Trends in vegetation productivity related to climate change in China’s Pearl River Delta

14. Hansen J, Ruedy R, Sato M, Lo K. Global surface temperature change. Rev Geophys. 2010; 48: RG4004. https://doi.org/10.1029/2010RG000345 15. Estrada F, Botzen WJW, Tol RSJ. A global economic assessment of city policies to reduce climate change impacts. Nat Clim Chang. 2017; 7: 403–406. https://doi.org/10.1038/nclimate3301 16. Qin D, Ding Y, Mu M. Climate and Environmental Change in China: 1951–2012. Qin D, Ding Y, Mu M, editors. Berlin, Heidelberg: Springer Berlin Heidelberg; 2016. https://doi.org/10.1007/978-3-662- 48482-1 17. Xue L, Gu R, Wang T, Wang X, Saunders S, Blake D, et al. Oxidative capacity and radical chemistry in the polluted atmosphere of Hong Kong and Pearl River Delta region: analysis of a severe photochemical smog episode. Atmos Chem Phys. 2016; 16: 9891–9903. https://doi.org/10.5194/acp-16-9891-2016 18. Nichol JE, Choi SY, Wong MS, Abbas S. Temperature change and urbanisation in a multi-nucleated megacity: China’s Pearl River Delta. Urban Clim. 2020; 31: 100592. https://doi.org/10.1016/j.uclim. 2020.100592 PLOS ONE Climate and vegetation trends in the Pearl River Delta PLOS ONE | https://doi.org/10.1371/journal.pone.0245467 February 24, 2021 17 / 20

19. World Bank Group. East Asia’s changing urban landscape: measuring a decade of spatial growth. Urban Development Series, The World Bank, Washington. 2015. 20. Guardian. China’s Pearl River Delta overtakes Tokyo as world’s largest megacity. Guardian Newspa- per, 28.01.2015. 2015. 21. Friedl M, Sulla-Menashe D. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS L Process DAAC. https://doi.org/10.5067/MODIS/MCD12Q1. 006 22. Zhu Z, Bi J, Pan Y, Ganguly S, Anav A, Xu L, et al. Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2. Remote Sens. 2013; 5: 927–948. https://doi.org/10.3390/rs5020927 23. Pinzon J, Tucker C. A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series. Remote Sens. 2014; 6: 6929–6960. https://doi.org/10.3390/rs6086929 24. TPDC. Global GIMMS NDVI3g v1 dataset (1981–2015). Natl Tibet Plateau Data Cent. 2018. Available: http://dx.doi.org. 25. Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci Data. 2015; 2: 1– 21. https://doi.org/10.1038/sdata.2015.66 PMID: 26646728 26. C3S. Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmo- spheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store

(CDS). 2017 [cited 15 Sep 2020]. Available: https://cds.climate.copernicus.eu/cdsapp#!/home. 27. Wu J, Albert LP, Lopes AP, Restrepo-Coupe N, Hayek M, Wiedemann KT, et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science (80-). 2016; 351: 972–976. https://doi.org/10.1126/science.aad5068 PMID: 26917771 28. White MA, Thornton PE, Running SW. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem Cycles. 1997; 11: 217–234. https://doi. org/10.1029/97GB00330 29. Tong X, Tian F, Brandt M, Liu Y, Zhang W, Fensholt R. Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012. Remote Sens Environ. 2019; 232: 111307. https://doi.org/10.1016/j.rse.2019.111307 30. Islam T, Rico-Ramirez MA, Han D, Srivastava PK, Ishak AM. Performance evaluation of the TRMM pre- cipitation estimation using ground-based radars from the GPM validation network. J Atmos Solar-Ter- restrial Phys. 2012; 77: 194–208. https://doi.org/10.1016/j.jastp.2012.01.001 31. Yu L, Liu T, Bu K, Yan F, Yang J, Chang L, et al. Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015. Sci Rep. 2017; 7: 1–9. https://doi.org/10.1038/s41598-016- 0028-x PMID: 28127051 32. Forkel M, Carvalhais N, Verbesselt J, Mahecha MD, Neigh CSR, Reichstein M. Trend Change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sens. 2013; 5: 2113–

