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Outline: High resolution data at EOS Land Validation Core Site

Satellite LAI Cross Comparison Jeff Morisette, Jeff Privette – MODLAND Validation Eric Vermote – MODIS Surface Reflectance David Roy – MODIS Quality Assurance Alfredo Huete – MODIS NDVI product. Outline: High resolution data at EOS Land Validation Core Site

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Outline: High resolution data at EOS Land Validation Core Site

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  1. Satellite LAI Cross ComparisonJeff Morisette, Jeff Privette – MODLAND ValidationEric Vermote – MODIS Surface ReflectanceDavid Roy – MODIS Quality AssuranceAlfredo Huete – MODIS NDVI product Outline: High resolution data at EOS Land Validation Core Site Statistical regression analysis, initial results (comparing NDVI from ETM+, MODIS, and AVHRR) Future plans for comparing multiple LAI products

  2. EOS Land Validation Core Siteshttp://modarch.gsfc.nasa.gov/MODIS/LAND/VAL

  3. Satellite imagery MODIS Subsets (EDC DAAC) ETM+ (EDC DAAC) ASTER data (EDC DAAC) MISR Local Mode (Langley DAAC) SeaWiFS Subsets (GSFC) IKONOS (SDP/GLCF) “GeoCover ’90s TM (SDP) EO-1 Ancillary layers and background information such as existing - elevation - land cover - reference layer available through UMd ESIP – GLCF Field and airborne data: archive and access through ORNL DAAC’s “Mercury System” AERONET and FLUXNET data Field data graphic courtesy of the BigFoot program

  4. High resolution data“targets” • Timing – as close as possible special consideration for composite products • Resolution – to allow integration with field measurements • Viewing geometry – to match product or cover viewing range of product • Spectral – as close as possible or over sampled (i.e.hyperspectral) • Geocoding – as close as possible

  5. Hyperion Pan. IKONOS 1 2 3 4 Pan. ALI 1’ 1 2 3 4 4’ ASTER 1 2 3 Pan. ETM+ 1 2 3 4 AVHRR 1 2 SeaWiFS 1 2 3 4 5 6 7 8 MODIS 3 4 1 2 Spectral Characteristics 5’ 5 7 4 5 6 7 8 9 5 7 5 6 7

  6. Core Site data summary

  7. NDVI over Konza Prairie Data: ETM+ & CIMEL 11 September 2000“6S” atm. correction MODIS daily 11 September 2000 MODIS 16-day 8-21 September, 2000 AVHRR 14-day 28 Aug.-12 Sept, 2000

  8. Correlative Analysis:Issues • Typical inference is based on a null hypothesis of “no correlation” (i.e. correlation = 0, regression slope term = 0) • Inference on correlation assume, among other things, independent data • Correlation and R2 alone do not tell the whole story • Regression analysis includes several assumptions • Subset size and location will influence results

  9. MODIS Projection issues ISIN Geographic http://edc.usgs.gov/programs/sddm/modisdist/index.shtml

  10. Projection issues 30 km MODIS daily MODIS 16-day AVHRR 14-day

  11. Preliminary analysis Histograms: General agreement between ETM+ and MODIS AVHRR, somewhat lower Variability decreases with spatial averaging

  12. Variogram Maps • Semi-variograms in 2-D • allows visual inspection of Anisotropy • 1-D semivariograms canbe extracted

  13. Preliminary analysis: Spatial structure

  14. Regression analysis:Model selection • NDVIY, i = b0 + b1 AVEETM+, i + ei • NDVIY, i = b0 + b1 AVEETM+, i + b2 SDETM+, i ei • NDVIY, i = b0 + b1 AVEETM+, i + b1 (AVEETM+, I)2 ei Best fit, agrees with previous results* * Klökitz, C., van Boxtel, A.; Carfagna, E. and van Deursen, W., 1998. Estimating the Accuracy of Coarse Scale Classification Using High Scale Information. Photogrammetric Engineering and Remote Sensing, 64(2)127-133.

  15. Regression analysis MOD. Daily MOD.comp. AVHRR comp. 500m 1km 2km

  16. MOD09, 500m R2 = 0.6982 MOD13, 500m R2 = 0.6254 MOD09, 1km R2 = 0.9037 MOD13, 1km R2 = 0.8286 AVHRR, 1km R2 = 0.715 MOD09, 2km R2 = 0.9577 MOD13, 2km R2 = 0.9207 AVHRR, 2km R2 = 0.9011 Regression analysis:Joint confidence intervals Regression “target”: One-to-one line

  17. Checking Regression assumptions • Linearity (plot resids vs independent variable) • Constant Variance (plot resids vs fitted values) • Independence (plot residuals in space) • Outliers (examine residuals) • Normally distributed error term (goodness of fit test on residuals) • Independent terms missing from model (from Neter et al., “Applied Linear Regression Models”)

  18. MOD09, 500m MOD13, 500m MOD09, 1km MOD13, 1km AVHRR, 1km MOD09, 2km MOD13, 2km AVHRR, 2km Checking Regression assumptions: map of residuals

  19. MOD09, 500m MOD13, 500m MOD09, 1km MOD13, 1km AVHRR, 1km MOD09, 2km MOD13, 2km AVHRR, 2km Checking Regression assumptions: spatial independence of residuals

  20. Sampling area and size Method one: Increase area, maintainingcenter pixel

  21. Regression results:method one Fitted parameters, +/- 3 standard deviations

  22. Tiled subsets 2.5km per side … 10kmper side 144 subsets at 2.5km subsets, 36 @ 5km, 16 @7.5km, 9 @ 10km

  23. Regression results:method two

  24. 144 subsets2.5km corr. = 0.29 36 subsets 5km corr. = 0.36 16 subsets 7.5km corr. = 0.68 9 subsets 10km Corr. = 0.69 Range in NDVI values vs slope parameter

  25. Conclusion from correlative analysis • Regression analysis and the joint confidence intervals on the slope and intercept terms provide a meaningful summary for validation analysis. • In comparing two coarse resolution products, the comparison should be made with both products at the same resolution. • Here, the daily MODIS product is the most directly related to the averaged ETM+ data; which implies the importance of considering temporal composite issues. • Subset location and size have an affect on the regression parameters. For this area, a 10km subset provided a stable subset size. • These statistical methods can be directly applied to comparing high and coarse resolution LAI products.

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