Adjusting for Long-Term Anomalous Trends in NOAA´s Global Vegetation Index Data Sets

The weekly 0.144 resolution global vegetation index from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) has a long history, starting late 1981, and has included data derived from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA-7, -9, -11, -14, -16, -17, and -18 satellites. Even after postlaunch calibration and mathematical smoothing and filtering of the normalized difference vegetation index (NDVI) derived from AVHRR visible and near-infrared channels, the time series of global smoothed NDVI (SMN) still has apparent discontinuities and biases due to sensor degradation, orbital drift [equator crossing time (ECT)], and differences from instrument to instrument in band response functions. To meet the needs of the operational weather and climate modeling and monitoring community for a stable long-term global NDVI data set, we investigated adjustments to substantially reduce the bias of the weekly global SMN series by simple and efficient algorithms that require a minimum number of assumptions about the statistical properties of the interannual global vegetation changes. Of the algorithms tested, we found the adjusted cumulative distribution function (ACDF) method to be a well-balanced approach that effectively eliminated most of the long-term global-scale interannual trend of AVHRR NDVI. Improvements to the global and regional NDVI data stability have been demonstrated by the results of ACDF-adjusted data set evaluated at a global scale, on major land classes, with relevance to satellite ECT, at major continental regions, and at regional drought detection applications.