Unraveling the Spatiotemporal Dynamics of Rubber Phenology in Hainan Island, China: A Multi-Sensor Remote Sensing and Climate Drivers Analysis
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Processing
2.2.1. Rubber Plantation Map and Field Observations Data
2.2.2. Satellite Imagery
2.2.3. Climate Data
2.2.4. DEM Data
2.3. Methodology
2.3.1. Method Overview
2.3.2. Establishment of NDVI Time Series
2.3.3. Defining SOS, EOS, and LOS in Rubber Plantations
2.3.4. Statistical Analysis
3. Results
3.1. Evaluation of the Accuracy and Characterization
3.2. Spatiotemporal Characteristics of Rubber Phenology
3.3. Spatial Distribution of Rubber Phenology Variation Trend
3.4. Driving Factors and the Correlation Between Rubber Phenology
3.4.1. Topographic Influences
3.4.2. Annual Temperature and Precipitation
3.4.3. Sensitivity to Preseason Temperature and Precipitation
4. Discussion
4.1. Enhanced Monitoring of Rubber Phenology Using Remote Sensing Data
4.2. Rubber Phenology Patterns and Influencing Factors
4.3. The Impact of Preseasonal Climatic Conditions on the Rubber Phenology
4.4. Study Limitations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Richardson, A.D.; Keenan, T.F.; Migliavacca, M.; Ryu, Y.; Sonnentag, O.; Toomey, M. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 2013, 169, 156–173. [Google Scholar] [CrossRef]
- Wolkovich, E.M.; Cook, B.I.; Allen, J.M.; Crimmins, T.M.; Betancourt, J.L.; Travers, S.E.; Pau, S.; Regetz, J.; Davies, T.J.; Kraft, N.J.B.; et al. Warming experiments underpredict plant phenological responses to climate change. Nature 2012, 485, 494–497. [Google Scholar] [CrossRef]
- Piao, S.; Liu, Q.; Chen, A.; Janssens, I.A.; Fu, Y.; Dai, J.; Liu, L.; Lian, X.; Shen, M.; Zhu, X. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 2019, 25, 1922–1940. [Google Scholar] [CrossRef]
- Priyadarshan, P.M. Biology of Hevea Rubber; Springer International Publishing: Cham, Switzerland, 2017. [Google Scholar]
- Abernethy, K.; Bush, E.R.; Forget, P.; Mendoza, I.; Morellato, L.P.C. Current issues in tropical phenology: A synthesis. Biotropica 2018, 50, 477–482. [Google Scholar] [CrossRef]
- Davidson, K.J.; Lamour, J.; Rogers, A.; Ely, K.S.; Li, Q.; McDowell, N.G.; Pivovaroff, A.L.; Wolfe, B.T.; Wright, S.J.; Zambrano, A.; et al. Short-term variation in leaf-level water use efficiency in a tropical forest. New Phytol. 2023, 237, 2069–2087. [Google Scholar] [CrossRef]
- Adams, B.T.; Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, M.P.; Zhao, K. Spring phenological variability promoted by topography and vegetation assembly processes in a temperate forest landscape. Agric. For. Meteorol. 2021, 308–309, 108578. [Google Scholar] [CrossRef]
- An, S.; Chen, X.; Li, F.; Wang, X.; Shen, M.; Luo, X.; Ren, S.; Zhao, H.; Li, Y.; Xu, L. Long-term species-level observations indicate the critical role of soil moisture in regulating China’s grassland productivity relative to phenological and climatic factors. Sci. Total Environ. 2024, 929, 172553. [Google Scholar] [CrossRef] [PubMed]
- Fu, Y.H.; Zhao, H.; Piao, S.; Peaucelle, M.; Peng, S.; Zhou, G.; Ciais, P.; Huang, M.; Menzel, A.; Peñuelas, J.; et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 2015, 526, 104–107. [Google Scholar] [CrossRef]
- Nezval, O.; Krejza, J.; Světlík, J.; Šigut, L.; Horáček, P. Comparison of traditional ground-based observations and digital remote sensing of phenological transitions in a floodplain forest. Agric. For. Meteorol. 2020, 291, 108079. [Google Scholar] [CrossRef]
- Berra, E.F.; Gaulton, R. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics. For. Ecol. Manag. 2021, 480, 118663. [Google Scholar] [CrossRef]
