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Remote Sens. 2014, 6(8), 7320-7338; doi:10.3390/rs6087320

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

1
Center for Earth System Science, Tsinghua University, Beijing 100084, China
2
Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Received: 6 January 2014 / Revised: 28 June 2014 / Accepted: 9 July 2014 / Published: 6 August 2014
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Abstract

Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008–2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI), MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes. View Full-Text
Keywords: phenology; MODIS; PERSIANN; EVI; LST; time series phenology; MODIS; PERSIANN; EVI; LST; time series
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Clinton, N.; Yu, L.; Fu, H.; He, C.; Gong, P. Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution. Remote Sens. 2014, 6, 7320-7338.

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