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Remote Sens. 2013, 5(8), 3918-3950; doi:10.3390/rs5083918
Article

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

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Received: 24 May 2013; in revised form: 23 July 2013 / Accepted: 23 July 2013 / Published: 8 August 2013
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Abstract: Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability in the new 30.5-year long global satellite-derived surface reflectance index data, Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). Pearson’s correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation anomalies. Temperature correlations were strongest in northern mid- to high-latitudes in the spring and early summer where up to 70% of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America, winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wet season precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.
Keywords: GIMMS NDVI3g; climate-driven interannual variability; interference GIMMS NDVI3g; climate-driven interannual variability; interference
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zeng, F.-W.; Collatz, G.J.; Pinzon, J.E.; Ivanoff, A. Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales. Remote Sens. 2013, 5, 3918-3950.

AMA Style

Zeng F-W, Collatz GJ, Pinzon JE, Ivanoff A. Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales. Remote Sensing. 2013; 5(8):3918-3950.

Chicago/Turabian Style

Zeng, Fan-Wei; Collatz, G. J.; Pinzon, Jorge E.; Ivanoff, Alvaro. 2013. "Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales." Remote Sens. 5, no. 8: 3918-3950.


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