99, Greenness: 0.99, Wetness: 0.95

L5 COST reflectance to L7 COST reflectance

Brightness: 0.97, Greenness: 0.96, Wetness: 0.93