Theoretical and Applied Climatology

, Volume 137, Issue 3–4, pp 1659–1674 | Cite as

Variations in land surface phenology and their response to climate change in Yangtze River basin during 1982–2015

  • Moxi Yuan
  • Lunche WangEmail author
  • Aiwen Lin
  • Zhengjia Liu
  • Sai Qu
Original Paper


Studying the shifts of vegetation phenology and the response of vegetation phenology to climate change helps to understand the dynamics of future ecosystems. However, since previous studies mostly focused on temperate ecosystems, much less is known about the biogeographic phenological shifts in sub-tropical regions, which have abundant biodiversity. The Yangtze River Basin (YRB) is located in the subtropical region of China and has abundant natural resources, a large population, and rapid economic development. Studying the variation characteristics of phenology and its responses to recent climate changes in YRB are important for understanding the impact of regional climate on subtropical ecosystems. In this study, we extracted the phenological parameters using Global Inventory Modeling and Mapping Studies (GIMMS) data to investigate the spatial and temporal variations of vegetation phenology across YRB during 1982–2015 and to examine how vegetation phenology responds to climate within different ecological zones. The results revealed that the start of growing season (SOS) was significantly advanced by 0.2 days/year (p < 0.01). However, there has been no significant trend in the end of growing season (EOS) throughout the whole study area for the past 34 years. The spatial pattern of the phenology metrics showed a high spatial heterogeneity: the SOS in the central YRB was earlier than that in other regions; the EOS in the southeast YRB was later than that in any other regions. Meanwhile, the SOS had a higher correlation with temperature than with precipitation. In particular, the spring temperature had a strong impact on the SOS and the effects of winter temperatures cannot be ignored. Although there were no significant correlations between the EOS and precipitation/temperature, it is interesting to note that when examining the interactions between phonological parameters, the EOS was positively correlated with the SOS. Furthermore, the pre-season temperature had a lag effect on the SOS, but no significant lag effect was observed for the EOS in YRB. In all, the present study can enhance our understanding of phenology dynamics and its relationship with climate in YRB and provide a useful reference to put forward a corresponding ecological protection policy.



We would like to thank China Meteorological Administration (CMA) for providing the meteorological data.

Funding information

This work was financially supported by National Natural Science Foundation of China (No.41601044, No.41571400), the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences, Wuhan (No.CUG150631, CUGL170401, CUGCJ1704), and Opening Foundation of Key Laboratory for National Geography State Monitoring, National Administration of Surveying, Mapping and Geoinformation.


