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Linking In situ Measurements, Remote Sensing, and Models to Validate MODIS Products Related to the Terrestrial Carbon Cycle Peter B. Reich, University.

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Presentation on theme: "Linking In situ Measurements, Remote Sensing, and Models to Validate MODIS Products Related to the Terrestrial Carbon Cycle Peter B. Reich, University."— Presentation transcript:

1 Linking In situ Measurements, Remote Sensing, and Models to Validate MODIS Products Related to the Terrestrial Carbon Cycle Peter B. Reich, University of Minnesota Warren B. Cohen USDA Forest Service Stith T. Gower University of Wisconsin David P. Turner Oregon State University Steven W. Running University of Montana MODLAND Validation Meeting, January 22 & 23, 2001

2 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 total net primary production and carbon allocation within and among biomes Learn how flux tower-measured NEE and field-measured NPP co-vary in time & how to translate between them using ecological models Explore errors and information losses that accrue when generalizing detailed ecological information through remotely sensed data and models

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4 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

5 Field-Based Sampling Design m 2 plots 80 in a nested spatial series 20 plots broadly distributed Plot measurements Vegetation cover LAI, f APAR Aboveground biomass Aboveground productivity Belowground productivity

6 Ecosystem properties (e.g, LAI) highly variable in space Tower footprints not necessarily representative of greater site area Specific crop types can have very different productivity levels A given crop can vary in productivity among years

7 JulyAugust Overall Accuracy: 95% 2000 AGRO

8 Corn LAI= *CCI cj R 2 =0.61 Soy LAI= *CCI sj R 2 =0.58 ETM+ predictions of July LAI Corn LAI= *CCI ca R 2 =0.63 Soy LAI= *CCI sa R 2 =0.27 ETM+ predictions of Aug. LAI RMSE=0.45 Slope=0.99 Intercept=0.01 R=0.95 1:1 RMSE=0.71 Slope=0.92 Intercept=0.27 R=0.57 1:1 Crop- and measurement date-specific indices derived from canonical correlation analyses of 4-date (April-September) ETM+ spectral trajectories

9 NOBS

10 Non-burned area error matrix RMSE=9.09 Slope=0.98 Intercept=1.46 R=0.84 1:1 Canonical indices ETM+ March, June RMSE=1.19 Slope=1.00 Intercept=0.10 R=0.74 Canonical indices ETM+ March, June

11 NOBS July BigFootMODLAND

12 NOBS July BigFootMODLAND

13 AGRO July BigFootMODLAND

14 AGRO July August BigFootMODLAND

15 MODIS GPP Product GPP (gC m -2 d -1 ) = PAR * f APAR *  g Where: PAR = from DAO climate model f APAR = 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

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25 Final Points Anticipated quantitative comparisons with MODIS products Direct map-to-map comparisons (e.g., freq. dist. fine- vs. coarse-grained landcover classes; mean/SD of LAI/f APAR /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 minimal delay 21 datasets thus far (field data, derived surfaces, climate data)

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27 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


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