- Broich, M.; Huete, A.; Tulbure, M.G.; Ma, X.; Xin, Q.; Paget, M.; Restrepo-Coupe, N.; Davies, K.; Devadas, R.; Held, A. Land surface phenological response to decadal climate variability across Australia using satellite remote sensing. Biogeosciences 2014, 11, 5181–5198. [Google Scholar] [CrossRef]
- Ruan, Y.; Zhang, X.; Xin, Q.; Sun, Y.; Ao, Z.; Jiang, X. A method for quality management of vegetation phenophases derived from satellite remote sensing data. Int. J. Remote Sens. 2021, 42, 5811–5830. [Google Scholar] [CrossRef]
- Ulsig, L.; Nichol, C.J.; Huemmrich, K.F.; Landis, D.R.; Middleton, E.M.; Lyapustin, A.I.; Mammarella, I.; Levula, J.; Porcar-Castell, A. Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series. Remote Sens. 2017, 9, 49. [Google Scholar] [CrossRef]
- Chen, H.; Chen, X.; Chen, Z.; Zhu, N.; Tao, Z. A Primary study on rubber acreage estimation from MODIS-based information in Hainan. Chin. J. Trop. Crops 2010, 31, 1181–1185. (In Chinese) [Google Scholar]
- Dong, J.; Xiao, X.; Chen, B.; Torbick, N.; Jin, C.; Zhang, G.; Biradar, C. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sens. Environ. 2013, 134, 392–402. [Google Scholar] [CrossRef]
- Fan, H.; Fu, X.; Zhang, Z.; Wu, Q. Phenology-based vegetation index differencing for mapping of rubber plantations using Landsat OLI data. Remote Sens. 2015, 7, 6041–6058. [Google Scholar] [CrossRef]
- Lai, H.; Chen, B.; Yin, X.; Wang, G.; Wang, X.; Yun, T.; Lan, G.; Wu, Z.; Yang, C.; Kou, W. Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province. Front. Plant Sci. 2023, 14, 1283315. [Google Scholar] [CrossRef]
- Zhang, X.; Xiao, X.; Qiu, S.; Xu, X.; Wang, X.; Chang, Q.; Wu, J.; Li, B. Quantifying latitudinal variation in land surface phenology of spartina alterniflora saltmarshes across coastal wetlands in China by Landsat 7/8 and Sentinel-2 images. Remote Sens. Environ. 2022, 269, 112810. [Google Scholar] [CrossRef]
- Li, N.; Xiao, J.; Bai, R.; Wang, J.; Wu, L.; Gao, W.; Li, W.; Chen, M.; Li, Q. Preseason sunshine duration determines the start of growing season of natural rubber forests. Int. J. Appl. Earth Obs. 2023, 124, 103513–103522. [Google Scholar] [CrossRef]
- Lu, X.; Liu, Z.; Zhou, Y.; Liu, Y.; An, S.; Tang, J. Comparison of phenology estimated from reflectance-based indices and Solar-Induced Chlorophyll Fluorescence (SIF) observations in a temperate forest using GPP-based phenology as the standard. Remote Sens. 2018, 10, 932. [Google Scholar] [CrossRef]
- Williams, L.J.; Bunyavejchewin, S.; Baker, P.J. Deciduousness in a seasonal tropical forest in western Thailand: Interannual and intraspecific variation in timing, duration and environmental cues. Oecologia 2008, 155, 571–582. [Google Scholar] [CrossRef]
- Qiu, J. Where the rubber meets the garden: China’s leading conservation centre is facing down an onslaught of rubber plantations. Nature 2009, 7227, 246–247. [Google Scholar] [CrossRef] [PubMed]
- Li, Z.; Fox, J.M. Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data. Appl. Geogr. 2012, 32, 420–432. [Google Scholar] [CrossRef]
- Azizan, F.A.; Astuti, I.S.; Young, A.; Abdul Aziz, A. Rubber leaf fall phenomenon linked to increased temperature. Agric. Ecosyst. Environ. 2023, 352, 108531. [Google Scholar] [CrossRef]
- Yeang, H. Synchronous flowering of the rubber tree (Hevea brasiliensis) induced by high solar radiation intensity. New Phytol. 2007, 175, 283–289. [Google Scholar] [CrossRef]
- Lai, H.; Chen, B.; Yun, T.; Yin, X.; Chen, Y.; Wu, Z.; Kou, W. Research progress on phenology of Hevea brasiliensis under climate change. J. Trop. Subtrop. Bot. 2023, 31, 886–896. [Google Scholar]
