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Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model

Abstract

Acquiring spatiotemporal patterns of phenological information and its drivers is essential for understanding the response of crops to climate change and implementing adaptation measures. However, current approaches to obtain phenology and analyse its drivers have deficiencies such as sparse observations, excessive dependence of remote sensing inversion on sensors, and inevitable difficulties in upscaling site-based crop models into larger regions. Based on the Wang-Engel temperature response function, we improved the Crop Estimation through Resource and Environment Synthesis-Wheat (CERES-Wheat) model. First, we calibrated the model at the regional scale and evaluated its performance. Furthermore, the spatiotemporal changes in winter wheat phenology in China from 2000 to 2015 were analysed. The results showed that the improved model significantly enhanced the simulation accuracy of the anthesis and maturity dates by averages of 13% and 12% in most planting areas, especially in the Yunnan-Guizhou Plateau (YG) with improvements of 26% and 28%. The simulated phenology of winter wheat grown in a colder environment (e.g., the average temperatures during the vegetative growth period range from 0 to 5°C and from 15 to 20°C, and the reproductive growth period ranges from 10 to 15°C) also notably improved. These results confirmed that the original temperature response function indeed had limitations. Further analyses revealed that the key phenological dates and growth periods over the past 16 years were dominantly advanced and shortened. Specifically, the anthesis date, vegetative growth period (VGP), and reproductive growth period (RGP) indicated obviously spatial characteristics. For example, the anthesis date and VGP in the North China Plain (NCP) and the Middle-Lower Yangtze Plain (YZ) and the RGP in northwestern China (NW) showed opposite trends of delay and prolongation as comparing with the dominant patterns. Sensitivity analysis indicated that the key phenological dates and growth periods were advanced and shortened as the minimum (Tmin) and maximum temperatures (Tmax) rose, while they were postponed and prolonged with the increased precipitation. However, their responses to solar radiation did not show spatial consistency. Additionally, we found that the sensitivity of phenology to climatic factors differed across subregions. In particular, phenology in southwestern China and YG was more sensitive to Tmin, Tmax, and solar radiation than in the NCP and NW. Moreover, the sensitivity to precipitation in NW was higher than that in YZ. Totally, the improved crop model could provide more refined spatial characteristics of phenology at a large scale and benefit to explore its drivers more objectively. Furthermore, our results highlight that different planting areas should adopt suitable adaptation measures to cope with climate change impacts. Ultimately, the improved model is promising to enhance the accuracy of yield prediction and provide powerful tools for assessing regional climate change impact and adaptability.

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References

  1. Angstrom A. 1924. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Q J R Meteorol Soc, 50: 121–126

    Article  Google Scholar 

  2. Asseng S, Ewert F, Rosenzweig C, Jones J W, Hatfield J L, Ruane A C, Boote K J, Thorburn P J, Rötter R P, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal P K, Angulo C, Bertuzzi P, Biernath C, Challinor A J, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt L A, Ingwersen J, Izaurralde R C, Kersebaum K C, Müller C, Naresh Kumar S, Nendel C, O'Leary G, Olesen J E, Osborne T M, Palosuo T, Priesack E, Ripoche D, Semenov M A, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White J W, Williams J R, Wolf J. 2013. Uncertainty in simulating wheat yields under climate change. Nat Clim Change, 3: 827–832

    Article  Google Scholar 

  3. Atzberger C, Klisch A, Mattiuzzi M, Vuolo F. 2014. Phenological metrics derived over the European continent from NDVI3g data and MODIS time series. Remote Sens, 6: 257–284

    Article  Google Scholar 

  4. Bassu S, Brisson N, Durand J L, Boote K, Lizaso J, Jones J W, Rosenzweig C, Ruane A C, Adam M, Baron C, Basso B, Biernath C, Boogaard H, Conijn S, Corbeels M, Deryng D, De Sanctis G, Gayler S, Grassini P, Hatfield J, Hoek S, Izaurralde C, Jongschaap R, Kemanian A R, Kersebaum K C, Kim S H, Kumar N S, Makowski D, Müller C, Nendel C, Priesack E, Pravia M V, Sau F, Shcherbak I, Tao F, Teixeira E, Timlin D, Waha K. 2014. How do various maize crop models vary in their responses to climate change factors? Glob Change Biol, 20: 2301–2320

    Article  Google Scholar 

  5. Dai Y J, Shangguan W, Duan Q Y, Liu B Y, Fu S H, Niu G. 2013. Development of a China dataset of soil hydraulic parameters using pedotransfer functions for land surface modeling. J Hydrometeorol, 14: 869–887