  1. Barr AG, Black TA, Hogg EH, Kljun N, Morgenstern K, Nesic Z (2004) Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric For Meteorol 126:237–255CrossRefGoogle Scholar
  2. Bigras FJ, D'Aoust AL (1993) Influence of photoperiod on shoot and root frost tolerance and bud phe. Can J For Res 23:219–228CrossRefGoogle Scholar
  3. Bradley AV, Gerard FF, Barbier N, Weedon GP, Anderson LO, Huntingford C, Arai E (2011) Relationships between phenology, radiation and precipitation in the Amazon region. Glob Chang Biol 17:2245–2260CrossRefGoogle Scholar
  4. Bronson DR, Gower ST, Tanner M, Van Herk I (2009) Effect of ecosystem warming on boreal black spruce bud burst and shoot growth. Glob Chang Biol 15:1534–1543CrossRefGoogle Scholar
  5. Buitenwerf R, Rose L, Higgins SI (2015) Three decades of multi-dimensional change in global leaf phenology. Nat Clim Chang 5(4):364–368CrossRefGoogle Scholar
  6. Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004) A simple method for reconstructing a high–quality ndvi time–series data set based on the Savitzky–Golay filter. Remote Sens Environ 91:332–344CrossRefGoogle Scholar
  7. Chen X, Wang L, Inouye D (2017) Delayed response of spring phenology to global warming in subtropics and tropics. Agric For Meteorol 234:222–235CrossRefGoogle Scholar
  8. Chuine I, Cour P (1999) Climatic determinants of budburst seasonality in four temperate-zone tree species. New Phytol 143:339–349CrossRefGoogle Scholar
  9. Cleland EE, Allen JM, Crimmins TM, Dunne JA, Pau S, Travers SE, Wolkovich EM (2012) Phenological tracking enables positive species responses to climate change. Ecology 93(8):1765–1771CrossRefGoogle Scholar
  10. Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:357–365CrossRefGoogle Scholar
  11. Cong N, Wang T, Nan H, Ma Y, Wang X, Myneni RB, Piao SL (2013) Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob Chang Biol 19:881–891CrossRefGoogle Scholar
  12. Danby RK, HIK DS (2007) Responses of white spruce (Picea glauca) to experimental warming at a subarctic alpine tree-line. Glob Chang Biol 13:437–451CrossRefGoogle Scholar
  13. Dubovyk O, Landmann T, Erasmus BF, Tewes A, Schellberg J (2015) Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa. Int J Appl Earth Obs 38:175–183CrossRefGoogle Scholar
  14. Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S (2007) The shuttle radar topography mission. Rev Geophys 45.
  15. Fatichi S, Leuzinger S, Körner C (2014) Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. New Phytol 201:1086–1095CrossRefGoogle Scholar
  16. Fracheboud Y, Luquez V, Björkén L, Sjödin A, Tuominen H, Jansson S (2009) The control of autumn senescence in European aspen. Plant Physiol 149:1982–1991CrossRefGoogle Scholar
  17. Fu BJ, Liu GH, Chen LD, Ma KM, Li JR (2001) Scheme of ecological regionalization in China. Acta Ecol Sin 21:1–6Google Scholar
  18. Fu Y, He HS, Zhao J, Larsen DR, Zhang H, Sunde MG, Duan S (2018) Climate and spring phenology effects on autumn phenology in the greater Khingan Mountains. Northeastern China Remote Sens 10(3):449CrossRefGoogle Scholar
  19. Fu YS, Campioli M, Vitasse Y, De Boeck HJ, Van den Berge J, AbdElgawad H, Janssens IA (2014) Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. P Natl Acad Sci USA 111:7355–7360CrossRefGoogle Scholar
  20. Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia. Glob Planet Chang 100:172–182CrossRefGoogle Scholar
  21. Gonsamo A, Chen JM, Price DT, Kurz WA, Wu C (2012) Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J Geophys Res 117:1472–1472CrossRefGoogle Scholar
  22. Hamilton, J.A., El Kayal, W., Hart, A.T., Runcie, D.E., Arango-Velez, A., Cooke, J.E., 2016. The joint influence of photoperiod and temperature during growth cessation and development of dormancy in white spruce (Picea glauca). Tree Physiol 36, 1432–1448.Google Scholar
  23. Hänninen H, Tanino K (2011) Tree seasonality in a warming climate. Trends Plant Sci 16:412–416CrossRefGoogle Scholar
  24. Hartmann DL, Tank AMK, Rusticucci M, Alexander LV, Brönnimann S, Charabi YAR, Soden BJ (2013) Observations: atmosphere and surface. In: Climate change 2013 the physical science basis: working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  25. Hufkens K, Friedl MA, Keenan TF, Sonnentag O, Bailey A, O'keefe J, Richardson AD (2012) Ecological impacts of a widespread frost event following early spring leaf–out. Glob Chang Biol 18:2365–2377CrossRefGoogle Scholar
  26. Jepsen JU, Kapari L, Hagen SB, Schott T, Vindstad OPL, Nilssen AC, Ims RA (2011) Rapid northwards expansion of a forest insect pest attributed to spring phenology matching with sub-Arctic birch. Glob Chang Biol 17(6):2071–2083CrossRefGoogle Scholar
  27. Julien Y, Sobrino JA (2009) Global land surface phenology trends from GIMMS database. Int J Remote Sens 30:3495–3513CrossRefGoogle Scholar
  28. Karlsen SR, Solheim I, Beck PS, Høgda KA, Wielgolaski FE, Tømmervik H (2007) Variability of the start of the growing season in fennoscandia, 1982–2002. Int J Biometeorol 51:513–524CrossRefGoogle Scholar
