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

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