    Article  Google Scholar 

  6. Deryng D, Sacks W J, Barford C C, Ramankutty N. 2011. Simulating the effects of climate and agricultural management practices on global crop yield. Glob Biogeochem Cycle, 25: GB2006

    Article  Google Scholar 

  7. Dettori M, Cesaraccio C, Duce P. 2017. Simulation of climate change impacts on production and phenology of durum wheat in Mediterranean environments using CERES-Wheat model. Field Crops Res, 206: 43–53

    Article  Google Scholar 

  8. Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote K J, Buchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde R C, Mueller N D, Ray D K, Rosenzweig C, Ruane A C, Sheffield J. 2015. The global gridded crop model intercomparison: Data and modeling protocols for Phase 1 (v1.0). Geosci Model Dev, 8: 261–277

    Article  Google Scholar 

  9. FAO. 2018. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy

    Google Scholar 

  10. Fang X Q, Chen F H. 2015. Plant phenology and climate change. Sci China Earth Sci, 58: 1043–1044

    Article  Google Scholar 

  11. Fick S E, Hijmans R J. 2017. WorldClim 2: New 1 km spatial resolution climate surfaces for global land areas. Int J Climatol, 37: 4302–4315

    Article  Google Scholar 

  12. Franke J A, Müller C, Elliott J, Ruane A C, Jagermeyr J, Snyder A, Dury M, Falloon P D, Folberth C, François L, Hank T, Izaurralde R C, Jacquemin I, Jones C, Li M, Liu W, Olin S, Phillips M, Pugh T A M, Reddy A, Williams K, Wang Z, Zabel F, Moyer E J. 2020. The GGCMI Phase 2 emulators: Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). Geosci Model Dev, 13: 3995–4018

    Article  Google Scholar 

  13. Fu Y, Li X, Zhou X, Geng X, Guo Y, Zhang Y. 2020. Progress in plant phenology modeling under global climate change. Sci China Earth Sci, 63: 1237–1247

    Article  Google Scholar 

  14. Harris I, Osborn T J, Jones P, Lister D. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data, 7: 109

    Article  Google Scholar 

  15. He Z B, Du J, Zhao W Z, Yang J J, Chen L F, Zhu X, Chang X X, Liu H. 2015. Assessing temperature sensitivity of subalpine shrub phenology in semi-arid mountain regions of China. Agric For Meteorol, 213: 42–52

    Article  Google Scholar 

  16. Hoogenboom G, White J W, Messina C D. 2004. From genome to crop: Integration through simulation modeling. Field Crops Res, 90: 145–163

    Article  Google Scholar 

  17. Hutchinson M F. 2004. ANUSPLIN version 4.3. Canberra: Centre for Resource and Environmental Studies, Australian National University

    Google Scholar 

  18. Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T. 2003. The DSSAT cropping system model. Eur J Agron, 18: 235–265

    Article  Google Scholar 

  19. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Jenne R, Joseph D. 1996. The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteorol Soc, 77: 437–471

    Article  Google Scholar 

  20. Li T, Hasegawa T, Yin X, Zhu Y, Boote K, Adam M, Bregaglio S, Buis S, Confalonieri R, Fumoto T, Gaydon D, Marcaida III M, Nakagawa H, Oriol P, Ruane A C, Ruget F, Singh B, Singh U, Tang L, Tao F, Wilkens P, Yoshida H, Zhang Z, Bouman B. 2015. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob Change Biol, 21: 1328–1341

    Article  Google Scholar 

  21. Liu L L, Wallach D, Li J, Liu B, Zhang L X, Tang L, Zhang Y, Qiu X L, Cao W X, Zhu Y. 2018. Uncertainty in wheat phenology simulation induced by cultivar parameterization under climate warming. Eur J Agron, 94: 46–53

    Article  Google Scholar 

  22. Liu Y, Chen Q, Ge Q, Dai J. 2018. Spatiotemporal differentiation of changes in wheat phenology in China under climate change from 1981 to 2010. Sci China Earth Sci, 61: 1088–1097

    Article  Google Scholar 

  23. Liu Z J, Wu C Y, Liu Y S, Wang X Y, Fang B, Yuan W P, Ge Q S. 2017. Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain. ISPRS J Photogrammetry Remote Sens, 130: 81–91

    Article  Google Scholar 

  24. Luo W, Taylor M C, Parker S R. 2008. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int J Climatol, 28: 947–959

    Article  Google Scholar 

  25. Luo Y C, Zhang Z, Li Z Y, Chen Y, Zhang L L, Cao J, Tao F L. 2020a. Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources. Environ Res Lett, 15: 074003

