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Cited by 611 publications
(424 citation statements)
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References 54 publications
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“…Of the areas with an extended LGS, 76.53% showed a positive correlation with precipitation, but only 58.9% positively correlated with temperature. This result confirms again that precipitation is one of the key factors in changing the vegetation phenology in water-limited environments (Yu, 2003;Fensholt et al 2012).…”
Section: Discussionsupporting
confidence: 83%
“…Of the areas with an extended LGS, 76.53% showed a positive correlation with precipitation, but only 58.9% positively correlated with temperature. This result confirms again that precipitation is one of the key factors in changing the vegetation phenology in water-limited environments (Yu, 2003;Fensholt et al 2012).…”
Section: Discussionsupporting
confidence: 83%
“…A previous study (Du et al 2014) on independently assessing the performance of GIMMS and MODIS NDVI using Landsat samples indicated that spatial patterns and dynamic trends of GIMMS NDVI were in overall acceptable agreement with MODIS NDVI. Correlations and differences between the two datasets on the TP are both superior to those from corresponding results in other arid regions of the world (Fensholt et al 2012), and in northeast China (Mao et al 2012). This indicates that the GIMMS and MODIS NDVI datasets show relatively high agreement on the TP and that the use of the MODIS to extend the time series of the GIMMS is appropriate.…”
Section: Discussion Integrated Use Of Gimms and Modis Ndvi Datasetsmentioning
confidence: 77%
“…Using a spatial average resampling method like Fensholt et al (2009) andFensholt et al (2012), the MODIS NDVI was resampled to a spatial resolution of 8 km 9 8 km to be consistent with the GIMMS NDVI datasets. Pixels with a mean growing season NDVI \0.10 were excluded in this study to reduce the influence of sparsely vegetated pixels on the NDVI trend following the lead of earlier related research studies (Mohammat et al 2013;Piao et al 2011a, b;Zhang et al 2013;Zhao et al 2011).…”
Section: Data Sources and Processingmentioning
confidence: 99%
See 1 more Smart Citation
“…Because of the low spatial resolution and wide swath, these datasets are typically used for vegetation analysis at the global, national, and regional scales (Rigina and Rasmussen 2003;Ma et al 2006;Fensholt et al 2012). These data, however, are unable to discern the spatial heterogeneity of land cover within the urban areas Zhou and Troy 2008).…”
Section: Pros and Cons Of Low Medium And High Resolution Datamentioning
confidence: 99%