2144. https://doi.org/10.3390/rs5052113 33. Wu C, Wang X, Wang H, Ciais P, Peñuelas J, Myneni RB, et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nat Clim Chang. 2018. https://doi.org/10.1038/ s41558-018-0346-z 34. Kogan FN. Global Drought Watch from Space. 1994; 621–636. 35. Du L, Tian Q, Yu T, Meng Q, Jancso T, Udvardy P, et al. A comprehensive drought monitoring method integrating MODIS and TRMM data. Int J Appl Earth Obs Geoinf. 2013; 23: 245–253. https://doi.org/10. 1016/j.jag.2012.09.010 36. Shahabfar A, Ghulam A, Conrad C. Understanding Hydrological Repartitioning and Shifts in Drought Regimes in Central and South-West Asia Using MODIS Derived Perpendicular Drought Index and TRMM Data. 2013; 1–11. 37. Tian F, Wigneron J-P, Ciais P, Chave J, Oge´ e J, Peñuelas J, et al. Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite. Nat Ecol Evol. 2018; 2: 1428–1435. https://doi. org/10.1038/s41559-018-0630-3 PMID: 30104750 38. Nguyen P, Thorstensen A, Sorooshian S, Hsu K, AghaKouchak A, Ashouri H, et al. Global precipitation trends across spatial scales using satellite observations. Bull Am Meteorol Soc. 2018; 99: 689–697. https://doi.org/10.1175/BAMS-D-17-0065.1 39. Du S, Shi P, Rompaey A. The Relationship between Urban Sprawl and Farmland Displacement in the Pearl River Delta, China. Land. 2013; 3: 34–51. https://doi.org/10.3390/land3010034 PLOS ONE Climate and vegetation trends in the Pearl River Delta PLOS ONE | https://doi.org/10.1371/journal.pone.0245467 February 24, 2021 18 / 20

40. Liu Z, Huang H, Werners SEW, Werners DY. Construction area expansion in relation to economic- demographic development and land resource in the Pearl River Delta of China. J Geogr Sci. 2016; 26: 188–202. 41. Nichol J, Hang TP, Ng E. Temperature projection in a tropical city using remote sensing and dynamic modeling. Clim Dyn. 2013; 4. https://doi.org/10.1007/s00382-013-1748-2 42. Xue-Mei Z, Pu-Cai W, Xiang-Ao X. Variability and Long-Term Trend of Total Cloud Cover in China Derived from ISCCP, ERA-40, CRU3, and Ground Station Datasets. Atmos Ocean Sci Lett. 2013; 6: 133–137. https://doi.org/10.3878/j.issn.1674-2834.12.0100 43. Zongxing L, Qi F, Wei Z, Yuanqing H, Xufeng W, Catto N, et al. Decreasing trend of sunshine hours and related driving forces in Southwestern China. Theor Appl Climatol. 2012; 109: 305–321. https://doi.org/ 10.1007/s00704-012-0583-4 44. Wang Y, Wild M, Sanchez-Lorenzo A, Manara V. Urbanization effect on trends in sunshine duration in China. Ann Geophys. 2017; 35: 839–851. https://doi.org/10.5194/angeo-35-839-2017 45. Tao F, Zhang Z. Climate Change, High-Temperature Stress, Rice Productivity, and Water Use in East- ern China: A New Superensemble-Based Probabilistic Projection. J Appl Meteorol Climatol. 2013; 52: 531–551. https://doi.org/10.1175/JAMC-D-12-0100.1 46. de Jong R, Schaepman ME, Furrer R, de Bruin S, Verburg PH. Spatial relationship between climatolo- gies and changes in global vegetation activity. Glob Chang Biol. 2013; 19: 1953–1964. https://doi.org/ 10.1111/gcb.12193 PMID: 23512439

Nature. 2016; 529: 84–87. https://doi.org/10.1038/nature16467 PMID: 26738594 54. Huang J, Wang H, Dai Q, Han D. Analysis of NDVI data for crop identification and yield estimation. IEEE J Sel Top Appl Earth Obs Remote Sens. 2014; 7: 4374–4384. https://doi.org/10.1109/JSTARS. 2014.2334332 55. Huete a, Didan K, Miura T, Rodriguez E, Gao X, Ferreira L. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 2002; 83: 195–213. https://doi. org/10.1016/S0034-4257(02)00096-2 56. Gitelson AA. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Charac- teristics of Vegetation. J Plant Physiol. 2004; 161: 165–173. https://doi.org/10.1078/0176-1617-01176 PMID: 15022830 57. Guo W, Liu H, Wu X. Vegetation Greening Despite Weakening Coupling Between Vegetation Growth and Temperature Over the Boreal Region. J Geophys Res Biogeosciences. 2018; 123: 2376–2387. https://doi.org/10.1029/2018JG004486 58. Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature. 1997; 386: 698–702. https://doi.org/10.1038/386698a0 59. Park T, Ganguly S, T�mmervik H, Euskirchen ES, H�gda KA, Karlsen SR, et al. Changes in grow- ing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ Res Lett. 2016;11. https://doi.org/10.1088/1748-9326/11/8/084001 60. Abbas S, Nichol JE, Qamer FM, Xu J. Characterization of Drought Development through Remote Sens-

ing: A Case Study in Central Yunnan, China. 2014; 4998–5018. https://doi.org/10.3390/rs6064998 61. Nichol JE, Abbas S. Integration of remote sensing datasets for local scale assessment and prediction of drought. Sci Total Environ. 2015; 505: 503–507. https://doi.org/10.1016/j.scitotenv.2014.09.099 PMID: 25461052 PLOS ONE Climate and vegetation trends in the Pearl River Delta PLOS ONE | https://doi.org/10.1371/journal.pone.0245467 February 24, 2021 19 / 20

