Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data

Ni, Xiliang; Xie, Jianfeng; Zhou, Yuke; Gao, Xinliang; Ding, Lin

doi:10.3390/cli5020037

Cited by 3 publications

(2 citation statements)

References 55 publications

(91 reference statements)

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“…This dataset was developed with a neural network-based approach from the Normalized Difference Vegetation Index NDVI 3g and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of Photosynthetically Active Radiation (fAPAR) and LAI data products. Previous studies have used this dataset to derive both greening and browning trends and LSP trends (Xiao and Moody 2004, Liu et al 2010, Ni et al 2017, Cook and Pau 2013. LAI 3g was thoroughly evaluated in Zhu et al (2013), who used comparisons with both groundbased LAI measurements and satellite products such as SPOT-Vegetation to demonstrate the quality and research applicability of this dataset for monitoring global vegetation dynamics.…”

Section: Time Series Of Leaf Area Index (1982-2011)mentioning

confidence: 99%

Shifting relative importance of climatic constraints on land surface phenology

Garonna

Jong

Stöckli

et al. 2018

Environ. Res. Lett.

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Section: Time Series Of Leaf Area Index (1982-2011)mentioning

confidence: 99%

Shifting relative importance of climatic constraints on land surface phenology

Garonna

Jong

Stöckli

et al. 2018

Environ. Res. Lett.

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“…Study area Study period Satellite data SOS EOS (Qiao and Wang, 2019) Inner Mongolia, China 1982-2015 AVH09C1 À0.30 (Li et al, 2021a) Xinjiang, China 1982-2014 GIMMS3g À0.19 (Ni et al, 2017) Northeastern China 1982-2011 GIMMS LAI3g À0.26 0.11 (Yu et al, 2017) Northeast China 1982-2015 GIMMS NDVI À0.13 0.25 (Luo and Yu, 2017) Northern China 2001-2014 MOD13A2 À0.34 0.20 China's temperate 1982-1999 AVHRR À0.79 0.37 (Liu et al, 2016a) Temperate China 1982-2011 GIMMS3g 0.12 ± 0.01 (Zhou et al, 2020) Temperate China 1982-2015 GIMMS3g À0.12 ± 0.03 (Shen et al, 2018) Temperate grasslands of China 1982-2015 GIMMS3g À0.18 (Piao et al, 2011) Tibetan Plateau 1982-1999 GIMMS À0.88 (Zhang et al, 2013) Tibetan Plateau 1982-1998 GIMMS À1.02 (Zhang et al, 2013) Tibetan Plateau 1998-2006 GIMMS 2.33 (Zhang et al, 2013) Tibetan Plateau 2000-2011 SPOT-VGT À1.36 (Zhang et al, 2013) Tibetan Plateau 2000-2011 MOD13A2 À0.78 (Zu et al, 2018) Tibetan Plateau 2000-2015 MOD13A2 À0.45 À0.05 (Peng et al, 2021b) Tibetan Plateau 1982-2018 AVHRR LTDR NDVI 0.01 (Luo and Yu, 2017) Southern China 2001-2014 MOD13A2 0.79 (Li et al, 2021b) Subtropical forest of China 2002-2017 GOSIF À0.68 (Wang et al, 2018) China's Grasslands The VIPPHEN dataset À0.23 ± 0.47 0.17 ± 0.46 vegetation phenology (Fu et al, 2021;Ge et al, 2015;Su et al, 2021), for example, on the Qinghai-Tibet Plateau (An et al, 2020;Shen et al, 2014Shen et al, , 2016Yang, 2021;Zhang et al, 2018;Zhu et al, 2017). China's phenological research accounted for 3124 out of 39,268 papers on the subject of "phenology" between 1980 and 2020 according to the Web of Science platform (Figure 1 (Chen et al, 2017;Dai et al, 2019;Ge et al, 2015;Wang et al, 2015a).…”

Section: Referencesmentioning

confidence: 99%

Review of vegetation phenology trends in China in a changing climate

Zhang

Chen

et al. 2022

Progress in Physical Geography: Earth and Environment

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Vegetation phenology is sensitive to climate change and has been defined as the footprint of ongoing climate change. Previous studies have shown that the spatial difference in China’s vegetation phenology varies substantially in both spring and autumn. Here, we reviewed phenological dynamics at the national and the regional scale of China over the period 1982−2020 using a remote sensing-based dataset and meta-analysis from phenological studies in China. We also explored the underlying mechanisms of both spring and autumn phenology and discussed potential phenological studies under future climate conditions. We found that, over the past four decades, the spring phenology advanced at a rate of 0.23 ± 0.47 days/year, while the autumn phenology was delayed at a rate of 0.17 ± 0.46 days/year. This led to an extended vegetation growth season of approximately 5 days per decade. The trends in the spring and autumn phenology were spatially specific in the Northern region, Northwest region, Qinghai–Tibet region, and Southern region: the change in spring phenology was −0.16, −0.46, −0.18, and −0.13 days/year, respectively, while the change in autumn phenology was 0.02, 0.32, 0.09, and 0.28 days/year, respectively. We also explored the dominant climatic drivers of regional phenological changes. We found that temperature was the dominant factor for spring phenology in cold regions, while precipitation, radiation, and temperature co-determined spring phenology in warm regions. The autumn phenology was affected by all three environmental cues but the effect of temperature was larger than that of radiation and precipitation across all regions. In future climate warming conditions, we recommend that studies focus on the phenological feedback mechanisms, such as the climatic and hydrological effects of vegetation changes, and agricultural phenology to investigate its fundamental role in crop productivity, especially under extreme climate events, to ensure national food security and ecological security.

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Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years

Fan

Yuan

et al. 2021

Remote Sensing

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Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change.

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Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data

Cited by 3 publications

References 55 publications

Shifting relative importance of climatic constraints on land surface phenology

Shifting relative importance of climatic constraints on land surface phenology

Review of vegetation phenology trends in China in a changing climate

Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years

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