    Article  Google Scholar 

  26. Luo Y C, Zhang Z, Chen Y, Li Z Y, Tao F L. 2020b. ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth Syst Sci Data, 12: 197–214

    Article  Google Scholar 

  27. Mavromatis T, Boote K J, Jones J W, Irmak A, Shinde D, Hoogenboom G. 2001. Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Sci, 41: 40–51

    Article  Google Scholar 

  28. Parent B, Tardieu F. 2012. Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytol, 194: 760–774

    Article  Google Scholar 

  29. Parent B, Turc O, Gibon Y, Stitt M, Tardieu F. 2010. Modelling temperature- compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J Exp Bot, 61: 2057–2069

    Article  Google Scholar 

  30. Piao S L, Liu Q, Chen A P, Janssens I A, Fu Y S, Dai J H, Liu L L, Lian X, Shen M G, Zhu X L. 2019. Plant phenology and global climate change: Current progresses and challenges. Glob Change Biol, 25: 1922–1940

    Article  Google Scholar 

  31. Porter J R, Xie L, Challinor A, Cochrane K, Howden S M, Iqbal M M, Lobell D B, Travasso M I. 2014. Food security and food production systems. In: Field C B, Barros V R, Dokken D J, Mach K J, Mastrandrea M D, Bilir T E, Chatterjee M, Ebi K L, Estrada Y O, Genova R C, Girma B, Kissel E S, Levy A N, MacCracken S, Mastrandrea P R, White L L, eds. Climate Change 2014: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. 485–533

    Google Scholar 

  32. Ritchie J, Otter S. 1985. Description and performance of CERES-Wheat: A user-oriented wheat yield model. In: Willis W O, ed. ARS Wheat Yield Project. Washington D C: United States Department of Agriculture, Agricultural Research Service. 159–175

    Google Scholar 

  33. Rosenzweig C, Elliott J, Deryng D, Ruane A C, Müller C, Arneth A, Boote K J, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh T A M, Schmid E, Stehfest E, Yang H, Jones J W. 2014. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci USA, 111: 3268–3273

    Article  Google Scholar 

  34. Rosenzweig C, Jones J W, Hatfield J L, Ruane A C, Boote K J, Thorburn P, Antle J M, Nelson G C, Porter C, Janssen S, Asseng S, Basso B, Ewert F, Wallach D, Baigorria G, Winter J M. 2013. The agricultural model intercomparison and improvement project (AgMIP): Protocols and pilot studies. Agric For Meteorol, 170: 166–182

    Article  Google Scholar 

  35. Rosenzweig C, Parry M L. 1994. Potential impact of climate change on world food supply. Nature, 367: 133–138

    Article  Google Scholar 

  36. Rosenzweig C, Ruane A C, Antle J, Elliott J, Ashfaq M, Chatta A A, Ewert F, Folberth C, Hathie I, Havlik P, Hoogenboom G, Lotze-Campen H, MacCarthy D S, Mason-D’Croz D, Contreras E M, Müller C, Perez- Dominguez I, Phillips M, Porter C, Raymundo R M, Sands R D, Schleussner C F, Valdivia R O, Valin H, Wiebe K. 2018. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments. Phil Trans R Soc A, 376: 20160455

    Article  Google Scholar 

  37. Rötter R P, Carter T R, Olesen J E, Porter J R. 2011. Crop-climate models need an overhaul. Nat Clim Change, 1: 175–177

    Article  Google Scholar 

  38. Ruane A C, Goldberg R, Chryssanthacopoulos J. 2015. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agric For Meteorol, 200: 233–248

    Article  Google Scholar 

  39. Sakamoto T. 2018. Refined shape model fitting methods for detecting various types of phenological information on major U.S. crops. ISPRS J Photogrammetry Remote Sens, 138: 176–192

    Article  Google Scholar 

  40. Shangguan W, Dai Y, Duan Q, Liu B, Yuan H. 2014. A global soil data set for earth system modeling. J Adv Model Earth Syst, 6: 249–263

    Article  Google Scholar 

  41. Streck N A, Lago I, Gabriel L F, Samboranha F K. 2008. Simulating maize phenology as a function of air temperature with a linear and a nonlinear model. Pesq Agropec Bras, 43: 449–455

    Article  Google Scholar 

  42. Streck N A, Weiss A, Baenziger P S. 2003a. A generalized vernalization response function for winter wheat. Agron J, 95: 155–159

