An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Tucker, Compton J.; Pinzón, Jorge Enrique Dí­az; Brown, M. E.; Slayback, D. A.; Pak, Edwin; Mahoney, Robert; Vermote, Éric; Saleous, Nazmi

doi:10.1080/01431160500168686

Cited by 2,054 publications

(1,411 citation statements)

References 36 publications

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“…Here we use a phenology model to transform the bimonthly NDVI dataset into phenological parameters. Previous research and extensive use of GIMMS NDVI and phenology metrics in Africa has shown that the start of season and cumulative NDVI are the most stable and reliable parameters, with the longest record of use and publications in Africa Tucker et al, 2005). Our interest is to understand the impact of climate variability on growing season quality, as measured by phenological parameters and we are able to move significantly closer to understanding the impact of climate variability on agriculture.…”

Section: Discussionmentioning

confidence: 99%

The response of African land surface phenology to large scale climate oscillations

Brown

Beurs

Vrieling

2010

Remote Sensing of Environment

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129

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Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the FebruaryJune season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.Published by Elsevier Inc.

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Section: Discussionmentioning

confidence: 99%

The response of African land surface phenology to large scale climate oscillations

Brown

Beurs

Vrieling

2010

Remote Sensing of Environment

Self Cite

129

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“…The data were collected daily at a 1.1 km spatial resolution and were upscaled to an 8 km, bi-weekly composite Downloaded by [University of Edinburgh] at 01:35 01 May 2012 to create the Global Inventory Modeling and Mapping Studies (GIMMS) data set (Tucker et al 2005).…”

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confidence: 99%

Vegetation shifts observed in arctic tundra 17 years after fire

Barrett

Rocha

Weg

et al. 2012

Remote Sensing Letters

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“…NDVI calculations from the AVHRR record are sensitive to the instrument intercalibration of the various NOAA satellites over time, as well as to sensor degradation and satellite drift [Cracknell, 1997;Gutman, 1999;Tucker et al, 2005]. For instance, a significant jump in the NDVI was observed at the end of 1999 in the NOAA/NASA pathfinder data set, corresponding to the switch from NOAA-14 to NOAA-16: these two AVHRR instruments were built by two different manufacturers.…”

Section: Introductionmentioning

confidence: 99%

Interannual variability of surface water extent at the global scale, 1993–2004

et al. 2010

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[1] Land surface waters play a primary role in the global water cycle and climate. As a consequence, there is a widespread demand for accurate and long-term quantitative observations of their distribution over the whole globe. This study presents the first global data set that quantifies the monthly distribution of surface water extent at ∼25 km sampling intervals over 12 years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004). These estimates, generated from complementary multiple-satellite observations, including passive (Special Sensor Microwave Imager) and active (ERS scatterometer) microwaves along with visible and near-infrared imagery (advanced very high-resolution radiometer; AVHRR), were first developed over 1993-2000. The ERS encountered technical problems in 2001 and the processing scheme had to be adapted to extend the time series. Here we investigate and discuss the adjustments of the methodology, compare the various options, and show that the data set can be extended with good confidence beyond 2000, using ERS and AVHRR mean monthly climatologies. In addition to a large seasonal and interannual variability, the new results show a slight overall decrease in global inundated area between 1993 and 2004, representing an ∼5.7% reduction of the mean annual maximum in 12 years. The decrease is mainly observed in the tropics during the 1990s. Over inland water bodies and large river basins, we assess the variability of the surface water extent against related variables such as in situ river discharges, altimeter-derived and in situ river/floodplain water level heights, and precipitation estimates. This new 12 year data set of global surface water extent represents an unprecedented source of information for future hydrological or methane modeling.

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An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Cited by 2,054 publications

References 36 publications

The response of African land surface phenology to large scale climate oscillations

The response of African land surface phenology to large scale climate oscillations

Vegetation shifts observed in arctic tundra 17 years after fire

Interannual variability of surface water extent at the global scale, 1993–2004

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