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Peter Romanov, CICS-University of Maryland Hui Xu, IMSG Inc.

Application of geostationary satellite data to derive normalized difference vegetation index (NDVI). Peter Romanov, CICS-University of Maryland Hui Xu, IMSG Inc. Background. NDVI: provides information on vegetation condition In the past derived only from polar orbiting satellite data

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Peter Romanov, CICS-University of Maryland Hui Xu, IMSG Inc.

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  1. Application of geostationary satellite data to derive normalized difference vegetation index (NDVI) Peter Romanov, CICS-University of Maryland Hui Xu, IMSG Inc.

  2. Background NDVI: provides information on vegetation condition In the past derived only from polar orbiting satellite data NDVI from Geostationary satellites: Since 2005: Meteosat-8 SEVIRI Since 2014: GOES-R ABI Objective: evaluate potentials for NDVI retrievals from GOES-R. Meteosat-8 SEVIRI is used as GOES-R ABI prototype

  3. Outline • Background: NDVI • Geo vs polar orbiting satellites • NDVI: advantages of geo satellites • Problems in NDVI mapping and monitoring

  4. Normalized Difference Vegetation Index • NDVI = ( Rnir - Rvis ) / ( Rnir + Rvis ) • Rnir: Near IR reflectance (0.9 µm) • Rvis: Visible reflectance (0.6 µm) Larger index value corresponds to “greener” vegetation Clouds and snow: Low positive NDVI Water: Negative NDVI

  5. NDVI monitoring at NOAA Afternoon NOAA AVHRR data are used Maximum NDVI compositing Weekly maps since 1981 Global coverage 4 and 16 km resolution

  6. Problems in NDVI from polar satellites Cloud contamination - Gaps in spatial coverage - Discontinuity in time series Reflectance anisotropy in reflectances, hence in NDVI - NDVI spurious variations caused by variable geometry of observations Other problems: - Variable atmospheric attenuation - Sensor degradation - Polar satellite orbital drift

  7. NDVI: polar vs geo satellites Geostationary Frequent (15-30 min) observations Better chance for cloud-clear view Possibility to observe any location at slowly changing geometry of observations But Limited coverage (low and mid latitudes)

  8. NDVI from geo satellites: Approach Daily map: Maximum NDVI compositing and cloud identification Weekly map: Maximum NDVI compositing (same as NOAA AVHRR)

  9. NDVI from geo satellites: Approach Daily map: Maximum NDVI compositing and cloud identification Weekly map: Maximum NDVI compositing (same as NOAA AVHRR)

  10. cloud cloud NDVI map NDVI map False color image False color image Daily max NDVI composite NDVI map NDVI map Effect of daily compositing Image time: 13.15 UTC June 23, 2007

  11. cloud cloud NDVI map NDVI map False color image False color image Weekly compositing One image per day (13.15 UTC) Max NDVI compositing All daily images Daily max NDVI composite Week June 16-23, 2007

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