    Article  Google Scholar 

  43. Streck N A, Weiss A, Xue Q, Baenziger P S. 2003b. Improving predictions of developmental stages in winter wheat: A modified Wang and Engel model. Agric For Meteorol, 115: 139–150

    Article  Google Scholar 

  44. Tao F, Yokozawa M, Zhang Z. 2009a. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis. Agric For Meteorol, 149: 831–850

    Article  Google Scholar 

  45. Tao F, Zhang S, Zhang Z. 2012. Spatiotemporal changes of wheat phenology in China under the effects of temperature, day length and cultivar thermal characteristics. Eur J Agron, 43: 201–212

    Article  Google Scholar 

  46. Tao F L, Zhang Z, Liu J Y, Yokozawa M. 2009b. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection. Agric For Meteorol, 149: 1266–1278

    Article  Google Scholar 

  47. Tao F L, Zhang Z, Xiao D P, Zhang S, Rotter R P, Shi W J, Liu Y J, Wang M, Liu F S, Zhang H. 2014. Responses of wheat growth and yield to climate change in different climate zones of China, 1981-2009. Agric For Meteorol, 189-190: 91–104

    Article  Google Scholar 

  48. Verger A, Filella I, Baret F, Peñuelas J. 2016. Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sens Environ, 178: 1–14

    Article  Google Scholar 

  49. Wang C L, Yue T X, Fan Z M. 2014. Solar radiation climatology calculation in China. J Resources Ecol, 5: 132–138

    Article  Google Scholar 

  50. Wang C Z, Zhang Z, Chen Y, Tao F L, Zhang J, Zhang W. 2018. Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products-A case study in the Hunan province of China. Int J Remote Sens, 39: 6405–6428

    Article  Google Scholar 

  51. Wang E L, Martre P, Zhao Z G, Ewert F, Maiorano A, Rotter R P, Kimball B A, Ottman M J, Wall G W, White J W, Reynolds M P, Alderman P D, Aggarwal P K, Anothai J, Basso B, Biernath C, Cammarano D, Challinor A J, De S G, Doltra J, Dumont B, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt L A, Izaurralde R C, Jabloun M, Jones C D, Kersebaum K C, Koehler A K, Liu L L, Müller C, Kumar S N, Nendel C, O’Leary G, Olesen J E, Palosuo T, Priesack E, Rezaei E E, Ripoche D, Ruane A C, Semenov M A, Shcherbak I, Stockle C, Stratonovitch P, Streck T, Supit I, Tao F L, Thorburn P, Waha K, Wallach D, Wang Z M, Wolf J, Zhu Y, Asseng S. 2017. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat Plants, 3: 17102

    Article  Google Scholar 

  52. Wang J, Wang E L, Feng L P, Yin H, Yu W D. 2013. Phenological trends of winter wheat in response to varietal and temperature changes in the North China Plain. Field Crops Res, 144: 135–144

    Article  Google Scholar 

  53. Wang N, Wang E L, Wang J, Zhang J P, Zheng B Y, Huang Y, Tan M X. 2018. Modelling maize phenology, biomass growth and yield under contrasting temperature conditions. Agric For Meteorol, 250-251: 319–329

    Article  Google Scholar 

  54. Wang N, Wang J, Wang E L, Yu Q, Shi Y, He D. 2015. Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming. Eur J Agron, 71: 19–33

    Article  Google Scholar 

  55. Wang X J, Pan X B, Chen C, Long B J. 2012. Forecasting cotton chilling damage based on COSIM (in Chinese). Cotton Sci, 24: 52–61

    Google Scholar 

  56. Wu X, Cheng C, Qiao C, Song C. 2020. Spatio-temporal differentiation of spring phenology in China driven by temperatures and photoperiod from 1979 to 2018. Sci China Earth Sci, 63: 1485–1498

    Article  Google Scholar 

  57. Xiao D P, Tao F L. 2012. Impact of climate change in 1981–2009 on winter wheat phenology in the North China Plain (in Chinese). Chin J Eco-Agr, 20: 1539–1545

    Article  Google Scholar 

  58. Xiao D P, Tao F L, Liu Y J, Shi W J, Wang M, Liu F S, Zhang S, Zhu Z. 2013. Observed changes in winter wheat phenology in the North China Plain for 1981-2009. Int J Biometeorol, 57: 275–285

    Article  Google Scholar 

  59. Xiao D P, Tao F L, Shen Y J, Qi Y Q. 2016. Combined impact of climate change, cultivar shift, and sowing date on spring wheat phenology in Northern China. J Meteorol Res, 30: 820–831