  29. Keenan TF, Gray J, Friedl MA, Toomey M, Bohrer G, Hollinger DY, Yang B (2014) Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat Clim Chang 4(7):598–604CrossRefGoogle Scholar
  30. Keskitalo J, Bergquist G, Gardeström P, Jansson SA (2005) Cellular timetable of autumn senescence. Plant Physiol 139:1635–1648CrossRefGoogle Scholar
  31. Körner C, Basler D (2010) Phenology under global warming. Science 327:1461–1462CrossRefGoogle Scholar
  32. Kramer K, Leinonen I, Loustau D (2000) The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: an overview. Int J Biometeorol 44:67–75CrossRefGoogle Scholar
  33. Lam E (2004) Controlled cell death, plant survival and development. Nature 5:305Google Scholar
  34. Li P, Peng C, Wang M, Luo Y, Li M, Zhang K, Zhu Q (2018) Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan plateau in China. Sci Total Environ 637:855–864CrossRefGoogle Scholar
  35. Liu L, Monaco TA, Sun F, Liu W, Gan Y, Sun G (2017) Altered precipitation patterns and simulated nitrogen deposition effects on phenology of common plant species in a tibetan plateau alpine meadow. Agric For Meteorol 236:36–47CrossRefGoogle Scholar
  36. Liu Q, Fu YH, Zhu Z, Liu Y, Liu Z, Huang M (2016b) Delayed autumn phenology in the northern hemisphere is related to change in both climate and spring phenology. Glob Chang Biol 22:3702–3711CrossRefGoogle Scholar
  37. Liu Q, Fu YH, Zeng Z, Huang M, Li X, Piao SL (2016a) Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob Chang Biol 22:644–655CrossRefGoogle Scholar
  38. Luo X, Chen X, Wang L, Xu L, Tian Y (2014) Modeling and predicting spring land surface phenology of the deciduous broadleaf forest in northern China. Agric For Meteorol 198:33–41CrossRefGoogle Scholar
  39. Luo Z, Yu S (2017) Spatiotemporal variability of land surface phenology in China from 2001–2014. Remote Sens 9.
  40. Ma X, Huete A, Yu Q, Coupe NR, Davies K, Broich M, Boulain N (2013) Spatial patterns and temporal dynamics in savanna vegetation phenology across the north Australian tropical transect. Remote Sens of Environ 139:97–115CrossRefGoogle Scholar
  41. Menzel A, Fabian P (1999) Growing season extended in europe. Nature 397:659CrossRefGoogle Scholar
  42. Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R (2006) European phenological response to climate change matches the warming pattern. Glob Chang Biol 12:1969–1976CrossRefGoogle Scholar
  43. Mo F, Zhang J, Wang J, Cheng ZG, Sun GJ, Ren HX, Xiong YC (2017) Phenological evidence from China to address rapid shifts in global flowering times with recent climate change. Agric For Meteorol 246:22–30CrossRefGoogle Scholar
  44. Mo F, Zhao H, Wang JY, Qian SC, Zhou H, Wang SM, Xiong YC (2011) The key issues on plant phenology under global change. Acta Ecol Sin 31:2593–2601Google Scholar
  45. Peñuelas J, Rutishauser T, Filella I (2009) Phenology feedbacks on climate change. Science 324:887–888CrossRefGoogle Scholar
  46. Piao SL, Cui M, Chen A, Wang X, Ciais P, Liu J, Tang Y (2011) Altitude and temperature dependence of change in the spring vegetation green–up date from 1982 to 2006 in the Qinghai–xizang plateau. Agric For Meteorol 151:1599–1608CrossRefGoogle Scholar
  47. Piao SL, Mohammat A, Fang J, Cai Q (2006) Ndvi–based increase in growth of temperate grasslands and its responses to climate changes in China. Glob Environ Chang 16:340–348CrossRefGoogle Scholar
  48. Potter CS, Brooks V (1998) Global analysis of empirical relations between annual climate and seasonality of NDVI. Int J Remote Sens 19:2921–2948CrossRefGoogle Scholar
  49. Prevéy JS, Seastedt TR (2015) Seasonality of precipitation interacts with exotic species to alter composition and phenology of a semi-arid grassland. J Ecol 102:1549–1561CrossRefGoogle Scholar
  50. Pudas E, Leppälä M, Tolvanen A, Poikolainen J, Venäläinen A, Kubin E (2008) Trends in phenology of Betula pubescens across the boreal zone in Finland. Int J Biometeorol 52:251–259CrossRefGoogle Scholar
  51. Qiu B, Zhong M, Tang Z, Chen C (2013) Spatiotemporal variability of vegetation phenology with reference to altitude and climate in the subtropical mountain and hill region, China. Chin Sci Bull 58(23):2883–2892CrossRefGoogle Scholar
  52. Richardson AD, Anderson RS, Arain MA, Barr AG, Bohrer G, Chen G, Dietze MC (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the north a merican carbon program site synthesis. Glob Chang Biol 18(2):566–584CrossRefGoogle Scholar
  53. Shen M, Piao S, Cong N, Zhang G, Jassens IA (2015) Precipitation impacts on vegetation spring phenology on the Tibetan plateau. Glob Chang Biol 21:3647–3656CrossRefGoogle Scholar
  54. Shen M, Tang Y, Chen J, Zhu X, Zheng Y (2011) Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai–Tibetan plateau. Agric For Meteorol 151:1711–1722CrossRefGoogle Scholar
