1. Satellite Cross comparisonMorisette 1 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 Statistical regression analysis, initial results (comparing NDVI from ETM+, MODIS, and AVHRR) Future plans for comparing multiple LAI products

2. Satellite Cross comparisonMorisette 2 EOS Land Validation Core Sites http://modarch.gsfc.nasa.gov/MODIS/LAND/VAL

3. Satellite Cross comparisonMorisette 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 data graphic courtesy of the BigFoot program Field and airborne data: archive and access through ORNL DAAC’s “Mercury System” AERONET and FLUXNET data

4. Satellite Cross comparisonMorisette 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. Satellite Cross comparisonMorisette 5 Spectral Characteristics 567 57 45 67 8 9 575’ 1243 1 2 3 4 5 6 7 8 12 1243 Pan. 123 12434’1’ Pan. 1243 MODIS SeaWiFS AVHRR ETM+ ASTER ALI IKONOS Hyperion

6. Satellite Cross comparisonMorisette 6 Core Site data summary

7. Satellite Cross comparisonMorisette 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. Satellite Cross comparisonMorisette 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 R 2 alone do not tell the whole story Regression analysis includes several assumptions Subset size and location will influence results

9. Satellite Cross comparisonMorisette 9 http://edc.usgs.gov/programs/sddm/modisdist/index.shtml ISINISINGeographicGeographic MODIS Projection issues

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

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

12. Satellite Cross comparisonMorisette 12 Variogram Maps Semi-variograms in 2-D allows visual inspection of Anisotropy 1-D semivariograms can be extracted

13. Satellite Cross comparisonMorisette 13 Preliminary analysis: Spatial structure

14. Satellite Cross comparisonMorisette 14 Regression analysis: Model selection NDVI Y, i = b 0 + b 1 AVE ETM+, i + e i NDVI Y, i = b 0 + b 1 AVE ETM+, i + b 2 SD ETM+, i e i NDVI Y, i = b 0 + b 1 AVE ETM+, i + b 1 (AVE ETM+, I ) 2 e i 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. Satellite Cross comparisonMorisette 15 Regression analysis 500m 1km 2km MOD. Daily MOD.comp. AVHRR comp.

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

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

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

20. Satellite Cross comparisonMorisette 20 Sampling area and size Method one: Increase area, maintaining center pixel

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

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

23. Satellite Cross comparisonMorisette 23 Regression results: method two

24. Satellite Cross comparisonMorisette 24 Range in NDVI values vs slope parameter 144 subsets 2.5km corr. = 0.29 36 subsets 5km corr. = 0.36 16 subsets 7.5km corr. = 0.68 9 subsets 10km Corr. = 0.69

25. Satellite Cross comparisonMorisette 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.