    Article  Google Scholar 

  60. Xie Y, Wang P X, Wang L, Zhang S Y, Li L, Liu J M. 2016. Estimation of wheat yield based on crop and remote sensing assimilation models (in Chinese). Trans Chin Soc Agric Eng, 32: 179–186

    Google Scholar 

  61. Xiong W, Conway D, Holman I, Lin E. 2008a. Evaluation of CERES-Wheat simulation of wheat production in China. Agron J, 100: 1720–1728

    Article  Google Scholar 

  62. Xiong W, Holman I, Conway D, Lin E, Li Y. 2008b. A crop model cross calibration for use in regional climate impacts studies. Ecol Model, 213: 365–380

    Article  Google Scholar 

  63. Yu Y, Huang Y, Zhang W. 2012. Changes in rice yields in China since 1980 associated with cultivar improvement, climate and crop management. Field Crops Res, 136: 65–75

    Article  Google Scholar 

  64. Yuan W P, Xu B, Chen Z Q, Xia J Z, Xu W F, Chen Y, Wu X X, Fu Y. 2015. Validation of China-wide interpolated daily climate variables from 1960 to 2011. Theor Appl Climatol, 119: 689–700

    Article  Google Scholar 

  65. Yue T X. 2011. Surface Modeling: High Accuracy and High Speed Methods. New York: CRC Press

    Book  Google Scholar 

  66. Yue T X, Liu Y, Zhao M W, Du Z P, Zhao N. 2016. A fundamental theorem of Earth's surface modelling. Environ Earth Sci, 75: 751

    Article  Google Scholar 

  67. Yue T, Zhao N, Liu Y, Wang Y, Zhang B, Du Z, Fan Z, Shi W, Chen C, Zhao M, Song D, Wang S, Song Y, Yan C, Li Q, Sun X, Zhang L, Tian Y, Wang W, Wang Y', Ma S, Huang H, Lu Y, Wang Q, Wang C, Wang Y, Lu M, Zhou W, Liu Y, Yin X, Wang Z, Bao Z, Zhao M, Zhao Y, Jiao Y, Naseer U, Fan B, Li S, Yang Y, Wilson J P. 2020. A fundamental theorem for eco-environmental surface modelling and its applications. Sci China Earth Sci, 63: 1092–1112

    Article  Google Scholar 

  68. Yue T X, Zhao N, Yang H, Song Y J, Du Z P, Fan Z M, Song D J. 2013. A multi-grid method of high accuracy surface modeling and its validation. Trans GIS, 17: 943–952

    Article  Google Scholar 

  69. Zhang S, Tao F. 2019. Improving rice development and phenology prediction across contrasting climate zones of China. Agric For Meteorol, 268: 224–233

    Article  Google Scholar 

  70. Zhang X, Chen J, Jiang Y, Deng A X, Song Z W, Zheng C Y, Zhang W J. 2014. Impacts of nighttime warming on rice growth stage and grain yield of leading varieties released in different periods in Jiangsu Province, China (in Chinese). Chin J Appl Ecol, 25: 1349–1356

    Google Scholar 

  71. Zhao G C. 2010. Study on Chinese wheat planting regionalization (I) (in Chinese). J Triticeae Crops, 30: 886–895

    Google Scholar 

  72. Zhao H, Dai T, Jing Q, Jiang D, Cao W. 2007. Leaf senescence and grain filling affected by post-anthesis high temperatures in two different wheat cultivars. Plant Growth Regul, 51: 149–158

    Article  Google Scholar 

  73. Zhao N, Yue T X, Wang C L. 2013. Surface modeling of seasonal mean precipitation in China during 1951-2010 (in Chinese). Prog Geogr, 32: 49–58

    Google Scholar 

  74. Zhao Y X, Xiao D P, Bai H Z, Tao F L. 2019. Research progress on the response and adaptation of crop phenology to climate change in China (in Chinese). Prog Geogr, 38: 224–235

    Article  Google Scholar 

  75. Zheng J, Xu X, Jia G, Wu W. 2020. Understanding the spring phenology of Arctic tundra using multiple satellite data products and ground observations. Sci China Earth Sci, 63: 1599–1612

    Article  Google Scholar 

Download references

Acknowledgements

We thank the two anonymous reviewers for constructive suggestions on the manuscript. This work was supported by the National Natural Science Foundation of China (Grant Nos. 41977405, 42061144003).

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Luo, Y., Zhang, Z., Zhang, L. et al. Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model. Sci. China Earth Sci. (2021). https://doi.org/10.1007/s11430-020-9821-0

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Keywords

  • Winter wheat
  • Phenology
  • Temperature response function
  • Spatiotemporal patterns
  • pDSSAT