Inter-Sensor Validation of NDVI time series from AVHRR, SPOT-Vegetation, SeaWIFS, MODIS, and LandSAT ETM+

1. Inter-Sensor Validation of NDVI time series from AVHRR, SPOT-Vegetation, SeaWIFS, MODIS, and LandSAT ETM+ Molly E. Brown + Jorge E. Pinzon+ Jeffery T. Morisettex Kamel Didan* Compton J. Tuckerx + SSAI, NASA Goddard Space Flight Center * Soil, Water and Environmental Sciences Greenbelt, MD 20771 University of Arizona xNASA Goddard Space Flight Center Greenbelt, MD 20771 Molly E. Brown, PhD

2. Overview • Data used in study • Global NDVI datasets, LandSAT ETM+ for comparison • Methods • Spectral, spatial and temporal considerations • Global 1 degree datasets • CEOS sites and drought locations • Results • Discussion – data continuity from AVHRR through MODIS to VIIRS Molly E. Brown, PhD

3. VIS/NIR/SWIR Band Comparison VGT SeaWiFS AVHRR MODIS Differences in spectral range will necessitate increased processing in AVHRR and SPOT due to water vapor sensitivity. Molly E. Brown, PhD

4. Data Molly E. Brown, PhD

5. CEOS Land Validation Sites Validation Methods:59 Sites • Aggregations to monthly time step and 1 degree resolution for pixel by pixel comparison. • Global hemispherical means created to provide direct comparison of NDVI behavior. • Comparisons of time series created from 25x25 km box at native temporal and spatial resolutions: CEOS sites, locations of droughts, deserts, agricultural production regions, etc. • Anomaly and seasonal characteristics evaluated • Atmospherically corrected, 25x25km subsets of selected LandSAT ETM+ scenes provide a base for comparison of datasets. Molly E. Brown, PhD

6. Maps of NDVI correlation at 1degree Molly E. Brown, PhD

7. Global averages show that • Four sensors have similar • signals. • Improvements in AVHRR • NDVI have reduced many • differences between the • sensors, enabling a direct • comparison between the • records: • Longer base means for anomaly • Multiple data sources for NDVI • More work to be done for • data integration to be operational Molly E. Brown, PhD

8. Results from CEOS Sites: Harvard, Massachusetts Correlations: AV-SP 0.89 AV-MO 0.84 AV-SW 0.86 Note: similarity in range, seasonality of NDVI LandSAT scene range of variation Differences in treatment of winter, clouds Molly E. Brown, PhD

9. Correlations: AV-SP 0.85 AV-MO 0.82 AV-SW 0.66 Correlations: AV-SP 0.59 AV-MO 0.65 AV-SW 0.59 Molly E. Brown, PhD

10. + Correlations AV-SP 0.60 AV-MO 0.33 AV-SW 0.38 MODIS cloud and aerosol atmospheric correction explains the differences between MODIS and the other sensors. Molly E. Brown, PhD

11. Anomaly Time Series: Drought Detection Molly E. Brown, PhD

12. Conclusions • Many lessons have been learned from the creation of a consistent NDVI record from AVHRR • How to integrate sensors with different gains (NOAA 7-14 and NOAA 16-17) • Overcome sensor limitations to reduce clouds, reduce noise and improve image coherence • More work to be done on further integrating the records of AVHRR, MODIS, SPOT, SeaWIFS to maximize their various strengths, minimizing their weaknesses • AVHRR – MODIS – VIIRS data continuity will be required to maximize length of record to answer important science questions Molly E. Brown, PhD

13. Thanks go to Brad Doorn, Assaf Anyamba and Jennifer Small for providing the SPOT data, Gene Feldman, Norman Kuring and Jacques Descloitres for the monthly global SeaWIFS data. • URLs: • GIMMS NDVIg: http://landcover.org • SeaWIFS: http://daac.gsfc.nasa.gov/data/dataset/SEAWIFS_LAND • MODIS: http://edcdaac.usgs.gov/modis/dataproducts.asp • SPOT VGT: http://free.vgt.vito.be/ • Subsets of SPOT, AVHRR, MODIS tiles, and Landsat ETM+ data at CEOS sites: http://landval.gsfc.nasa.gov/MODIS/index.php Thank you! Molly E. Brown, PhD