2017
DOI: 10.3390/rs9010065
|View full text |Cite
|
Sign up to set email alerts
|

Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014

Abstract: Abstract:Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a period during which China has experienced remarkably strong El Niño events. In addition, even fewer studies have focused on changes of the end of season (EOS) and length of season (LOS) despite their importance… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

4
34
2

Year Published

2017
2017
2024
2024

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 42 publications
(40 citation statements)
references
References 63 publications
4
34
2
Order By: Relevance
“…Due to autocorrelation among the inter-annual time series data, a robust non-parametric Mann-Kendall (M-K) trend analysis [46] was applied. This method did not require the independence and normality of the time series data [47], which has been widely used in trend analysis [19]. Previous studies have reported that the M-K test statistic Z was approximately normally distributed when the sample size was n ≥ 8.…”
Section: Trend Analysis and Turning Point Year Detectionmentioning
confidence: 99%
“…Due to autocorrelation among the inter-annual time series data, a robust non-parametric Mann-Kendall (M-K) trend analysis [46] was applied. This method did not require the independence and normality of the time series data [47], which has been widely used in trend analysis [19]. Previous studies have reported that the M-K test statistic Z was approximately normally distributed when the sample size was n ≥ 8.…”
Section: Trend Analysis and Turning Point Year Detectionmentioning
confidence: 99%
“…Time series NDVI is often used when investigating land cover or change using vegetation phenology. Methods based on line interpolation [13,14], fast Fourier transformation (FFT) [15,16], and wavelet [17][18][19] have been generally used to correct pixels containing clouds included in a time series NDVI profile. However, these correction methods have limitations in minimizing cloud effects because the input data for profile correction are also used by data affected by clouds.…”
Section: Discussionmentioning
confidence: 99%
“…The EVI was developed to be more sensitive in high biomass regions and minimize the disturbance of the soil background [32,33]. Furthermore, by utilizing a constrained-view angle-maximum value composite (CV-MVC) method, the EVI dataset was processed to reduce the disturbances from clouds, atmosphere, sensors, and surface bidirectional reflectance [32,34].…”
Section: Modis Evi Productmentioning
confidence: 99%