A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series

By Jorge E. Pinzón, Compton J. Tucker. Remote Sensing, 2014

1,101 citations recorded by the source corpus.

The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/68105b1c75925a0c20616ba544748a7b28c1c17c

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Citations in the corpus

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends (2015, methodology): “…information, used to mask the monthly ET between 1982 and 2010, was first determined from the global inventory modeling andmapping studies normalized difference vegetation index (NDVI3g) dataset (Pinzon and Tucker 2014) using a Savitzky–Golay filter (Chen et al 2004, Jonsson and Eklundh 2004).”
Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions (2014, neutral): “The PAL and GIMMS/3 g records do not apply atmospheric correction, instead using maximum value compositing (MVC) of Top of Atmosphere (TOA) values to preserve the original data patterns [38,39,99].”
Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. (2016, background): “…Advanced Very High Resolution Radiometer (AVHRR) observations available since 1981, although those that are publically available are subject to scientific debate (Myneni, Tucker, Asrar, & Keeling, 1998; Pedelty et al., 2007; Beck et al., 2011; Fensholt & Proud, 2012; Pinzon & Tucker, 2014).”
Quantifying the effectiveness of ecological restoration projects on long-term vegetation dynamics in the karst regions of Southwest China (2017, methodology): “This study uses the GIMMS-3g NDVI, available in a bimonthly temporal resolution of 8 km spatial resolution from 1982 to 2011 (Pinzon and Tucker, 2014).”
Plant phenology and global climate change: Current progresses and challenges (2019, background): “…of satellite‐derived estimates of plant phenological events, such as poor observation conditions (e.g., clouds, snow and ice), BRDF (bidirectional reflectance distribution function) effects, shifts in sensors (Pinzon & Tucker, 2014; Tucker et al., 2005), and coarse spatial and temporal resolutions.”
Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers (2017, methodology): “As part of our analysis, we also examined trends in AVHRR GIMMS3g NDVI (Pinzon and Tucker 2014) over each of the study sites included in our Landsat-based analysis.”
Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data (2016, neutral): “MODIS NBAR NDVI (MCD43C4) (WWW-MCD43C4): Nadir Bidirectional Reflectance 64 Distribution Function (BRDF)-Adjusted Reflectance (NBAR), which spans same temporal 65 coverage (2000-2014) with MOD13C1 is used in this study. Spatial resolution (0.05°) is adjusted 66 into NDVI3g (1/12°) and then growing s”