- Yin, S.; Chuan, X.; Wang, C.; Zhang, X.; Li, J.; Li, H. Occurrence of powdery mildew on Hevea brasiliensis in Southwest Yunnan. Chin. J. Trop. Agric. 2022, 42, 57–61. (In Chinese) [Google Scholar]
- Azizan, F.A.; Young, A.; Aziz, A.A. Determining the optimum climate preseason for plant phenology analysis using rubber (Hevea brasiliensis) as a model. Remote Sens. Lett. 2022, 13, 1121–1130. [Google Scholar] [CrossRef]
- Zhai, D.; Thaler, P.; Worthy, F.R.; Xu, J. Rubber latex yield is affected by interactions between antecedent temperature, rubber phenology, and powdery mildew disease. Int. J. Biometeorol. 2023, 67, 1569–1579. [Google Scholar] [CrossRef]
- Gutiérrez-Vanegas, A.J.; Correa-Pinilla, D.E.; Gil-Restrepo, J.P.; López-Hernández, F.G.; Guerra-Hincapié, J.J.; Córdoba-Gaona, O.D.J. Foliar and flowering phenology of three rubber (Hevea brasiliensis) clones in the eastern plains of Colombia. Braz. J. Bot. 2020, 43, 813–821. [Google Scholar] [CrossRef]
- Guerra-Hincapié, J.J.; de Jesús Córdoba-Gaona, O.; Gil-Restrepo, J.P.; Monsalve-García, D.A.; Hernández-Arredondo, J.D.; Martínez-Bustamante, E.G. Phenological patterns of defoliation and refoliation processes of rubber tree clones in the Colombian Northwest. Rev. Fac. Nac. Agron. Medellín 2020, 73, 9293–9303. [Google Scholar] [CrossRef]
- Chen, Y.; Khongdee, N.; Wang, Y.; Song, Q.; Lu, D.; Wang, S.; Chen, Y. Spatiotemporal trends of rubber defoliation and refoliation and their responses to abiotic factors in the northern edge of the Asian tropics. Ind. Crops Prod. 2025, 226, 120753. [Google Scholar] [CrossRef]
- Xin, Q.; Broich, M.; Zhu, P.; Gong, P. Modeling grassland spring onset across the western United States using climate variables and MODIS-derived phenology metrics. Remote Sens. Environ. 2015, 161, 63–77. [Google Scholar] [CrossRef]
- Hu, Y.; Dai, S.; Luo, H.; Li, H.; Li, M.; Zheng, Q.; Yu, X.; Li, N. Spatial-temporal variation characteristics of rubber forest phenology in Hainan Island, 2001–2015. Remote Sens. Nat. Resour. 2022, 1, 210–217. (In Chinese) [Google Scholar]
- Li, N.; Bai, R.; Wu, L.; Li, W.; Chen, M.; Chen, X.; Fan, C.-H.; Yang, G.-S. Impacts of future climate change on spring phenology stages of rubber tree in Hainan, China. Chin. J. Appl. Ecol. 2020, 31, 1241–1249. (In Chinese) [Google Scholar]
- Chen, X.; Chen, H.; Li, W.; Liu, S. Remote sensing monitoring of spring phenophase of natural rubber forest in Hainan Province. Chin. J. Agrometeorol. 2016, 37, 111–116. (In Chinese) [Google Scholar]
- Golbon, R.; Cotter, M.; Suerborn, J. Climate change impact assessment on the potential rubber cultivating area in the Greater Mekong Subregion. Environ. Res. Lett. 2018, 13, 84002. [Google Scholar] [CrossRef]
- Wang, Y.; Hollingsworth, P.M.; Zhai, D.; West, C.D.; Green, J.M.H.; Chen, H.; Hurni, K.; Su, Y.; Warren-Thomas, E.; Xu, J.; et al. High-resolution maps show that rubber causes substantial deforestation. Nature 2023, 623, 340–346. [Google Scholar] [CrossRef]
- Xu, G.; Luo, S.; Guo, Q.; Pei, S.; Shi, Z.; Zhu, L.; Zhu, N. Responses of leaf unfolding and flowering to climate change in 12 tropical evergreen broadleaf tree species in Jianfengling, Hainan Island. Chin. J. Plant Ecol. 2014, 38, 585–598. (In Chinese) [Google Scholar]
- Chen, B.; Li, X.; Xiao, X.; Zhao, B.; Dong, J.; Kou, W.; Qin, Y.; Yang, C.; Wu, Z.; Sun, R.; et al. Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images. Int. J. Appl. Earth Obs. 2016, 50, 117–130. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 2, 127–150. [Google Scholar] [CrossRef]
- Shen, Z.; Yong, B.; Gourley, J.J.; Qi, W.; Lu, D.; Liu, J.; Ren, L.; Hong, Y.; Zhang, J. Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). J. Hydrol. 2020, 591, 125284. [Google Scholar] [CrossRef]
