References:
[1] Priyadarshan, P. M. Biology of Hevea rubber [M]. Springer, 2011.
     [2] Xiao, C. W., Feng, Z. M., Li, P. Spatiotemporal changes of global rubber production during 1961-2013[J]. Progress in Geography, 2016, 35(10): 1228-1236
     [3] Chen, J. W., Cao, K. F. A possible link between hydraulic properties and leaf habits in Hevea brasiliensis [J]. Functional Plant Biology, 2015, 42(8): 718-26.
     [4] Chen, Y. L., Wang, S. S., Zhou, R. W., et al. What leads to rubber leaf senescence in the northern edge of the Asian tropics? [J]. Industrial Crops and Products, 2022, 178: 114617.
     [5] Chen, B. Q., Xiao, X. M., Wu, Z. X., et al. Identifying establishment year and pre-conversion land cover of rubber plantations on Hainan Island, China using landsat data during 1987–2015 [J]. Remote Sensing, 2018, 10(8): 1240.
     [6] Dong, J. W., Xiao, X. M., Chen, B. Q., et al. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery [J]. Remote Sensing of Environment, 2013, 134: 392-402.
     [7] Kou, W. L., Dong, J. W., Xiao, X. M., et al. Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000–2010 [J]. GIScience & Remote Sensing, 2018, 55(6): 905-25.
     [8] Xiao, C. W., Li, P., Feng, Z. M. Monitoring annual dynamics of mature rubber plantations in Xishuangbanna during 1987-2018 using Landsat time series data: A multiple normalization approach [J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 77: 30-41.
     [9] Cui, L.L., Shi, J., Du, H.Q. Advances in Remote Sensing Extraction of Vegetation Phenology and Its Driving Factors[J]. Advances in Earth Science, 2021, 36(01): 9-16.
     [10] Verhegghen, A., Bontemps, S., Defourny, P. A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations [J]. International Journal of Remote Sensing, 2014, 35(7): 2440-71.
     [11] Li, Q., Shen, M. G., Chen, X. H., et al. Optimal color composition method for generating high-quality daily photographic time series from PhenoCam [J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2021, 14: 6179-93.
     [12] Richardson, A. D., Hufkens, K., Li, X. L., et al. Testing Hopkins’ bioclimatic law with PhenoCam data [J]. Applications in Plant Sciences, 2019, 7(3): e01228.
     [13] White, M. A., Thornton, P. E., Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability [J]. Global biogeochemical cycles, 1997, 11(2): 217-34.
     [14] Gao, F., Zhang, X. Y. Mapping crop phenology in near real-time using satellite remote sensing: Challenges and opportunities [J]. Journal of Remote Sensing, 2021, 2021.
     [15] Chen, X. Q., Wang, L. H. Progress in remote sensing phenological research [J]. Progress in Geography, 2009, (1): 33-40.
     [16] Fan, H., Fu, X. H., Zhang, Z., et al. Phenology-based vegetation index differencing for mapping of rubber plantations using Landsat OLI data [J]. Remote Sensing, 2015, 7(5): 6041-58.
     [17] Jonsson, P., Eklundh, L. TIMESAT—a program for analyzing time-series of satellite sensor data [J]. Computers & geosciences, 2004, 30(8): 833-45.
     [18] Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation [J]. Remote sensing of Environment, 1979, 8(2): 127-50.
     [19] Jonsson, P., Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data [J]. IEEE transactions on Geoscience and Remote Sensing, 2002, 40(8): 1824-32.
     [20] Azizan, F. A., Astuti, I. S., Aditya, M. I., et al. Using multi-temporal satellite data to analyze phenological responses of Rubber (Hevea brasiliensis) to climatic variations in South Sumatra, Indonesia [J]. Remote Sensing, 2021, 13(15): 2932.