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Cited by 447 publications
(258 citation statements)
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References 52 publications
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“…NDVI of the growing season and different seasons were calculated as the mean of the NDVI values for the corresponding months. The method of leastsquares linear regression is characterized by simplicity and robustness (Peng et al 2012b), and the results are easy to interpret and compare between datasets and between pixels (Fensholt et al 2009). This statistical technique was therefore used to estimate linear time trends of NDVI and three climate variables (temperature, precipitation and sunshine duration) during given periods at regional and pixel scales, and the significance level (P) of these variables was determined using the F test.…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…NDVI of the growing season and different seasons were calculated as the mean of the NDVI values for the corresponding months. The method of leastsquares linear regression is characterized by simplicity and robustness (Peng et al 2012b), and the results are easy to interpret and compare between datasets and between pixels (Fensholt et al 2009). This statistical technique was therefore used to estimate linear time trends of NDVI and three climate variables (temperature, precipitation and sunshine duration) during given periods at regional and pixel scales, and the significance level (P) of these variables was determined using the F test.…”
Section: Methodsmentioning
confidence: 99%
“…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%
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“…GIMMS AVHRR-NDVI was important model inputs for the calibration and validation. It was approved to be well-suited for vegetation studies of arid and semi-arid areas (Fensholt et al, 2009), which makes it helpful in building regional ET model. SPOT VEG-ETATION NDVI was used in model application before and after GFG project.…”
Section: Regional Database For Et Model Calibration Validation and Amentioning
confidence: 99%
“…SPOT VEG-ETATION NDVI was used in model application before and after GFG project. It was considered an improvement over AVHRR GIMMS especially in spatial resolution (Fensholt et al, 2009). The uncertainty caused by using different vegetation sensors was reduced by the following trend analysis of model application.…”
Section: Regional Database For Et Model Calibration Validation and Amentioning
confidence: 99%