Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China

Liu, Qiang; Fu, Yongshuo H.; Zeng, Zhenzhong; Huang, Mengtian; Li, Xiran; Piao, Shilong

doi:10.1111/gcb.13081

Cited by 310 publications

(245 citation statements)

References 98 publications

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“…However, by comparing the multi-year mean SOS/EOS of 2000-2004 and that of 2011-2015, we still found a significant advance of SOS in typical steppe and desert steppe, and a significant advance of EOS in desert steppe, which have also been identified by other studies with different data and methods [62,74]. SOS experienced a larger shift than EOS.…”

Section: What Factors Contributed To Sos/eos Trendssupporting

confidence: 79%

“…The predominant role of temperature in affecting EOS has also been observed in Mongolian grasslands [68] and in the Tibetan Plateau [69]. However, other studies reported precipitation to be more important than temperature in determining autumn phenology of temperate grassland ecosystems [47,62]. The discrepant conclusion among different studies may be related to differences in data sample size and acquisition methods, as well as in spatial and temporal scales, but also in the studied vegetation types.…”

Section: Key Factors Of Controlling Sos/eos For the Whole Study Regionmentioning

confidence: 95%

“…It includes seven three-hourly climate variables from 2000 to 2015 at a spatial resolution of 0.1 • × 0.1 • . In this current study, however, because SOS/EOS changes are primarily correlated with the hydrothermal conditions of the previous 2-3 months [31,33,43], and the effects of sunlight on herbaceous plants are relatively weak [8,61,62], only the temperature and precipitation rate were employed. In order to match the temporal resolution of phenological metrics, we calculated daily air temperature and precipitation from the three-hourly air temperature and precipitation data, respectively.…”

Section: Climate Datamentioning

confidence: 99%

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Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Ren

Peichl

et al. 2017

Remote Sensing

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Vegetation phenology in temperate grasslands is highly sensitive to climate change. However, it is still unclear how the timing of vegetation phenology events (especially for autumn phenology) is altered in response to climate change across different grassland types. In this study, we investigated variations of the growing season start (SOS) and end (EOS), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), for meadow steppe, typical steppe, and desert steppe in the Inner Mongolian grassland of Northern China. Using gridded climate data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), we further analyzed correlations between SOS/EOS and pre-season average air temperature and total precipitation (defined as 90-day period prior to SOS/EOS, i.e., pre-SOS/EOS) in each grid. The results showed that both SOS and EOS occurred later in desert steppe (day of year (doy) 114 and 312) than in meadow steppe (doy 109 and 305) and typical steppe (doy 111 and 307); namely, desert steppe has a relatively late growing season than meadow steppe and typical steppe. For all three grasslands, SOS was mainly controlled by pre-SOS precipitation with the sensitivity being largest in desert steppe. EOS was closely connected with pre-EOS air temperature in meadow steppe and typical steppe, but more closely related to pre-EOS precipitation in desert steppe. During 2000-2015, SOS in typical steppe and desert steppe has significantly advanced by 2.2 days and 10.6 days due to a significant increase of pre-SOS precipitation. In addition, EOS of desert steppe has also significantly advanced by 6.8 days, likely as a result from the combined effects of elevated preseason temperature and precipitation. Our study highlights the diverse responses in the timing of spring and autumn phenology to preceding temperature and precipitation in different grassland types. Results from this study can help to guide grazing systems and to develop policy frameworks for grasslands protection.

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Section: What Factors Contributed To Sos/eos Trendssupporting

confidence: 79%

Section: Key Factors Of Controlling Sos/eos For the Whole Study Regionmentioning

confidence: 95%

Section: Climate Datamentioning

confidence: 99%

See 1 more Smart Citation

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Ren

Peichl

et al. 2017

Remote Sensing

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“…Major advancement in remote sensing allows the quantification of dates of major phenological events using space-borne instruments [11,13,41,44,57,88]. It was demonstrated that in spite of their different spatial scales satellite based SOS and EOS estimations roughly match the in situ phenology observations [7,11,41,57,59,60].…”

Section: Phenology and Ndvi Variabilitymentioning

confidence: 99%

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

2016

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Abstract:Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate-NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.

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“…spatiotemporal variations of vegetation activities caused by climate change, including plant phenology [9][10][11][12][13], net primary productivity [14][15][16][17], vegetation cover [18][19][20][21][22], biomass [23][24], and carbon process models [25], have used various methods. These studies have revealed that vegetation activities have increased at middle and high latitudes of the northern hemisphere over the past few decades.…”

mentioning

confidence: 99%

Spatiotemporal Variations of Growing-season NDVI Associated with Climate Change in Northeastern China’s Permafrost Zone

Guo¹,

Hu²,

Xiong³

et al. 2017

Pol. J. Environ. Stud.

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Vegetation is an essential component of terrestrial ecosystems, and is important in regulating climate change, the carbon cycle, and energy exchange via biophysical factors such as photosynthesis, evapotranspiration, surface albedo, and roughness [1][2][3][4]. Vegetation has obvious interannual and seasonal variations that are recognized as indicators for the detection of climate trends in global change studies [5][6][7][8]. Thus, we can better understand and simulate the dynamic characteristics of terrestrial ecosystems and reveal the regulation of global change by monitoring long-term vegetation variations. Studies of the Pol. J. Environ. Stud. Vol. 26, No. 4 (2017), 1521-1529 Original Research AbstractVegetation is an essential component of terrestrial ecosystems, and it plays an important role in regulating climate change, the carbon cycle, and energy exchange. And permafrost is extremely sensitive to climate change. In particular, aboveground vegetation on permafrost has great sensitivity to that change. The permafrost zone of northeastern China, within middle and high latitudes of the northern hemisphere, is the second-largest region of permafrost in China. It is at the southern edge of the Eurasian cryolithozone. This study analyzes growing-season spatiotemporal variation of the normalization difference vegetation index (NDVI) in this permafrost zone and the correlation between NDVI and climate variables during 1981-2014. Mean growing-season NDVI significantly increased by 0.0028 yr -1 over the entire permafrost zone. The spatial dynamics of vegetation cover in the zone had strong heterogeneity on the pixel scale. Pixels that showed increasing trends accounted for 80% of the permafrost area, and were mostly found in the permafrost zone with the exception of western steppe regions. Pixels that showed decreasing trends (approximately 20% of the permafrost area) were mainly in the cultivated and steppe portions of the study area. Our results indicated that temperature was the dominant influence on vegetation growth during the growing season in most permafrost zones.

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Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China

Cited by 310 publications

References 98 publications

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

Spatiotemporal Variations of Growing-season NDVI Associated with Climate Change in Northeastern China’s Permafrost Zone

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