- Metz, M.; Andreo, V.; Neteler, M. A new fully gap-free time series of Land Surface Temperature from MODIS LST Data. Remote Sens. 2017, 9, 1333. [Google Scholar] [CrossRef]
- Peterson, P.; Funk, C.C.; Husak, G.J.; Pedreros, D.H.; Landsfeld, M.; Verdin, J.P.; Shukla, S. The Climate Hazards group InfraRed Precipitation (CHIRP) with Stations (CHIRPS): Development and Validation. In Proceedings of the AGU 2013 Fall Meeting, San Francisco, CA, USA, 9–13 December 2013. [Google Scholar]
- Zhai, D.; Yu, H.; Chen, S.; Ranjitkar, S.; Xu, J. Responses of rubber leaf phenology to climatic variations in Southwest China. Int. J. Biometeorol. 2019, 63, 607–616. [Google Scholar] [CrossRef] [PubMed]
- Caglar, B.; Becek, K.; Mekik, C.; Ozendi, M. On the vertical accuracy of the ALOS world 3D-30m digital elevation model. Remote Sens. Lett. 2018, 9, 607–615. [Google Scholar] [CrossRef]
- Li, G.; Kou, W.; Chen, B.; Wu, Z.; Zhang, X.; Yun, T.; Ma, J.; Sun, R.; Li, Y. Spatio-temporal changes of rubber plantations in Hainan Island over the past 30 years. J. Nanjing For. Univ. 2023, 47, 189. (In Chinese) [Google Scholar]
- Jönsson, P.; Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote 2002, 40, 1824–1832. [Google Scholar] [CrossRef]
- Wu, C.; Peng, D.; Soudani, K.; Siebicke, L.; Gough, C.M.; Arain, M.A.; Bohrer, G.; Lafleur, P.M.; Peichl, M.; Gonsamo, A.; et al. Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites. Agric. For. Meteorol. 2017, 233, 171–182. [Google Scholar] [CrossRef]
- Kendall, M.G. Rank Correlation Methods; Charles Griffin & Co. Ltd.: London, UK, 1975. [Google Scholar]
- Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- Theil, H. A rank invariant method of linear and polynomial regression analysis. Indag. Math. 1950, 53, 386–392. [Google Scholar]
- Azizan, F.; Astuti, I.; Aditya, M.; Febbiyanti, T.; Williams, A.; Young, A.; Aziz, A.A. Using multi-temporal satellite data to analyse phenological responses of rubber (Hevea brasiliensis) to climatic variations in South Sumatra, Indonesia. Remote Sens. 2021, 13, 2932. [Google Scholar] [CrossRef]
- Waskom, M.L. Seaborn: Statistical data visualization. J. Open Source Softw. 2021, 6, 3021. [Google Scholar] [CrossRef]
- Olaniyi, O.N.; Szulczyk, K.R. Estimating the economic impact of the white root rot disease on the Malaysian rubber plantations. For. Policy Econ. 2022, 138, 102707. [Google Scholar] [CrossRef]
- Chaves, M.E.D.; Picoli, M.C.A.; Sanches, I.D. Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover mapping: A systematic review. Remote Sens. 2020, 12, 3062. [Google Scholar] [CrossRef]
- Chen, G.; Liu, Z.; Wen, Q.; Tan, R.; Wang, Y.; Zhao, J.; Feng, J. Identification of rubber plantations in Southwestern China based on multi-source remote sensing data and phenology windows. Remote Sens. 2023, 15, 1228. [Google Scholar] [CrossRef]
- Laskin, D.N.; McDermid, G.J.; Nielsen, S.E.; Marshall, S.J.; Roberts, D.R.; Montaghi, A. Advances in phenology are conserved across scale in present and future climates. Nat. Clim. Change 2019, 9, 419–425. [Google Scholar] [CrossRef]
- An, L.; Wu, J.; Zhang, Z.; Zhang, R. Failure analysis of a lattice transmission tower collapse due to the super typhoon Rammasun in July 2014 in Hainan Province, China. J. Wind. Eng. Ind. Aerod. 2018, 182, 295–307. [Google Scholar] [CrossRef]
- Augspurger, C.K.; Cheeseman, J.M.; Salk, C.F. Light gains and physiological capacity of understorey woody plants during phenological avoidance of canopy shade. Funct. Ecol. 2005, 19, 537–546. [Google Scholar] [CrossRef]
- Chen, J.; Jönsson, P.; Tamura, M.; Gu, Z.; Matsushita, B.; Eklundh, L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 2004, 91, 332–344. [Google Scholar] [CrossRef]
