1. MOLLY E. BROWN, PHD NASA GODDARD GIMMS Group Challenges of AVHRR Vegetation Data for Real Time Applications

2. 2 Context Monitoring agricultural production in Africa is a critical part of maintaining food security. Rainfall products are not reliable – thus NDVI is used instead. NDVI = NIR + Red NIR - Red NDVI is a measure of vegetation condition and health.

3. 3 AVHRR sensor provides the longest record AVHRR: 26+ year record 1981-2008 (present) AVHRR Data provides information about vegetation health and density at a global scale.

4. 4 NOAA 11 AVHRR 1980200019901985201020051995 NOAA 7 AVHRR NOAA 9 AVHRR NOAA 14 AVHRR SPOT NOAA-17 NOAA9 N- 16 1982-20021992-2002 Decadal trends in terrestrial vegetation NOAA-18

5. 5 Objectives Compare real time vegetation index data to historical AVHRR NDVIg during overlap period Data products:  the real-time G product (RG),  the NOAA-17 real time product (N17)  the S10 SPOT Vegetation data (SP) for comparison For identifying anomalies ( departure from the normal ) in growing conditions in Africa Short latency (9 hours after acquisition/composite) necessary for real time tracking of crop growth New products on the horizon

6. 6 GIMMS NDVIg product(benchmark) Uses NOAA meteorological satellite series 7, 9, 11, 14 and 16, 17 and 18 Processing uses empirical mode decomposition technique developed by Jorge Pinzon Is only updated annually, and thus the current data spans from 1981 July to December 2006. Is the state of the art, best available AVHRR dataset for conducting scientific analyses of vegetation dynamics. G

7. 7 Product 1: RG (real time G) product Uses same empirical mode decomposition technique as the NDVIg – exactly the same code Does not include removal of cloudy pixels like G product Calibration updated annually or less frequently when the NDVIg is updated Uses traditional T5 cloud screening (285 dK for Africa) Assumes N16 and N17 do not have significant orbital degradation R

8. 8 Product 2: N17 (NOAA17) product N17 product uses the flags embedded in the raw NOAA data to remove water, cloudy, snow, and ice pixels Monthly NOAA near real-time post-launch calibration coefficients to adjust for degradation of the AVHRR/3 visible channels (1, 2, and 3A). NOAA operational coefficients are based on vicarious calibrations and MODIS measurements over a stable desert target and cross calibration among POES sensors based on Simultaneous Nadir Overpass (SNO). This release applies the initial calibration coefficient update from 2002 Sep 1 to 2004 Jun 9 Monthly updates of calibration thereafter. N

9. 9 Comparison product: SPOT Vegetation SPOT vegetation data spans from 1998 to the present and is also produced in real time. The data is derived from the SPOT Vegetation sensor and is processed in Europe The data provides a useful comparison to AVHRR data products. S

10. 10 Methods Compare time series of R and N products by location and by image Use NDVIg as a benchmark –  Are the anomalies we see in the R and N products similar?  Difference the G and N/R to determine which is more similar Compare to SPOT data – are the anomalies we see in the G, R and N similar to SPOT? Conclude and make recommendations

11. 11 Map of comparison points

12. 12 Results – time series G – NDVIg RG – Real time NDVIg N – NOAA-17 S - SPOT

13. 13 NDVIg - Real Time GNDVIg- NOAA-17 Difference between NDVIg and Real Time G and NOAA-17

14. 14 Anomaly G – NDVIg RG – Real time NDVIg N – NOAA-17 S - SPOT

15. 15 Anomaly - Senegal Both the N-N and the N-G have different anomaly than G product

16. 16 Mean vs Std of Anomaly

17. 17 Images of July 2005 NDVIgNOAA-17 Real Time G SPOT

18. 18 Histogram of July Image

19. 19 Anomaly images (self), July 05

20. 20 Conclusions Neither real-time AVHRR dataset is very good The RG compares better with the G dataset The RG is more consistently like the G dataset The N17 data is more self- consistent than the RG The N17 has more missing data due to clouds, but is able to capture the same anomaly pattern as the G and SPOT (unlike the RG)

21. 21 Recommendation For anomalies during the past five years, I would recommend the N17 product For anomalies from the 25 year record, I would recommend staying with the RG product as the differences between N17 and the NDVIg are large and intractable

22. 22 New Products GLAM daily MODIS NDVI products to be available by December – 9 hr latency, collection 5 algorithms  Comparable to MOD09 product LTDR product may provide opportunity for bringing together the long AVHRR record with MODIS  0.05 degree resolution, daily, all 5 channels mapped and corrected (temperature, reflectance)  Very complete and accurate atmospheric correction, which removes effect of satellite drift  Will be combined with MODIS CMG by using a 2003 overlap period