  55. Shen X, Liu B, Henderson M, Wang L, Wu Z, Wu H, Lu X (2018) Asymmetric effects of daytime and nighttime warming on spring phenology in the temperate grasslands of China. Agric For Meteorol 259:240–249CrossRefGoogle Scholar
  56. Shi C, Sun G, Zhang H, Xiao B, Ze B, Zhang N, Wu N (2014) Effects of warming on chlorophyll degradation and carbohydrate accumulation of alpine herbaceous species during plant senescence on the Tibetan plateau. PLoS One 9.
  57. Stinziano JR, Way DA (2017) Autumn photosynthetic decline and growth cessation in seedlings of white spruce are decoupled under warming and photoperiod manipulations. Plant Cell Environ doi 40:1296–1316. CrossRefGoogle Scholar
  58. Stöckli R, Vidale PL (2004) European plant phenology and climate as seen in a 20–year avhrr land–surface parameter dataset. Int J Remote Sens 25:3303–3330CrossRefGoogle Scholar
  59. Suepa T, Qi J, Lawawirojwong S, Messina JP (2016) Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environ Res 147:621–629CrossRefGoogle Scholar
  60. Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, El Saleous N (2005) An extended AVHRR 8km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26:4485–4498CrossRefGoogle Scholar
  61. Tylewicz, S., Petterle, A., Marttila, S., Miskolczi, P., Azeez, A., Singh, R.K., Bowman, J.L., 2018. Photoperiodic control of seasonal growth is mediated by ABA acting on cell-cell communication. Science, eaan8576.Google Scholar
  62. Wang H, Dai J, Zheng J, Ge Q (2015) Temperature sensitivity of plant phenology in temperate and subtropical regions of China from 1850 to 2009. Int J Climatol 35(6):913–922CrossRefGoogle Scholar
  63. Wang S, Wang X, Chen G, Yang Q, Wang B, Ma Y, Shen M (2017) Complex responses of spring alpine vegetation phenology to snow cover dynamics over the Tibetan plateau. China Sci Total Environ 593:449–461CrossRefGoogle Scholar
  64. Wareing PF (2003) Photoperiodism in woody plants. Annu Rev Plant Physiol 7:191–214CrossRefGoogle Scholar
  65. White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O’Keefe J, Zhang G, Nemani RR (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Chang Biol 15:2335–2359CrossRefGoogle Scholar
  66. Wu C, Chen JM, Gonsamo A, Price DT, Black TA, Kurz WA (2012) Interannual variability of net carbon exchange is related to the lag between the end-dates of net carbon uptake and photosynthesis: evidence from long records at two contrasting forest stands. Agric For Meteorol 164:29–38CrossRefGoogle Scholar
  67. Wu C, Hou X, Peng D, Gonsamo A, Xu S (2016) Land surface phenology of China's temperate ecosystems over 1999–2013: spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity. Agric For Meteorol 216:177–187CrossRefGoogle Scholar
  68. Wu WB, Peng Y, Tang HJ, Zhou QB, Chen ZX, Shibasaki R (2010) Characterizing spatial patterns of phenology in cropland of China based on remotely sensed data. J Integr Agric 9:101–112Google Scholar
  69. Yan D, Zhang X, Yu Y, Guo W (2017) Characterizing land cover impacts on the responses of land surface phenology to the rainy season in the Congo Basin. Remote Sens 9.
  70. Yang Y, Guan H, Shen M, Liang W, Jiang L (2015) Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010. Glob Chang Biol 21(2):652–665CrossRefGoogle Scholar
  71. You X, Meng J, Zhang M, Dong T (2013) Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method. Remote Sens 5(7):3190–3211CrossRefGoogle Scholar
  72. Yu H, Luedeling E, Xu J (2010) Winter and spring warming result in delayed spring phenology on the Tibetan plateau. P Natl Acad Sci USA 107(51):22151–22156CrossRefGoogle Scholar
  73. Yu L, Liu T, Bu K, Yan F, Yang J, Chang L, Zhang S (2017) Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015. Sci Rep-UK 7(1):14770CrossRefGoogle Scholar
  74. Yun J, Jeong SJ, Ho CH, Park CE, Park H, Kim J (2018) Influence of winter precipitation on spring phenology in boreal forests. Glob. Chang. In: BiolGoogle Scholar
  75. Zhang X, Dan T, Sullivan JT (2007) Diverse responses of vegetation phenology to a warming climate. Geophys Res Lett 34:255–268Google Scholar
  76. Zhang X, Friedl MA, Schaaf CB, Strahler AH (2004) Climate controls on vegetation phenological patterns in northern mid– and high latitudes inferred from modis data. Glob Chang Biol 10:1133–1145CrossRefGoogle Scholar
  77. Zhou J, Cai W, Qin Y, Lai L, Guan T, Zhang X, Zheng Y (2016) Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China. Sci Total Environ 572:119–128CrossRefGoogle Scholar
  78. Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res-Atmos 106:20069–20083CrossRefGoogle Scholar
  79. Zu J, Zhang Y, Huang K, Liu Y, Chen N, Cong N (2018) Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan plateau. Int J Appl Earth Obs 69:198–205CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Resource and Environmental ScienceWuhan UniversityWuhanChina
  2. 2.Laboratory of Critical Zone Evolution, School of Earth SciencesChina University of GeosciencesWuhanChina
  3. 3.Institute of Geographic Sciences and Natural Resources Research, CASBeijingChina

Personalised recommendations