- Atzberger, C.; Eilers, P.H.C. Time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digit. Earth 2011, 4, 365–386. [Google Scholar] [CrossRef]
- Jönsson, P.; Eklundh, L. TIMESAT—A program for analyzing time-series of satellite sensor data. Comput. Geosci. 2004, 30, 833–845. [Google Scholar] [CrossRef]
- Wang, J.; Xiao, X.; Qin, Y.; Dong, J.; Zhang, G.; Kou, W.; Jin, C.; Zhou, Y.; Zhang, Y. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Sci. Rep. 2015, 5, 10088. [Google Scholar] [CrossRef] [PubMed]
- Liyanage, K.K.; Khan, S.; Ranjitkar, S.; Yu, H.; Xu, J.; Brooks, S.; Beckschäfer, P.; Hyde, K.D. Evaluation of key meteorological determinants of wintering and flowering patterns of five rubber clones in Xishuangbanna, Yunnan, China. Int. J. Biometeorol. 2019, 63, 617–625. [Google Scholar] [CrossRef]
- Rafferty, N.E.; Diez, J.M.; Bertelsen, C.D. Changing climate drives divergent and nonlinear shifts in flowering phenology across elevations. Curr. Biol. 2020, 30, 432–441. [Google Scholar] [CrossRef] [PubMed]
- Boehm, A.R.; Hardegree, S.P.; Glenn, N.F.; Reeves, P.A.; Moffet, C.A.; Flerchinger, G.N. Slope and Aspect Effects on Seedbed Microclimate and Germination Timing of Fall-Planted Seeds. Rangel. Ecol. Manag. 2021, 75, 58–67. [Google Scholar] [CrossRef]
- Yang, J.; Xu, J.; Zhai, D. Integrating phenological and geographical information with artificial intelligence algorithm to map rubber plantations in Xishuangbanna. Remote Sens. 2021, 13, 2793. [Google Scholar] [CrossRef]
- Razak, J.; Shariff, A.M.; Ahmad, N.B.; Ibrahim Sameen, M. Mapping rubber trees based on phenological analysis of Landsat time series data-sets. Geocarto Int. 2018, 33, 627–650. [Google Scholar] [CrossRef]
- Lin, Y.; Zhang, Y.; Zhou, L.; Li, J.; Zhou, R.; Guan, H.; Zhang, J.; Sha, L.; Song, Q. Phenology-related water-use efficiency and its responses to site heterogeneity in rubber plantations in Southwest China. Eur. J. Agron. 2022, 137, 126519. [Google Scholar] [CrossRef]
- Liyanage, A.D.S. Influence of some factors on the pattern of wintering and on the incidence of oidium leaf fall in clone PB 86. Rubber Res. Inst. Sri Lanka 1976, 53, 31–38. [Google Scholar]
- Li, Y.; Lan, G.; Xia, Y. Rubber trees demonstrate a clear retranslocation under seasonal drought and cold stresses. Front. Plant Sci. 2016, 7, 1907–1918. [Google Scholar] [CrossRef]
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Lai, H.; Chen, B.; Wang, G.; Yin, X.; Wang, X.; Yun, T.; Lan, G.; Wu, Z.; Jia, K.; Kou, W. Unraveling the Spatiotemporal Dynamics of Rubber Phenology in Hainan Island, China: A Multi-Sensor Remote Sensing and Climate Drivers Analysis. Remote Sens. 2025, 17, 2403. https://doi.org/10.3390/rs17142403
Lai H, Chen B, Wang G, Yin X, Wang X, Yun T, Lan G, Wu Z, Jia K, Kou W. Unraveling the Spatiotemporal Dynamics of Rubber Phenology in Hainan Island, China: A Multi-Sensor Remote Sensing and Climate Drivers Analysis. Remote Sensing. 2025; 17(14):2403. https://doi.org/10.3390/rs17142403
Chicago/Turabian StyleLai, Hongyan, Bangqian Chen, Guizhen Wang, Xiong Yin, Xincheng Wang, Ting Yun, Guoyu Lan, Zhixiang Wu, Kai Jia, and Weili Kou. 2025. "Unraveling the Spatiotemporal Dynamics of Rubber Phenology in Hainan Island, China: A Multi-Sensor Remote Sensing and Climate Drivers Analysis" Remote Sensing 17, no. 14: 2403. https://doi.org/10.3390/rs17142403
APA StyleLai, H., Chen, B., Wang, G., Yin, X., Wang, X., Yun, T., Lan, G., Wu, Z., Jia, K., & Kou, W. (2025). Unraveling the Spatiotemporal Dynamics of Rubber Phenology in Hainan Island, China: A Multi-Sensor Remote Sensing and Climate Drivers Analysis. Remote Sensing, 17(14), 2403. https://doi.org/10.3390/rs17142403