Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹ (GOVERNMENT PRINCIPAL INVESTIGATOR), Felix Kogan¹ and Wei Guo² ¹NOAA/NESDIS/STAR/SMCD, ²IMSG Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹ (GOVERNMENT PRINCIPAL INVESTIGATOR), Felix Kogan¹ and Wei Guo² ¹NOAA/NESDIS/STAR/SMCD, ²IMSG Volcanic aerosols (Mt. Pinatubo and El Chichon). Calibration uncertainty. Satellite orbital drift. Intersatellite sensor differences. Bidirectional and atmospheric effects. Factors unrelated to ecosystems that affect AVHRR derived NDVI Difference between benchmark and aerosol affected NDVI (1982 – 2007) Latitude NOAA-7 NOAA-9 NOAA-11 NOAA-14 NOAA-18 NOAA Week Week Week Week Week 12 Latitude Band 7.5N – 7N Empirical Distribution Function (EDF) difference between benchmark and aerosol affected NDVI Dynamics of correlation coefficient between end-of-season MG corn yield departure from trend and spatial-average VCI during corn silking (critical period), solid line is normalized NDVI, dashed line is aerosol-affected NDVI. Validation: correlation dynamics of corrected and uncorrected data Higher values of NDVI derived from GIMMS (seen across all latitudes) compared to GVI & LTDR. All time series (GIMMS, GVI and LTDR) are comparable in shape. Time series plots show that the GVI and LTDR datasets are more similar between each other than either is with the GIMMS dataset. There is a trend of increasing NDVI between 1982 and The trends give some insight into the changes that the global vegetation has experienced over the last decades. Differences in datasets are caused by differences in resolution, calibration, atmospheric correction, orbital drift correction, volcanic aerosol correction, etc. All three datasets compared have different levels of correction, are processed differently and have potential errors. This results show the importance of building a unified system to produce a reliable Climate Data Record (CDR) for climate studies. Long Term AVHRR - NDVI Data and Trends (GVI, GIMMS and LTDR datasets, ) LTDR (Land Long Term Data Record) AVHRR data processed by NASA. Daily, 0.05 degree resolution. NDVI product available from 1981 to NDVI from NOAA 7,9,11,14. Calibration (Vermote and Kaufmann, 1995). Atmospheric Correction of channels 1 and 2. Data products available at GIMMS (Global Inventory Modeling and Mapping Studies) AVHRR data processed by the University of Maryland and NASA. 15-Day maximum value composites, 8 km resolution. NDVI product available from 1981 to NDVI from NOAA 7,9,11,14,16 and 17. Calibration (Rao and Chen 1995, and Los 1995). No atmospheric correction, Rayleigh scattering or stratospheric ozone correction. Satellite drift have been corrected using the empirical mode decomposition (EMD) during post-processing (Pinzon 2002). Stratospheric aerosol correction (Vermote et al., 1997). Data available at GVI-x (Global Vegetation Index x-version) AVHRR data processed by NOAA. 7-Day maximum value composites, 4km resolution. NDVI product available from1981 to NDVI from NOAA 7,9,11,14,16,17 and 18. Calibration (Rao and Chen 1995, and Wu 2004). Statistically smoothed NDVI. No atmospheric correction. Normalization for volcanic aerosols, extreme cases of orbital drift and differences between satellites using the Empirical Distribution Function (EDF) statistical technique (Vargas et al. 2009). Data available at ftp://ftp.orbit.nesdis.noaa.gov/ The AVHRR dataset is the longest daily synoptic coverage dataset available for climate studies. The NDVI derived from AVHRR is a proxy for plant photosynthesis and has been extensively used for climate studies. The satellite NDVI record begins in July 1981 and extends to the present time. In this work we compare the NDVI time series and trends extracted from three different publicly available datasets: Global Vegetation Index x-version (GVI-x), Global Inventory Modeling and Mapping Studies (GIMMS) and Land Long Term Data Record (LTDR) over the period The AVHRR-NDVI datasets are subject to uncertainty because of errors associated with sensor and atmosphere related effects. All three datasets compared have different levels of correction, are processed differently and have potential errors. This study compares NDVI time series from the three datasets and estimates trends. Results show that GIMMS has higher values of NDVI compared to GVI-x and LTDR, and also larger upward trends compared with smaller trends derived from GVI-x and LTDR. Time series and trends in spatially averaged NDVI ( ) Top of the Atmosphere (TOA) NDVI Central Africa 1989 Week Week Week 40 Recovery Reduced NDVI 1991 Week and 90 no aerosol 91 reduced NDVI due to aerosol 92 NDVI recovery after dissipation of aerosol Excessive stratospheric aerosol reduce incoming and outgoing radiation affecting NDVI. The statistical method to remove the effect of volcanic aerosols makes use of a Benchmark NDVI. The Benchmark NDVI is calculated from five years of NDVI data (89,90,95,96,97). Benchmark years are not contaminated by volcanic aerosols and not affected by orbital drift. TOA NDVI Time Series (AVHRR) NOAA-7 NOAA-9 NOAA-11 NOAA-14 NOAA-18 NOAA-16 YEAR Years affected by aerosol (yellow shade) Years affected by orbital drift (pink shade) Latitudinal profile of benchmark and aerosol affected NDVI Week TOA – aerosol affected and corrected NDVI YEAR Normalized time series enhances NDVI and exhibits better stability Science Challenges: Generate time series with climate data record quality for climate change studies. Next Steps: Implement system to perform atmospheric correction on entire AVHRR dataset. Transition Path: Generate experimental version, validate and compare results with other available datasets (LTDR, GIMMS), and transition the GVI system to operations through the SPSRB process. Requirement: Improve the quality of climate observations, analyses, interpretation, and archiving by maintaining a consistent climate record and by improving our ability to determine why changes are taking place. Science: How can we remove biases from the vegetation dataset that are unrelated to vegetation and create consistent time series? Benefit: A predictive understanding of the global climate system on time scales of weeks to decades for making informed and reasoned decisions. Aerosol Optical Thickness (after Mt. Pinatubo eruption)