62. Be´ gue´ A, Vintrou E, Ruelland D, Claden M, Dessay N. Can a 25-year trend in Soudano-Sahelian vege- tation dynamics be interpreted in terms of land use change? A remote sensing approach. Glob Environ Chang. 2011; 21: 413–420. https://doi.org/10.1016/j.gloenvcha.2011.02.002 63. Liu Y, Li Y, Li S, Motesharrei S. Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors. Remote Sens. 2015; 7: 13233–13250. https://doi.org/10.3390/ rs71013233 64. Park H-S, Sohn BJ. Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations. J Geophys Res. 2010; 115: D14101. https://doi.org/10.1029/2009JD012752 65. Piao S, Nan H, Huntingford C, Ciais P, Friedlingstein P, Sitch S, et al. Evidence for a weakening rela- tionship between interannual temperature variability and northern vegetation activity. Nat Commun. 2014; 5: 5018. https://doi.org/10.1038/ncomms6018 PMID: 25318638 66. Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science. 2003; 300: 1560–3. https://doi. org/10.1126/science.1082750 PMID: 12791990 67. Wei H, Zhao X, Liang S, Zhou T, Wu D, Tang B. Effects of Warming Hiatuses on Vegetation Growth in the Northern Hemisphere. Remote Sens. 2018; 10: 683. https://doi.org/10.3390/rs10050683 68. Piao S, Sitch S, Ciais P, Friedlingstein P, Peylin P, Wang X, et al. Evaluation of terrestrial carbon cycle

models for their response to climate variability and to CO 2 trends. Glob Chang Biol. 2013; 19: 2117– 2132. https://doi.org/10.1111/gcb.12187 PMID: 23504870 69. Liu Z, He C, Zhang Q, Huang Q, Yang Y. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landsc Urban Plan. 2012; 106: 62–72. https://doi. org/10.1016/j.landurbplan.2012.02.013 70. Qian Y, Wang W, Leung LR, Kaiser DP. Variability of solar radiation under cloud-free skies in China: The role of aerosols. Geophys Res Lett. 2007; 34: L12804. https://doi.org/10.1029/2006GL028800 71. Shi G-Y, Hayasaka T, Ohmura A, Chen Z-H, Wang B, Zhao J-Q, et al. Data Quality Assessment and the Long-Term Trend of Ground Solar Radiation in China. J Appl Meteorol Climatol. 2008; 47: 1006– 1016. https://doi.org/10.1175/2007JAMC1493.1 72. Lolli S, Khor WY, Matjafri MZ, Lim HS. Monsoon Season Quantitative Assessment of Biomass Burning Clear-Sky Aerosol Radiative Effect at Surface by Ground-Based Lidar Observations in Pulau Pinang, Malaysia in 2014. Remote Sens. 2019; 11: 2660. https://doi.org/10.3390/rs11222660 73. Reid JS, Lagrosas ND, Jonsson HH, Reid EA, Atwood SA, Boyd TJ, et al. Aerosol meteorology of Mari- time Continent for the 2012 7SEAS southwest monsoon intensive study–Part 2: Philippine receptor observations of fine-scale aerosol behavior. Atmos Chem Phys. 2016; 16: 14057–14078. https://doi. org/10.5194/acp-16-14057-2016 74. Zheng N, Liu J, Wang Q, Liang Z. Health risk assessment of heavy metal exposure to street dust in the

zinc smelting district, Northeast of China. Sci Total Environ. 2010; 408: 726–733. https://doi.org/10. 1016/j.scitotenv.2009.10.075 PMID: 19926116 75. Aunan K, Berntsen TK, Seip HM. Surface Ozone in China and its Possible Impact on Agricultural Crop Yields. AMBIO A J Hum Environ. 2000; 29: 294–301. https://doi.org/10.1579/0044-7447-29.6.294 76. Kuang YW, Sun FF, Wen DZ, Zhou GY, Zhao P. Tree-ring growth patterns of Masson pine (Pinus mas- soniana L.) during the recent decades in the acidification Pearl River Delta of China. For Ecol Manage. 2008; 255: 3534–3540. https://doi.org/10.1016/j.foreco.2008.02.036 77. He C, Yang L. Urban Development and Climate Change in China’s Pearl River Delta. L Lines. 2011; 2– 7. 78. Garonna I, de Jong R, de Wit AJW, Mu¨ cher CA, Schmid B, Schaepman ME. Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011). Glob Chang Biol. 2014; 20: 3457–3470. https://doi.org/10.1111/gcb.12625 PMID: 24797086 PLOS ONE Climate and vegetation trends in the Pearl River Delta PLOS ONE | https://doi.org/10.1371/journal.pone.0245467 February 24, 2021 20 / 20